D4.2 Spectrum Sensing to Complement...

ICT-777137

5G-RANGE

5G-RANGE: Remote Area Access Network for the 5th Generation

Research and Innovation Action

H2020-EUB-2017 – EU-BRAZIL Joint Call

D4.2 Spectrum Sensing to Complement Databases

Due date of report: 31st October 2018

Actual submission date: 21st December 2018

Start date of project: 1st November 2017 Duration: 30 months

Lead contractor for this report: UOULU

Version 1 date: 1st June 2018

Confidentiality status: Public

[D4.2] [Spectrum Sensing to Complement Databases]

© 5G-RANGE Consortium 2018 of (67)

Abstract

This document is a 5G-RANGE project report about different spectrum sensing methods and their

suitability to complement database approach for television white space (TVWS) usage in remote area

scenarios. At first, spectrum sharing concepts are introduced to give high-level overview of the context.

Then a review about different spectrum sensing methods is presented. Finally, spectrum sharing model

is defined and performance evalutions of spectrum sensing techniques for 5G-RANGE is introduced.

Suitable spectrum sensing methods based on spectrum sharing model, project scenarios, core use cases

and present signals are proposed.

Disclaimer

This document contains material, which is the copyright of certain 5G-RANGE consortium parties, and

may not be reproduced or copied without permission. All 5G-RANGE consortium parties have agreed

to the full publication of this document. The commercial use of any information contained in this

document may require a license from the proprietor of that information.

Neither the 5G-RANGE consortium as a whole, nor a certain party of the 5G-RANGE consortium

warrant that the information contained in this document is capable of use, or that use of the information

is free from risk and accept no liability for loss or damage suffered by any person using this information.

This document does not represent the opinion of the European Community, and the European

Community is not responsible for any use that might be made of its content.

Impressum

Full project title: 5G-RANGE: Remote Area Access Network for the 5th Generation

Document title: D4.2 Spectrum Sensing to Complement Databases

Editor: Heikki Karvonen (UOULU)

Work Package No. and Title: WP4: Cognitive MAC layer of the 5G-RANGE

Work Package leader: Heikki Karvonen, UOULU (EU), Jorge Seki, CpQD (BR),

Project Co-ordinator: Marcelo Bagnulo, UC3M (EU), Priscila Solis, UnB (BR)

Technical Manager: Luciano Mendes, Inatel (BR)

Copyright notice

© 2018 Participants in project 5G-RANGE



Executive Summary

The objective of this deliverable is to provide information of different spectrum sensing methods and

their suitability to complement database approach for 5G in remote area scenarios. Different spectrum

sharing concepts are first presented to provide insight of the wider concept. Spectrum sharing enables

efficient usage of spectrum resources and its purpose is to allow several radio systems to efficiently use

the same frequency band. With spectrum sharing, additional users can exploit the spectrum when the

primary user is not occupying it. Database and spectrum sensing are high-level approaches that are

commonly studied to be used for obtaining information about spectrum availability for sharing.

Database approach collects and stores information about the usage of spectrum bands over some

geographical area into a database, and through defined protection criteria it assigns access rights to

additional users while fulfilling the protection criteria. Spectrum sensing aims at finding out whether

signals are present in the measurement area, which helps to define what frequency bands are available

for sharing. This report focuses on spectrum sensing techniques, and a review about different spectrum

sensing methods is presented. Finally, spectrum sharing model is proposed and performance evaluation

of spectrum sensing is presented. Suitable spectrum sensing methods based on spectrum sharing model,

core use cases of the project and present signals, are defined for 5G-RANGE.



List of Authors

Johanna Vartiainen (UOULU)

Heikki Karvonen (UOULU)

Marja Matinmikko-Blue (UOULU)

Guilherme P. Aquino (Inatel)

Luciano Leonel Mendes (Inatel)

Alexandre Matos (UFC)

Raphael Braga (UFC)

Carlos Silva (UFC)

Marcos F. Caetano (UnB)

Priscila Solis (UnB)

Wagner Silveira (USP)

Douglas L. Dantas (USP)

Sergio T. Kofuji (USP)



Table of contents

Executive Summary .............................................................................................................................. 3

List of Authors ....................................................................................................................................... 4

Table of contents .................................................................................................................................... 5

List of figures ......................................................................................................................................... 6

Definitions and abbreviations ............................................................................................................... 8

1 Introduction ................................................................................................................................. 10

2 Spectrum Sharing Orchestration ............................................................................................... 11

2.1 Spectrum sharing models ...................................................................................................... 11

2.2 White space detection ............................................................................................................ 12

3 Spectrum Sensing Techniques .................................................................................................... 15

3.1 Individual Sensing Methods .................................................................................................. 18

3.2 Hybrid Sensing Methods ....................................................................................................... 20

4 Signal Classification Model for 5G-RANGE ............................................................................ 25

5 Spectrum Sensing Performance Evaluation ............................................................................. 28

5.1 Signal detection using LAD and WIBA methods ................................................................. 28

5.1.1 Method descriptions ...................................................................................................... 28

5.1.2 Performance analysis ..................................................................................................... 29

5.2 The GRCR and Eigenvalue-based detection ......................................................................... 50

5.2.1 Method descriptions ...................................................................................................... 50

5.2.2 Performance Analysis .................................................................................................... 52

5.3 The CSS scheme with high bandwidth and energy efficiency .............................................. 54

5.3.1 Performance Analysis .................................................................................................... 57

5.4 Selected methods ................................................................................................................... 59

6 Functional Parameters ................................................................................................................ 60

7 Conclusions .................................................................................................................................. 61

8 Bibliography ................................................................................................................................ 62



List of figures

Figure 1. Spectrum sharing principle. ................................................................................................... 11

Figure 2: Spectrum sharing models. ...................................................................................................... 12

Figure 3: Example of database for TV white space operations. ............................................................ 13

Figure 4. Spectrum sensing in 5G-RANGE to complement database approach [3]. ............................ 14

Figure 5. Spectrum sensing as a part of 5G-RANGE cognitive cycle. ................................................. 14

Figure 6: Noise and signal below noise level. ....................................................................................... 15

Figure 7: Noise and signal above the noise power level. Signal can be detected using ED based threshold

setting method. ...................................................................................................................................... 16

Figure 8: Signal is considered wideband for detection window 1. For detection window 2, signal is

considered relatively narrowband.......................................................................................................... 16

Figure 9. Selection of users for cooperative sensing decision making. ................................................. 22

Figure 10: Proposed spectrum sharing model. ...................................................................................... 26

Figure 11: Example of overlapping blocks of the window-based method. ........................................... 29

Figure 12: Probability of detection vs. SNR (left) and the number of detected signals vs. SNR. The signal

bandwidth is 5% of the overall bandwidth [34]. ................................................................................... 30

Figure 13: Snapshot of a detection of one signal with 10% bandwidth. ............................................... 31

Figure 14: RMSE vs. SNR results for a signal with 10 % bandwidth. .................................................. 31

Figure 15. Pd vs. SNR results in multipath case. SNR is calculated for the LOS component. ............. 32

Figure 16: RMSE vs. SNR results in multipath case. Signal BW is 10% and M = 102. ....................... 33

Figure 17: Snapshot of two simulated signals with 5 and 10% bandwidth. M = 52, 102 and 204. ....... 34

Figure 18: Number of detected signals vs. SNR results. There are two signals with 10% and 5%

bandwidths. ........................................................................................................................................... 35

Figure 19: Pd vs. detection distance results when transmit power of the signal is 53 dBm. ................. 36





Figure 24: Probability of detection vs. SNR [dB] in AWGN channel case. The signal bandwidth is 10%

(102 samples), M = 102 and L = 20. ...................................................................................................... 40

Figure 25: Probability of detection vs. SNR [dB] in AWGN channel case. The signal bandwidth is 5%

(52 samples), M = 52 and L = 39. .......................................................................................................... 41

Figure 26: Probability of detection vs. distance [km] results. Transmit power of the signal to be detected

is 53 dBm. ............................................................................................................................................. 41

Figure 27: Probability of detection vs. distance [km] results. Transmit power of the signal to be detected

is 30 dBm. ............................................................................................................................................. 42

Figure 28: A: One 6 MHz channel with effective BW 4.5 MHz and one signal with 4.5 MHz BW. B:

Two 6 MHz channels with effective BWs 9 MHz and one signal with 4.5 MHz BW. ......................... 42

Figure 29: Pd vs. SNR results when signal BW is 75% (768 samples). ................................................ 43



Figure 30: Pd vs. SNR results when signal BW is 37.5% (384 samples). ............................................. 43

Figure 31: OR, AND and k-out-of-n rules when n=10 and k=1~10. ..................................................... 46

Figure 32: Performance of the OR, AND and k-out-of-n rules when n = 5 and k = 1 ~ 5. ................... 46

Figure 33: Illustration of the distances where single sensing and cooperative sensing using OR and 3 out

of 5 rules can detect the signal. Transmit power is 53 dBm and signal bandwidth is 2,4 and 6 MHz.

WIBA method was used in this case. .................................................................................................... 48

Figure 34: Generic spectrum sensing scenario of 5G-RANGE use cases. ............................................ 50

Figure 35. Cooperative spectrum sensing scenario for analysis of techniques based on eigenvalues. . 50

Figure 36. Gershgorin disks under H0 (a) and H1 (b) hypotheses for a sample covariance matrix R. .. 52

Figure 37. ROC performance for spectrum sensing techniques MED, ED, GLRT and MMED. ......... 53

Figure 38. Performance of MED, GLRT, MMED and GRCR techniques under non-uniform and

dynamical noise. .................................................................................................................................... 53

Figure 39. ROC performance of the efficient schemes. ........................................................................ 57

Figure 40. Lowest energy consumption per RbTt fair opportunistic transmitted bits. ........................... 58



Definitions and abbreviations

ACC Adjacent cluster combining

AMED Adaptive maximum eigenvalue detection

ATB Adaptive Transmission Bandwidth

ATSC Advanced Television Systems Committee

AWGN Additive White Gaussian Noise

BCED Blindly combined energy detection

BPSK Binary Phase Shift Keying

BS Base station

BW Bandwidth

CAV Covariance absolute value

CBRS US Citizens Broadband Radio

CFAR Constant false alarm rate

CED Combined energy detection

CFN Covariance Frobenius-norm

CMMB China Mobile Multimedia Broadcasting

COSORA Collaborative Spectrum Sensing Optimized for Remote Areas

CP Cyclic prefix

CR Cognitive radio

CSS Cooperative spectrum sensing

DARA Dynamic Access and Resource Allocation

DSS Dynamic spectrum sharing

DTV Digital TV

DTMB Digital Terrestrial Multimedia Broadcast

ED Energy detection

ERD Eigenvalue ratio detector

ESPRIT Estimation of signal parameters via rotational invariance techniques

FC Fusion Center

FCC Federal Communications Commission

FCME Forward consecutive mean excision

FFT Fast Fourier Transform

FM Frequency modulation

GAA General authorized access

GRCR Gershgorin radii and centers ratio

GLRT Generalized Likelihood Ratio Test

IU Incumbent user

LA Link adaptation

LAD Localization algorithm based on double-thresholding

LBT Listen-before-talk

LAT Linear inertia weight particle swarm optimization

LRT Likelihood Ratio Test

LRT-E Noise-Independent Likelihood Ratio Test

LSA Licensed shared access

MAC Medium access control

MF Matched filter

MED Maximum eigenvalue detection

MMED Maximum-minimum eigenvalue detection

Mo-DCAED Modified-double constraints adaptive energy detection

NB Narrowband

NTSC National Television Systems Committee



OFDM Orthogonal frequency division multiplexing

PA Priority access

PAL Phase Alternating Line

PAL-D/K Phase Alternating Line (analog, China)

PMSE Programme-making and special events

PSO Particle swarm optimization

PU Primary user

QPSK Quadrature Phase Shift Keying

PSD Power spectral density

RF Radio frequency

RLRT Roy’s Largest Root Test

RNB Relatively narrowband

RRC Root raised cosine

SAS Spectrum access system

SCHED Scheduler

SNR Signal-to-noise ratio

SAMP Joint-sparsity adaptive matching pursuit

SU Secondary user

TS Two-stage

TV Television

TVWS TV white space

UFS Unutilized frequency spectrum

UHF Ultra-High Frequency

VHF Very-High Frequency

VoIP Voice over Internet Protocol

WM Wireless microphone



1 Introduction

Frequency spectrum is a scarce resource, so it is necessary to optimize its usage. Wireless networks are

usually made available through an inflexible frequency spectrum allocation model with long-term wide-

area exclusive spectrum licenses. While this approach has been successful providing mobile broadband

in densely populated areas, it has not guaranteed that networks are deployed in remote areas where they

are needed by a smaller amount of end users. The static spectrum allocation has proven to be very

inefficient since many allocated channels are underutilized by the system that holds its license, while

they could be used by other systems which needs them. A survey showed that, in 11 European countries,

around 56% of the Television (TV) bands from 470 MHz to 790 MHz are actually not used for TV

broadcasting [1].

The use of Television White Spaces (TVWS) through cognitive radio (CR) has attracted interest as an

alternative to improve spectrum usage, with the potential to decrease energy consumption, increase

throughput and coverage, thus decreasing the number of cells in a region [2]. In 5G-RANGE, the TVWS

usage is studied as a potential cost-efficient solution for rural area customers.

This document focuses on spectrum sensing techniques for operation in the Very High Frequency (VHF)

and Ultra High Frequency (UHF) bands, in remote area scenarios, to complement the conventional

TVWS database approach. In the 5G-RANGE architecture [3], the spectrum sensing is organized by

Collaborative Spectrum Sensing Optimized for Remote Areas (COSORA) block which operates at the

MAC layer as a part of cognitive cycle [4]. Spectrum sensing is used to find spectrum holes, to protect

the primary (incumbent) spectrum users and to enhance the coexistence among secondary networks.

This research expands the existing state of the art techniques to address the specific needs of operation

in lower frequency bands in remote areas and integrates them with the database approach. The

implementation of distributed and collaborative sensing techniques shall be used to furnish the BS (base

station) with a detailed view of the status of the channels in its coverage area. The distributed sensing is

expected to decrease the overhead of the BS while providing a panorama of the communication

channels. We will build distributed collaborative sensing techniques that will consider the energy

restrictions of the mobile devices.

This report is divided into following sections. Section 2 introduces the high-level context by discussing

spectrum sharing models and white space detection principles. Section 3 deals with the review of

spectrum sensing techniques. Section 4 defines the spectrum sharing model for 5G-RANGE to be used

with spectrum sensing and database approach. Section 5 provides results about the spectrum sensing

techniques performance and definition of suitable techniques for 5G-RANGE. Section 6 introduces

functional parameters for COSORA block. Finally, conclusions are given in Section 7.



2 Spectrum Sharing Orchestration

Here will be introduced the high-level context for the spectrum sensing and its importance for 5G-

RANGE. One of the goals of the project is to utilize unused spectrum holes (TVWS) in the UHF and

VHF bands that requires spectrum sharing between primary users (PU) and secondary users (SU).

Frequency spectrum sharing enables efficient usage of spectrum resource and its purpose is to allow

additional users to use the spectrum when the PU (incumbent user (IU)) is not using it, as illustrated in

Figure 1. Dynamic spectrum sharing (DSS) is the term that defines that the spectrum is shared in a

flexible and efficient manner. DSS is a solution to mitigate against the lack of spectrum resources. At

first, there is a need for spectrum sharing models to define the rules for different type of users operating

at the same frequency band. Different models have been proposed and they will be shortly introduced

in Section 2.1. Secondly, it must be enabled that the unutilized frequency holes can be found by the SUs

and that SUs does not interfere with PUs. There are two approached for that purpose, which are database

usage and spectrum sensing. These solutions will be introduced in Section 2.2.

Figure 1. Spectrum sharing principle.

2.1 Spectrum sharing models

There exist several spectrum sharing models for PUs (IU) and SUs with different levels of access

rights. A summary of the main spectrum sharing concepts is presented in Figure 2, where the three

different models with the different levels of access rights are illustrated. Next, these spectrum sharing

models are shortly introduced.

US Citizens Broadband Radio Service (CBRS) is a dynamic spectrum sharing model defined by Federal

Communications Commission (FCC) [5]. CBRS access model consists of three tiers with different levels

of spectrum access rights (see Figure 2): Tier 1 includes IUs, Tier 2 includes priority access (PA) users,

and Tier 3 includes general authorized access (GAA). Tier 1 users are incumbent federal users and fixed

satellite service operators, and those have complete interference protection from lower tiers (Tier 2 and

3). Tier 2 users have short time licenses (originally planned for 1-3 years) and users include both critical

(like governmental, hospitals, utilities) and non-critical (like mobile network operators) users. Tier 2

users operate in a specific geographical area. Tier 2 users have to provide interference protection for

Tier 1 users, and they may suffer some interference from Tier 1 users, but they have protection of

interference from Tier 3 users. Tier 3 users include residential users, business parties and so on, and they

do not have any license. Tier 3 users have no interference protection from Tiers 1 and 2, and they may

suffer interference from those tiers. Spectrum access system (SAS) is a mechanisms that makes three



Figure 2: Spectrum sharing models.

tiers spectrum sharing possible. SAS coordinates and assings spectrum access rights to the users. In

CBRS, databases are also used to keep track and share information of spectrum usage.

European Licensed shared access (LSA) is a spectrum sharing model where spectrum sharing is allowed

with some predetermined rules and conditions that guarantee operation for both PUs and SUs [6]. The

idea is to allow additional licensed users to access the band while protecting the IUs. Spectrum sharing

is originally planned to be voluntary for IU, and meant for cases when IU and SU are different types of

wireless services, such as broadcast and mobile communications. Sharing can be done in frequency (IU

uses only part of its frequency band), time (IU does not need the band all the time), and geographical

dimensions (IU uses its frequency band only in a small limited geographic area). Geolocation databases

are used for spectrum usage decisions. This model has been trialled with a live LTE network in [6].

CBRS and LSA models have been evaluated in [7]. As LSA is simple, CBRS is more complex but

flexible and most likely provides the most efficient spectrum utilization. In [8], also some other spectrum

sharing approaches are considered. Therein it was noticed that regulatory approaches and IUs differ

between countries, so national implementations are required.

The goal in TVWS spectrum sharing [9] is to utilize unused TV broadcast spectrum in space and time

without causing harmful interference to IUs. SUs are the additional users admitted to used the band

either with licensed or licence-exempt usage, which provide permission to use free spectrum but SUs

are not allowed to cause any interference to IU. Typically, geolocation databases are used to ensure

interference-free operation for IUs. Database includes, for example, information about TV network

infrastructure and channel occupancy, and instruct the PUs about which channels they can use.

2.2 White space detection

Cognitive radio is a technology that enables network devices to use frequency spectrum in a dynamic

manner by acquiring information from their radio environment, and adapting transceiver parameters

based on the interaction with the environment [10], [11], [12]. In 5G-RANGE system, cognitive radios

will look for free spectrum holes from TV bands and then change radio parameters so that the

transmission will take place using the free spectrum resources without interfering the PU. Cognitive

radios can get information about free spectrum opportunities from geolocation database, by using

spectrum sensing or combination of both approaches. Several standards that employ cognitive radio

approaches rely purely on geolocation database to inform the base station about the spectrum

opportunities in a given region.



Figure 3: Example of database for TV white space operations.

Database contains up-to-date information about spectrum bands usage and regulations. It collects,

updates, restores and provides data to spectrum users [13]. Geolocation database contains information

from a certain geographical area. In general, geolocation database operates as follows. Database stores

information about the different spectrum users, most importantly the IUs – like operation frequency,

transmit power, TV station location, transmission schedule, transmitted signal characteristics, and

transmit antenna type (Figure 3). SU sends a query to the database and gets available channels at a

certain time in a given area. The database protects IUs from harmful interference with the knowledge

about the incumbents, potential entrants and the interference protection criteria defined by the regulator.

When defining database for some specific area, considered issues include, e.g., who are the users whose

information is stored, what kind of user information is stored, used propagation model, and optimal

pixel size (spatial resolution).

Spectrum sensing techniques can be used by cognitive radios to sense the channel and identify unused

frequency spectum. Based on the sensing information, cognitive radios can decide the frequency that

the SUs can use without interfering PUs. In 5G-RANGE, spectrum sensing will be used to complement

the database approach as illustrated in Figure 4 [3]. Although the position and parameters of official TV

transmitters are known, there are uncertainties about the TV coverage based on software based

predictions. Also, pirate TV transmitters in operation in remote areas are not known by the regulation

agencies and, therefore, are not present in the geo-location database normally used to inform the

cognitive radio about the vacant TV channels. Hence, the spectrum sensing plays an important role in

the 5G-RANGE network. Spectrum sensing must be used to complement database approach in order to

enable efficient and dynamic specrum allocation utilizing white spectrum.

Spectrum sensing block, COSORA, as a part of 5G-RANGE cognitive cycle is illustrated in Figure 5,

which has been introduced originally in D4.1 [4]. Spectrum sensing will be performed at the physical

layer to detect radio frequency (RF) signals when requested by the MAC layer Sensing Control. Sensing

can be performed using a centralized or collaborative approach, depending on the scenario. Information

about the spectrum sensing will be delivered to the Spectrum Decision component, which can also query

information about the incumbent user’s spectrum usage from the geolocation database. Based on the

spectrum sensing information, decision about frequency channel allocation to idle spectrum holes can

be performed. Details about the 5G-RANGE cognitive cycle will be defined in D4.3: Dynamic Spectrum

Access and Resource Allocation. There are different types of spectrum sensing techniques available,

which will be discussed and evaluated in this report.



Figure 4. Spectrum sensing in 5G-RANGE to complement database approach [3].

Figure 5. Spectrum sensing as a part of 5G-RANGE cognitive cycle.

DATABASEInformation about

registered TV signals

and PMSEs

5G-RANGE Sensing

Pirate TV signals

Regulatoryinformation

Sensor nodes,unregistered PMSEs,other (narrowband)

signals

Unlicensed use ofTVWS

Registered TV andPMSE systems

Accepted UE devices Other databaseupdates

Public accessSearching public

information

PirateTV signals



3 Spectrum Sensing Techniques

The goal in the spectrum sensing is to find out if the band is idle or occupied [14]. The sensing can be

formulated as a hypothesis testing problem. Let us assume that r(n) is the received signal at detector

input, u(n) is the noise and s(n) is the transmitted signal after wireless channel. There are two hypotheses:

𝐻0: 𝑟(𝑛) = 𝑢(𝑛)

𝐻1: 𝑟(𝑛) = 𝑠(𝑛) + 𝑢(𝑛), (1)

where H0 means that the signal is absent (channel is idle), H1 means that the signal is present (channel

is occupied), and n is a sample index. Signal is detected if H1 is true. Otherwise, when H0 is true, there

is no signal (or it is not detected).

Detected (sensed) signals can be classified into two classes in frequency domain:

Class 1: Signals are below the noise level, and/or wideband

Class 2: Signals are above the noise level and relatively narrowband (RNB).

Signals in class 1 are ‘invisible’ and can not be noticed from the spectrum as illustrated in Figure 6.

Therein, signal power is defined to be |FFT(x)|2, where x is signal sample vector in time domain. Class

1 signals include, for example, DTV and wireless microphone signals (SNR is approximately between

-17 dB and -21 dB), taking into consideration that, e.g., the IEEE 802.22 requirement for cognitive radio

systems is to detect Digital TV (DTV) signals with -116 dBm (-21 dB) and wireless microphone systems

with -107 dBm (-12 dB). However, in practice, e.g., DTV signals SNR must be above 15 dB at the

receiver in order to be properly decoded. Signals in class 2 are ‘visible’ and can be noticed from the

spectrum (Figure 7). As signals below the noise level (class 1) have to be detected using, for example,

some feature of the signal, signals in class 2 can be simply detected using some energy detection (ED)

based threshold setting method. RNB means that the signal is narrowband with respect to the studied

bandwidth. This is illustrated in Figure 8. It depends on the used energy detection method how wide the

signal can be. However, typically it can cover at most 90% of the studied bandwidth. Section 5 discusses

the impact of the detection window and signal bandwidth effect in the probability of detection.

Figure 6: Noise and signal below noise level.



Figure 7: Noise and signal above the noise power level. Signal can be detected using ED based threshold

setting method.

Figure 8: Signal is considered wideband for detection window 1. For detection window 2, signal is

considered relatively narrowband.

Spectrum sensing methods can be divided in different classes in many ways. In this report we classify

sensing method to be blind, semi blind, or classical (non-blind). The blind detection does not need any

information about the signal or noise. There are several techniques of spectrum sensing, among them:

the energy detection, the detection by matched filter, the detection by cyclostationary properties of the

primary signal and the detection by eigenvalues [15]. Matched filter detection is considered to be an

optimal method. However, the SUs must know the information about the waveform of the pulses of the

primary signal, as well as the channel gain between PUs and SUs, which makes this technique complex.

The high complexity is also an evident characteristic of the detection by cyclostationary properties,

which has attractive sensing performance, but inferior to the matched filter, at the expense of knowing

the waveform of the primary signal in order to know its cyclostationary properties.



Energy detection can be considered semi-blind or blind depending on their capability to estimate noise

level by themselves. ED techniques are able to detect Class 2 signals. In ED method, signal is detected

based on the energy sensed from the channel. ED is a simple and efficient method and it does not require

any a priori information about the signals to be detected. It is the most commonly used spectrum sensing

technique. Energy detection methods use threshold(s) to define if there is a signal present or not at the

sensed frequency channel. Noise energy level is used in a threshold setting, and therefore noise energy

level must be estimated if it is not known beforehand. Constant false alarm rate (CFAR) strategies can

be used with ED method. Therein, the threshold is defined so that the false alarm rate stays constant.

Benefits of ED method includes robustness to the variation in the sensed signal, because no a priori

information about sensed signals is required. However, energy detection has also several disadvantages.

The noise power uncertainty may degrade the detection performance, primary and secondary user

signals cannot be separated, wideband signals cannot be detected, and it does not perform well at very

low SNR values. Noise uncertainty problems can be mitigated using, for example, cooperative sensing.

Semi-blind methods like wavelet-based sensing requires information about the noise power or variance.

Wavelet-based methods are complex and high sampling rate is required. Feature detection uses

deterministic or statistical properties of the detected signals and can be used when detecting Class 1

signals (low-SNR TV and wireless microphone signals). Classical detection methods like

cyclostationary feature detection need some information about the signal and / or noise power. It uses

the periodicity of the sensed signal, like cyclic prefix, spreading code, hopping sequence, or sinusoidal

carrier. Cyclostationary feature detectors observe the mean and autocorrelation of the signal. The

method can be used when detecting, for example, orthogonal frequency division multiplexing (OFDM)

signals. Cyclostationary feature detection is robust to noise uncertainties because noise is typically not

cyclostationary, and it detects signals in low SNR areas. However, a priori information about the sensed

signal is required, high sampling rate and large number of samples are needed, sensing time is long, and

computational complexity is high. Because of that, energy detection is more common in cooperative

sensing. In addition, cyclostationary detectors are sensitive to synchronization errors. Covariance based

methods perform well when signal is highly correlated (as wireless microphone signal). Recently, the

interest in spectrum sensing techniques based on eigenvalues has increased considerably in the scientific

context. Basically, in these techniques, the test statistics are generated from the eigenvalues of the

covariance matrix of the received signal. The main advantages of the spectrum sensing techniques based

on eigenvalues are the high reliability of detection and the lack of knowledge of the characteristics of

the sensed signal. In some techniques of sensing by eigenvalue also it is not necessary to know the noise

power in the receiver, in which they are considered full-blind techniques. The main test statistics based

on eigenvalue sensing are [16]:

i) generalized likelihood ratio test (GLRT); ii) ratio of maximum-minimum eigenvalue detection (MMED), also known as

eigenvalue ratio detection (ERD) and;

iii) maximum eigenvalue detection (MED), also known as Roy's largest root test (RLRT).

The GLRT and MMED detectors are considered full-blind, whereas the MED is considered semi-blind

due to the fact, that it needs to know the noise power, like some of the energy detectors which cannot

estimate noise level by themselves.

Usually, a threshold is needed for making decision about is the channel idle or not. For example,

maximum-minimum eigenvalue detection uses minimum eigenvalue of the sample covariance matrix.

Threshold setting is a demanding task and there is a trade-off between probability of detection (Pd) and

probability of false alarm (Pfa). Too low threshold means that probability of false alarm is too high, as

too high threshold means that the probability of detection is too low. Adaptive threshold gives better

performance than fixed one because it considers changes of noise and interference level. Used spectrum

sensing method depends on the system requirements. Those include, for example, fast computation,

simplicity, secure communication, reliability, accurate results, and intelligence. Sensing performance

can be improved using two-stage sensing and/or cooperative sensing.



As can be seen, spectrum sensing techniques have different complexities. Next, a literature review about

main spectrum sensing methods is presented. Reviewed sensing methods are divided in two sections:

individual sensing methods and hybrid sensing method. Former one includes techniques that operate

independently and latter one includes two-stage methods and cooperative methods.

3.1 Individual Sensing Methods

Sensing in TVWS was considered in [17] where authors used a feature detection. ATSC (Advanced

Television Systems Committee) digital TV sensing can detect the ATSC pilot tone. Instead, NTSC

(National Television System Committee) analog TV includes luminance carrier that can be detected. In

the case of wireless microphone signals, PSD (Power Spectrum Density) can be used even though the

location of the carrier frequency may vary within the channel. Thus, wireless microphone signals can

be detected even when SNR = – 20 dB. There are also narrowband (NB) interference spikes, so only

PSD detection is not enough. Features that can be used include local power around the grid point, PSD

height and average bandwidth (BW).

Review about parametric and non-parametric test statistics used in spectrum sensing in the presence of

a DVB-T (OFDM) signal in TV white space was presented in [18]. A flat Rayleigh fading channel and

using 6-path Typical Urban (TU6) mobile radio propagation model (i.e. frequency- and time-selective

Rayleigh fading channel) were used. Considered methods include energy detection, multi-antenna

eigenvalue-based methods and methods that exploit signal characteristics like cyclic prefix. Non-

parametric tests with perfectly known noise variance include Roy’s Largest Root Test which tests the

largest eigenvalue of the sample covariance matrix against the known noise variance, energy detection

and Likelihood Ratio Tests (LRT). Non-parametric tests with unknown noise variance include GLRT,

Eigenvalue Ratio Detector (ERD) and Noise-Independent LRT (LRT-E). Detectors based on cyclic

prefix autocorrelation were based on Cyclic Prefix (CP) correlation. Open platform called GNU Radio

was used to implement the algorithms. When considering detection probability, in a flat Rayleigh fading

channel, the best method was RLRT followed by GLRT, LRT, ED, LRT- and ERD, respectively. Signal

detection probability 0.9 was achieved when –9 < SNR < –7 dB. When using TU6, the best methods

were LRT, ED and RLRT followed by GLRT, LRT-E and ERD, respectively. Detection probability 0.9

was achieved when –7 < SNR < –3 dB, depending on the method.

Feature detection-based spectrum sensing was studied in the presence of Chinese TV standards Digital

Terrestrial Multimedia Broadcast (DTMB, terrestrial reception), China Mobile Multimedia

Broadcasting (CMMB, handheld reception) and Phase Alternating Line (PAL-D/K, for analog TV) in

TV white space in [Kocks12]. Spectrum sensing thresholds were obtained assuming the same procedure

established for IEEE 802.22, leading to –114 dBm for 6 MHz channel and –112.8 dBm for 8 MHz

channels. In the presence of DTMB, the sensing was based on autocorrelation, as in the presence of

CMMB, the sensing is based on cyclic prefix of OFDM, since this is the waveform employed in this

standard. In the case of PAL-D/K, sensing was based on periodicity of certain parts of the signal. In the

case of DTMB, Pd = 0.9 when –113 < SNR < –109 dBm, depending on used Pfa (0.1 to 0.001). In the

case of CMMB, Pd = 1 when –113 < SNR < –109 dBm, respectively. In the case of PAL-D/K, Pd = 0.9

when SNR = –122 dBm (simulations) or SNR = –103 dBm (measurements).

In [19], Fast Fourier Transform (FFT) pilot detection and cyclostationary feature detection sensing

methods in TV white space were studied. When considering IEEE 802.22, it was noted that the average

noise PSD at the receiver is –163 dBm / Hz when noise floor is assumed to be –174 dBm / Hz, so the

average power of the noise in 6 MHz BW is approximately –95 dBm. If it is assumed that the minimum

signal power for detection is –116 dBm, minimum SNR for signal detection is then –21 dB. In the FFT

pilot sensing, signal pilot was filtered using narrow BPF and the result is compared to a threshold that

is defined a priori. The signal is present if the strength (i.e., energy) of the signal is above the threshold.

In the cyclostationary feature detection, 40 kHz BPF around the pilot is used and frequency domain

correlation was performed to reduce the complexity. In the simulations, bandwidth was 6 MHz, thermal

noise = –106.2 dBm, noise figure = 8 dBm, noise power = –98.2 dBm, SNR threshold = –21 dB and



signal detection threshold = –119.2 dBm. Pd = 0.9 was achieved when –25 < SNR < –17 dB for FFT

pilot sensing, depending on the sensing time, and –22 dB for cyclostationary feature detection.

An energy ratio algorithm was proposed in [20]. The method detects appearance of primary user by

sensing the change of signal strength over sub-carriers. A drawback is that the method detects only

multicarrier signals. The method has high immunity to frequency-selective fading, and its complexity is

only twice the complexity of energy detection.

In [21], several compressive sensing methods were proposed. Compressive sensing is a relatively low-

cost method that reduces the processing time. It consists of directly acquiring a sparse signal in its

compressed form. It includes the maximum information using a minimum number of measurements. At

the receiver, the original signal is recovered. One method combines the advantages of circulant matrix

with Bayesian models. The method can operate under uncertainties. Another proposed method uses

Bayesian compressive sensing based on the Toepliz matrix sampling. This method enhances the sensing

efficiency. Third method is a real-time spectrum scanning based on compressive scanning that speeds

up the scanning and minimizes the complexity.

In [22], FM wireless microphone sensing was studied. Autocorrelation function of wireless microphone

signal was used as a base of detection. In the presence of adjacent channel interference, higher order

statistic-based sensor was used when determining the threshold. Detection was successful when

SNR=-18 dB in the presence of adjacent channel interference.

Wireless microphone sensing was considered in [23], where a detector based on the estimation of signal

parameters via rotational invariance techniques (ESPRIT) algorithm and pre-whitening filter estimated

from an auto-regressive model were used. The method was able to find signals with SNR = –21 dB and

it also mitigated the effect of DTV transmission leakage. The method outperformed method presented

in [22] when considering miss detection probability.

In [24], single user multi-threshold sensing method based on phototropism was proposed. In

phototropism, the plant grows towards the light. AWGN channel was used. It was noticed that the

method outperformed classic ED, MF and cyclostationary detection. However, defining required

distribution was problematic.

Savitzky-Golay smoothing sensing method was presented in [25]. Method reduces noise and

outperforms standard linear moving average method. Authors evaluated performance of the method in

case of wireless microphone and DVB-T signals. Authors found that the method operates well when

SNR > –10 dB.

In [26], two wavelet-based sensing methods were proposed. The first one is based on wavelet entropy

estimation based on wavelet transform. Its computational complexity is low and it is cost-effective and

robust again noise uncertainty. The method finds signals with SNR > – 9 dB. Another proposed method

is wavelet packet entropy-based sensing based on wavelet packet transform. The computational

complexity is also low, when detecting signals with SNR > – 6 dB. The weakness is that both the

methods operate only in low-frequency bands.

In [27], covariance absolute value (CAV) and covariance Frobenius norm (CFN) methods were

proposed. Therein, smoothing factor was used, and autocorrelation was computed in order to form the

sample covariance matrixes. Their complexities are relatively low. Simulations were performed in the

presence of DTV signals. This method achieved Pd > 0.9 at SNR = –18 dB.

In [28], several combined energy detection (CED) methods were proposed. In the simulations, wireless

microphone signals were used. Blindly CED (BCED), which computes the sample covariance matrix

of the received signal, had the best performance, requiring –18 < SNR < – 20 dB to achieve Pd = 0.9.

Transmission parameter estimation for OFDM systems was considered in [29]. It was noticed that, in

the case of OFDM signal, most of the main parameters can be estimated blindly.



Energy detection-based sensing was considered in [30]. The used threshold was derived using central

limit theorem. The results were obtained assuming QPSK and DVB-T signals in an AWGN channel. It

was found that SNR > –12 dB to achieve Pd > 0.9.

Enhanced energy detection-based sensing method for detecting wireless microphone signals was

proposed in [31]. The blind sensing method uses random matrix theory. The method outperforms

conventional energy detection but is also more complex.

Localization algorithm based on double-thresholding (LAD) method is a blind, CFAR-type enhanced

energy detection method [32], [33]. The LAD method uses forward consecutive mean excision (FCME)

thresholds. The iterative FCME threshold is calculated a priori using a pre-determined false alarm rate

and statistical properties of the noise (chi-squared distribution). The LAD method calculates two FCME

thresholds with two different false alarm rates. The LAD method groups together adjacent samples that

are above the lower threshold. The signal is defined to be detected if at least one of the samples is also

above the upper threshold. This reduces the probability of false detection of signal. Computational

complexity of the LAD method is low, being O(N log2(N)), where N is the number of samples in the

detection window. The LAD method is robust against the noise variance, it does not need any knowledge

about signals or the noise and can be used in all frequency areas. The method has a good performance

even in the presence of multipath interference. The narrower the signal the better the LAD method

operates. However, it is enough that BW of the signal is less than 90% of the studied BW. In the case

of the LAD method, Pd = 0.9 is achieved when SNR is at least –2 dB (signal BW 5%). The performance

of the LAD method can be enhanced using adjacent cluster combining (ACC) or using two dimensions

(2D). In the former case, some samples can be below the lower threshold between two accepted clusters

in order to avoid separating the signals. Pd = 0.9 is achieved when SNR = –7 dB (signal BW 12.5%).

The latter one uses time domain processing after frequency domain processing. Pd = 0.9 is achieved

when SNR = –8 dB (signal BW 12.5%). Note that even though the FCME thresholds are calculated

based on the assumption that the noise is Gaussian, the LAD methods are able to operate if the detected

signal follows some other distribution than Gaussian [33].

Spectrum window-based signal energy detection was considered in [34]. Therein, a window-based

detection technique was adopted in signal detection. The length of the used detection window can be

selected based on the bandwidth of the signal to be detected, if it is known. The received frequency

domain energy samples are divided into overlapping detection windows with 50% overlap and samples

in each window are summed up among themselves. Used detection threshold is based on the chi-square

distributed noise variance with 2M degrees of freedom. In the simulations, there were AWGN channel

and a BPSK signal. Pd = 0.9 was achieved even when SNR = –14 dB, depending on the length of the

relation between the bandwidth of the signal and length of the detection window.

3.2 Hybrid Sensing Methods

In this section, hybrid sensing methods are considered. Hybrid sensing methods include here cooperative

and two-stage sensing methods.

Cooperative sensing

The major reasons to use cooperative spectrum sensing (CSS) are fading, Doppler effect, shadowing

and receiver uncertainty problems [35], [36], [15]. In cooperative sensing, information from severe

spatially distributed radios (or their antenna components) is used. This helps to prevent so called hidden

terminal problem, where the radio is not able to sense the signal because of low SNR caused by

shadowing. Another benefit of cooperative sensing is that single UE’s probability of detection

performance can be lower when compared to the non-cooperative system. In cooperative system, the ith

UE’s probability of detection can be calculated as 𝑃d,𝑖 = 1 − √1 − 𝑃d_tot𝐾 , where K is the number of

cooperative radios and Pd_tot is the total probability of detection performance of the cooperative system.

Because cooperation gives diversity gain, simple energy detection is the most common detection

technique in cooperative sensing, even though its performance non-cooperative detection is worse when

compared to other techniques [15].



Cooperative sensing can be done in a distributed or centralized fashion [37], [38], [39], [40]. In

distributed sensing, radios operate independently and communicate among themselves. Local sensing

results are shared with other radios, and decisions are made locally. They can either cooperate or

compete with other radios. Selfish radios may collapse the system. The radios should maximize overall

gains instead of individual gains. Therein, costs are lower than in centralized cooperation, because there

is no need to a common control channel (CCC).

In centralized cooperation, the sensing process is coordinated by a fusion center (FC), so the final

sensing decision is delivered to all cooperative radios. Sensing reports are combined using either soft,

quantized soft or hard combining. Sensing results are transmitted either entirely, quantized, or using

one-bit, respectively. Soft combining offers best detection performance, but the control channel

overhead is high because each UE’s sensing samples must be transmitted to the FC. Also, the energy

cost of transmitting all quantized samples to the FC is high. There exist several cooperative sensing

methods in the literature. Selected methods are introduced below.

In [41], a cooperative spectrum sensing method, which first checks status of the primary spectrum using

advanced sensing or database, is proposed. Then selected sensors located in a wide area perform sensing

(reduces the amount of shadowing/fading), and earlier sensing duration affects to the local sensing

decision. Cooperative decision is based on the majority rule which mitigates against the noise

uncertainty problem.

Linear inertia weight particle swarm optimization (LINWPSO) cooperative spectrum sensing was

proposed in [42]. It uses optimal weight setting. It outperforms modified detection coefficient methods,

but it has high computational complexity.

Joint-sparsity adaptive matching pursuit (SAMP) cooperative spectrum sensing was presented in [43].

The method outperforms several joint detection algorithms, but the weakness is that only 15% of the

channels can be occupied.

In [44], sensing method CSS was proposed. The method rewards truthful and accurate reporting in

distributed cooperative system. The method reduces the effect of spurious reputation values.

In [45], listen-and talk (LAT) CSS protocol was proposed. It senses and access spectrum simultaneously

with the assistance of full-duplex techniques. The method outperforms listen-before-talk (LBT) when

secondary user’s transmit power is low. The weakness is the transmit power-throughput tradeoff.

Cooperative user selection

In conventional cooperative spectrum sensing, usually information from all sensors are used. Therein,

all radios make sensing and send a report, and central identity decides which reports are taken into

account. However, this increases overheads (required bandwidth, extra sensing time and amount of

reports sent) and increases power consumption. In cooperation, overhead should be minimized and gain

should be maximized. Because of overhead (likes delay and extra sensing time), it is not optimal to use

all radios if the number of radios is large. Thus, it may be better to select only part of the radios, besides

the fact that is better to use more radios with weaker decisions than fewer radios with very good

decisions [15].

The problem is that it is not known which of the radios have the best detection performance – and which

radios are even able to ‘see’ PU (or sensed signals). Optimal selection of radios is in a key role when

considering the performance of cooperative sensing because it affects to the achieved gain from

cooperation and the amount of increased overhead. There is a trade-off between the overheads and

detection performance. The robustness of sensing results can be increased via radio selection, for

example, when there is shadowing. However, the selection of radios is not trivial. If the geographical

locations of radios are known, it is a little easier task. Both distribution of UEs and distance between

UEs must be taken into account. There are several methods dealing with that problem. One way is to

define that radios having the highest detection probabilities have also the best detection performance

[46]. It is also possible to select users that have minimum correlation measure among them [47], or

select UE with the largest reporting channel gain to be the cluster head that collects and forwards other



user’s reports to the FC [48]. The latter one reduces the reporting error in reporting channel. In [49], the

authors study clustering methods (random, reference-based, statistical and distance-based) for selecting

proper users. From those, only random clustering method does not need to know the location

information. In [50], UEs are selected based on the received PU signal power and distance from BS.

This method outperforms conventional K-mean clustering. Uncorrelated user selection in mobile

network was considered in [51]. Therein, distributed selection algorithm was developed. However, there

may also be selfish or hostile (malicious) radios, that give wrong decision in order to lower the detection

performance, and those should be removed. There are also several proposals how to find those. The

selection of radios can be done centralized or cluster-based. In centralized selection, central identity

makes the decision. The centralized decision can be made, for example, based on the location of radios

(distance between radios) or based on the correlation among radio measurements (high correlated radios

are removed). Here, the overhead may be high because the used radios are selected after they sent their

sensing reports. In cluster-based selection, radios are grouped to clusters. Clustering can be done, for

example, using the location information of radios. In random clustering, all the radios are divided into x

clusters randomly, and this can be used when locations are not known. The questions are: what is the

accepted relation between the overheads and detection performance, and what is the accepted amount

of transmitted data in common control channel?

Many existing cooperative methods do not consider mobility. A known radio location make things

somewhat easier, mobility makes things more difficult. Fading, shadowing, hidden terminal problem

among other effects. vary if radios are not stationary. So, mobility of radios affects the decisions about

which radios shall be used. One decision is valid only in one moment of time and this increases

overheads. However, mobility can also improve the achieved cooperative gain. PUs like TC signals are

assumed to be stationary, but other PUs like wireless microphones may be mobile. Then, PU tracking

methods could be used. The number of cooperating radios can be decreased if individual sensing is

performing more often (because of mobility). However, it is important to notice that spatial and time

diversities are reached.

As a thumb or rule it can be said that 10 – 20% of users may be enough if they are not highly correlated

and if there are, say, 100 or more users. If the nodes are static, one simple and efficient method is to

select nodes that are least correlated. If the nodes are moving, one way to get diversity and, thus, small

correlation between the nodes, is to divide area into a grid, and select single squares so that in each

selected square one node is selected. Because the less correlated the nodes are, the better the sensing

performance is. Figure 9 illustrates selection of cooperative nodes in a varying terrain.

Figure 9. Selection of users for cooperative sensing decision making.



It is also a common problem in what way radios are cooperating to perform sensing (model of

cooperation). Therein, optimal detection performance is the goal. Proposed approaches are, for example,

parallel fusion (PF), data fusion and game theoretical models. In PF, distributed signal processing

methods are used to determine how the decisions are made. Radios sense the channel, report their results,

and central identity makes the decision. Game theory-based models study radio’s interactions and

behaviour. Examples are coalitional or evolutionary game. In the first one, there are coalitions (groups),

as in the latter one, users are assumed to be selfish and their own benefits determines do they want to

cooperate or not.

Sensing reports are usually sent using a CCC. Bandwidth limits the amount of transmitted data.

Unnecessary reporting can be avoided, for example, using only part of the radios. After sending the

reports, data fusion combines the sensing results and makes the decision. Therein, either decision fusion

rules or signal combining methods are used, based on the type of the data. In soft combining, all sensing

samples/whole results are sent. In quantized soft combining, results are quantized before transmitting.

In hard combining, only one-bit decision is sent. Soft combining offers best performance because central

identity gets more information, but the amount of overhead is high. Instead, hard decision requires less

overhead but the performance is worse because not all information is sent.

Local decisions can be sent using orthogonal, non-orthogonal or random signalling. In orthogonal

signalling, it is possible that the orthogonality is lost in the receiver. In addition, it may be challenging

to have an orthogonal channel for every radio, and it requires considerable amount of bandwidth. Non-

orthogonal signalling may be more practical, but it degrades the final performance. One possibility is to

use random signalling.

In two-stage spectrum sensing, sensing is performed in two stages. Usually, coarse sensing is

performed first using some simple sensing method and, if necessary, sensing result is ensured in the

second stage where more complex and accurate fine sensing is performed. Two-stage sensing gives

more accurate results but requires more time and may be more complex than one-stage sensing.

In [52], two energy detection methods were considered. Both methods produce first coarse and then fine

sensing. Those are energy detection to covariance absolute value detection (CAV) and energy detection

to maximum-minimum eigenvalue detection. In CAV, autocorrelation of the received signal is

compared to CAV. Noise power information is not required, but CAV does not operate properly if

autocorrelation of signal is low. MMED uses the ratio between maximum and minimum eigenvalue of

the received signal. Also, MMED operates poorly if autocorrelation of the received signal is low. Both

proposed methods, ED+CAV and ED+MMED, give accurate detection of noise power, but require long

sensing time. The results presented in the literature employs wireless microphone signals in an AWGN

channel. Pd = 0.9 was achieved when –15 < SNR < –13 dB, depending on the used smoothing factor.

In [53], two-stage spectrum sensing named adaptive two-stage spectrum sensing (ATSS) was proposed

under noise uncertainty environment. It consists of four modules: noise estimator, stage activator, first-

stage (coarse) sensing and second-stage (fine) sensing. Modified-double constraints adaptive energy

detection (Mo-DCAED) is used in coarse sensing as adaptive maximum eigenvalue detection (AMED)

is used in fine sensing. ATSS adapts the decision threshold in both stages. Wireless microphone signal

(correlated) was used. Simulations were performed as a function of distance (max. 500 m).

The authors in [54] present two-stage (TS) cooperative spectrum sensing (CSS) method that includes

particle swarm optimization (PSO) algorithm. The method groups secondary users in two groups before

sensing. The first group performs energy detection and FC makes the sensing decision. If channel is

occupied, method stops. If channel is idle, the second group performs energy detection and FC makes

the decision based on results from the both groups. Method uses sparsity adaptive recovery algorithm

in order to solve the energy efficient optimization problem. The method outperforms single-stage CSS.

In [55], first stage (coarse) sensing uses energy detection as second stage (fine) sensing uses

cyclostationary detection. Detection thresholds were defined so that the probability of detection was

maximized. DVB OFDM signals were used. The method outperformed pure energy and cyclostationary



based sensing. A SNR = –18 dB ws necessary to achieve Pd > 0.9, as pure energy and cyclostationary

detectors required SNR = –16 to obtain Pd = 0.9.

In [33], two-stage channel selection method was proposed. Based on a request, channel history database

submits the most probably unoccupied channels as best candidates. In the first stage, power level

detection is performed. If the channel seems to be idle, a full signal detection is performed to ensure the

channel is really idle. In [56], the method was enhanced by classifying the traffic to deterministic and

stochastic, and by using prediction methods for different traffic types to estimate the following idle

times.

Spectrum sensing with geolocation database at TV white space has been considered in [57]. Therein,

joint sub-Nyquist wideband spectrum sensing with database was proposed. The goal was to enhance the

detection performance of TV signals. Database gave a priori information, and sensing was performed

on a some potentially vacant channels. In [58], it was noticed that sensing can assist database so that the

amount of available TV white space is increased. Spectrum sensing, cooperative sensing, database and

beacons were discussed in [59]. In [60], it was noted that spectrum sensing is useful especially in rural

areas, where database does not have all relevant factors like proper propagation models, efficient and

frequent connection to the database, and existence of detailed and relevant database information about

the area of interest.

In Table 1, selected set of spectrum sensing methods and their performance results considered above are

presented. In the performance column, some results are given as a SNR range since evaluated method’s

performance is varying depending on the parameter setting.

Table 1: Selected set of spectrum sensing methods – operation and performance.

Ref. Signals and operational principle Channel Performance

[17] WM signals:

PSD based detection

low-power NB man

made noise Pd = 1 at -100 dBm

[18]

OFDM signals:

RLRT, GLRT, LRT, ED,

LRT-E, ERD

flat Rayleigh fading

channel: TU6

Pd = 0.9 at –7 < SNR < –9 dB

Pd = 0.9 at –3 < SNR < –7 dB

[61]

DTMB: autocorrelation det.

CMMB: cyclic prefix det.

PAL-D/K: periodicity det.

Pd = 0.9 at –109 < PRX < –113 dBm

Pd = 0.9 at –109 < PRX < –113 dBm

Pd = 0.9 at PRX = –122 / –103 dBm

[19]

DTV signals:

-FFT pilot detection

-cyclostationary feat. det.

Pd = 0.9 at –17 < SNR < –25 dB

Pd = 0.9 at SNR = –22 dB

[22] WM signals: autocorrelation+high

order statistics

adjacent channel

interference Pd = 1 at SNR = –18 dB

[23] WM signals: ESPRIT+autoregressive Pd = 0.9 at SINR = –21.5 dB

[26] BPSK signals: wavelet-based methods AWGN; only low-

frequency bands

Pd = 0.9 at –13 < SNR < –5 dB

[27] DTV signals: CAV, CFN Pd = 0.9 at SNR = –18 dB

[28] WM signals: BCED multipath channel Pd = 0.9 at SNR = –18 /–20 dB

[30] QPSK and DVB-T signals: ED AWGN Pd = 0.9 at SNR > –12 dB

[32],

[33] RNB signals: (2-D) LAD AWGN +multipath

Pd = 0.9 at SNR = –8 dB (depends

on signal BW)

[34] RNB signals: spectrum window-based

signal detection AWGN

Pd = 0.9 at SNR = –14 dB

(depends on signal BW and

detection window length)

[52] WM signals: ED+CAV, ED+MMED AWGN Pd = 0.9 at –13 < SNR < –15 dB

[55] DVB OFDM: ED+cyclostationary det. Pd = 0.9 at SNR = –18 dB



4 Signal Classification Model for 5G-RANGE

For spectrum sensing purpose, it is important to analyze what type of signals are expected to be present

in the system scenarios. In addition, different users’ signals have different protection levels and they

must be classified to design a spectrum sharing model. Here will be analyzed the 5G-RANGE core uses

from the signal classification point of view. Then a spectrum sharing model will be defined based on

the different signal types expected to be present in the 5G-RANGE scenarios.

In D2.1 [62], the 5G-RANGE project’s use cases and potential applications are defined, and different

type of links are identified to be present in these scenarios:

(1) BS to UE (downlink) and UE to BS (uplink) or UE to wireless relay to BS (uplink), both low

mobility and high-speed. (2) D2D communications (UE to UE, downlink and uplink). Low mobility, distances of few

hundred meters.

(3) Real-time monitoring of a farm crop, i.e., BS to drone (downlink) and drone to BS (uplink).

Use of aerial vehicles, such as drones. (4) Real-time monitoring of farm machinery, i.e., BS to UE (downlink) and UE to BS (uplink), or

UE to RS to BS (uplink). Use of smart truck or tractors. (5) Local BS to Wireless backhaul (uplink) and Wireless backhaul to BS (downlink)

The core use cases defined in [62] are:

(1) Agribusiness and smart farming for remote areas that includes video surveillance, Wi-Fi (e.g.

on-board computers and cameras), GPS, as well as vehicle display and Controller Area

Network (CAN) bus embedded sensors, and Internet of Things (IoT) devices for data

collection. (2) Voice and data connectivity over long distances for remote areas that includes such services as

Voice over Internet Protocol (VoIP), multimedia on the web, audio/video conference and

basic internet services. (3) Wireless backhaul providing Internet connection to rural locations, e.g., small village, tourist

venues, industrial and farming premises.

(4) Remote care (e-health) that includes high quality video streaming (video conference, online

doctor, remote monitoring, etc.).

Based on above and CBRS model introduced in Section 2.1, we propose that existing signals will be

divided into three levels (Figure 10):

Level 1 consists of protected IUs that include TV signals and other protected IU signals such as wireless

microphone signals. TV signals include DVB-T, DVB-T2, ISDB-T, ATSC (TV signal that needs digital

aerial) and NTSC (TV signal that needs analog aerial).

Level 2 consists of protected, permanent SU signals that are from built systems meant to operate several

months – several years. Those include real-time monitoring systems which have temporary channel

allocation, e.g. in case of farm crop and machinery (drone systems, smart trucks and tractors), sensor

networks at farm and industry, industrial monitoring systems, etc.

Level 3 consists of unprotected, temporary SU signals. This level can be further divided into two levels:

Level 3a includes high priority SU signals like e-health (video conference, online doctor, remote

monitoring).

Level 3b includes lower priority SU signals.

Level 1 and level 2 signal information are stored in a database. Level 2 signals are not allowed to

interfere Level 1 signals, as level 3 signals are not allowed to interfere level 1 and 2 signals. In addition,

level 3b signals are not allowed to inferfere level 3a signals.



Figure 10: Proposed spectrum sharing model.

Spectrum sensing is needed, e.g., in the following situations [14]

• a connection to the database is lost like in crisis situations and under an cognitive attack

(hostile/selfish users) ;

• transmission powers are small and the transmitter and the receiver are close to each other. For

example, in case of wireless local area network in home;

• the amount of wireless traffic is small and short term, e.g. in case of wireless sensor and other

IoT devices;

• transmission powers are small and transmission based on sensing requires a lot less power than

asking for a permission;

• local network is in a remote area and there are only few members, like in national parks, small

isolated village;

• spectrum usage is temporary and transmission powers are small;

• finding out are there any local networks present, for example, when building a new local

network;

• in rural areas, where database does not have all relevant factors like proper propagation models,

efficient and frequent connection to the database, and existence of detailed and relevant database

information about the area of interest [60].

In 5G-RANGE use cases, the spectrum sensing is required at level 3 to find out free channel resources

for operation and to enable coexistence of different SU signals in that level. Basically, if upper levels

have a connection to the database, the spectrum sharing can be managed using purely that information

among devices at those levels. However, when the connection to the database is lost, sensing is required

also for level 2 signals to avoid interfering level 1 signals. In addition, we propose that spectrum sensing

can be used in level 2 also to improve reliability of decisions, i.e., spectrum sensing capability can be

used to guarantee that a frequency band assigned as vacant by the data base is indeed empty, and vice

versa. For example, non-authorized broadcasting might be present, or the propagation mechanisms in a

given region allow for a DTV receiver to get a signal in an area where should not be coverage based on

the propagation prediction software calculation.



Table 2: Summary of spectrum sharing model.

User type Signal type Primary spectrum

decision policy

Secondary

spectrum

decision policy

Comment

Level 1 (PU) TV signals (ATSC,

NTSC),

Database and optional

spectrum sensing

Only sensing Fixed channel

allocation for

PUs

Level 2 (SU) Secondary users,

e.g. wireless

microphone

Database and

spectrum sensing

Only sensing Temporary

channel

allocation for

SUs (e.g. 0.5 - 3

years).

Level 3a (SU) 5G-RANGE, other

high priority

secondary users

Spectrum sensing and

database

Only sensing No permanent

allocation

Level 3b (SU) 5G-RANGE, other

low priority

secondary users

Spectrum sensing and

database

Only sensing No permanent

allocation

Table 2 summarizes the characteristic of proposed spectrum sharing model for 5G-RANGE. Level 1

includes TV signals which are class 1 signals according to definition introduced in Section 3. Level 1

devices have a fixed channel allocation and they do not need to use spectrum sensing, but it can be used

optionally to improve reliability. Channel allocations are stored in database which can be used by each

layer devices. Level 1 devices are protected PUs, i.e., lower level devices must avoid interfering the PU

signals.

Level 2 includes SU devices which have a temporary channel allocation, e.g., for few months or years

durations. This level includes e.g. wireless microphone signals that are class 1 signals as defined in

Section 3. Level 2 can include also class 2 signals. Level 2 channel allocations are stored in database

and level 3 devices must avoid interfering them. Depending on the situation and scenario, level 2 devices

may also use spectrum sensing to avoid interfering of protected PUs at level 1.

Level 3 included 5G-RANGE devices and other SUs that will use the free spectrum available at the TV

bands. Level 3 can include class 1 or class 2 signals. 5G-RANGE devices will use spectrum sensing and

channel allocation information available from database to find out free channel opportunities. Spectrum

sensing can be used to improve the reliability of decisions and make the free channel resource discovery

more dynamic and efficient in comparison to pure database approach. If level 3 devices do not have

access to database, spectrum decision will be made based purely on sensing information. Level 3 devices

must avoid interfering of upper level users which have fixed or temporary channel allocation. Spectrum

sensing (in-band) is needed to notice the appearance of level 1 and level 2 users at the frequency band

used by 5G-RANGE devices. In addition, spectrum sensing will be used by 5G-RANGE devices to

improve coexistence with other 5G-RANGE users. In the next section, the focus is on performance

evaluation of spectrum sensing techniques for 5G-RANGE devices to enable cognitive spectrum usage.



5 Spectrum Sensing Performance Evaluation

Here, the performance evaluation of the spectrum sensing method is provided by simulation results.

These results are used to select suitable spectrum sensing methods for 5G-RANGE. In Section 5.1, two

energy detection-based methods are studied, while Section 5.2 focuses on GRCR and eigenvalue-based

methods.

5.1 Signal detection using LAD and WIBA methods

Two energy detection-based blind spectrum sensing methods are studied here, namely the window-

based signal detection method [34] and the localization algorithm based on double-thresholding (LAD)

signal detection method [32], [33]. Both methods calculate the noise levels by themselves using adaptive

thresholds. As window-based signal detection method (WIBA) uses overlapping (windowing) blocks to

ensure its detection performance, the LAD method uses two detection thresholds. Both methods

outperform the general energy detector [33], [34] and are suitable in spectrum sensing applications. The

LAD method is widely studied, as the window-based signal method is a novel method proposed in 2018.

Both the considered methods are:

• blind, i.e., no information about the signal(s) or noise level is required. The methods can

estimate the noise level themselves;

• independent from frequency range, i.e., they can be used in any frequency range from kHz to

GHz;

• measuring energy, i.e., signal type or modulation method does not matter;

• assuming AWGN noise, however, the noise does not need to be Gaussian. It is enough that the

distribution of signal(s) and noise differ, see, for example, [33];

• robust for noise variance, because they estimate the noise level themselves.

Detected signals must be class 2 signals (above or close) the noise level and relatively narrowband

(RNB).

In both methods, the detection threshold calculation uses detection threshold parameter Tp that is

calculated a priori using a pre-selected false alarm rate Pfa and the statistical properties of the noise.

This is so called constant false alarm rate (CFAR) principle. It is assumed that frequency-domain

samples xi are zero noise, independent, i.i.d. Gaussian distributed complex random variables. The energy

of sample xi is yi=|xi|2 and it follows a chi-squared distribution. Let us have chi-squared distributed

variables with 2M degrees of freedom. Thus, the threshold parameter Tp is found by solving [63]

𝑃fa = 𝑒−𝑇𝑝𝑀∑

1

𝑘!𝑀−1𝑘=0 (𝑇𝑝𝑀)

𝑘, (2)

where Pfa is a pre-selected false alarm rate. Note that the equation does not depend on the variance.

When M = 1, variables follow chi-squared distribution with two degrees of freedom, and it leads to

CME parameter

𝑇cme = − ln(𝑃fa). (3)

For example, when Pfa = 1% = 0.01, i.e., 1% of the samples is above the threshold when there is only

noise present, Tcme = – ln(0.01) = 4.605.

5.1.1 Method descriptions

The localization algorithm based on double-thresholding (LAD) method [32], [33] calculates two

forward consecutive mean excision algorithm (FCME) thresholds using two different threshold

parameters as follows: variables |xi|2 are arranged in an ascending order according their energy and 10%

of the smallest samples form an initial set. The FCME threshold is [32]

𝑇ℎ = 𝑇𝑐𝑚𝑒𝑥2̅̅ ̅, (4)



where

𝑥2̅̅ ̅ =1

𝑄∑|𝑥𝑖|

2.

𝑄

𝑖=1

(5)

In the first iteration, Q is the number of samples in the initial set. The samples below the threshold are

added to the set Q, and this iterative process continues until there are no samples below the threshold.

After calculating the two FCME thresholds, the upper and lower ones, the LAD method uses clustering

to group adjacent samples assumed to be from the same signal. The LAD method clusters together

adjacent samples above the lower threshold. The cluster is accepted to be caused from a signal if at least

one of the samples is also above the upper threshold. The performance of the LAD method is improved

using an ACC parameter that allows p (usually p = 3) samples to be below the lower threshold between

two accepted clusters [33].

The window-based method (WIBA) [34] divides signal into overlapping blocks. Assume that there

are n observation (signal samples) that are divided into L overlapping blocks (or detection windows)

with length M so that the overlapping degree is 1/2 as illustrated in Figure 11.

Figure 11: Example of overlapping blocks of the window-based method.

Samples in each block are summed up among themselves, that is, 𝐸𝑙 = ∑𝑌𝑖(𝑙) , i.e., the decision

variables are the total energy of El of each block. The signal detection threshold is [34]

𝑇ℎ = 𝑇𝑝1

𝐿∑𝐸𝑙

𝐿

𝑖=1

(6)

where Tp comes from (2).

5.1.2 Performance analysis

In the computer simulations using Matlab, the window-based (WIBA) method was studied and

compared to the LAD method. Probability of detection, number of detected signals, and bandwidth

estimation error were studied in the presence of one signal, one signal with multipath components, and

two signals. Detection performance as well as the optimal length of the used detection window were

studied. An adjacent version of the LAD method with ACC parameter p = 3 was used. There were

complex AWGN channel and a BPSK signal with a relative bandwidth from 5% up to 30% of the

system's bandwidth. The BPSK signal was bandlimited by a root raised cosine (RRC) filter with a roll-

of factor of 0.22. The number of samples was N = 1024, and length of the detection signal (M) varied.

The LAD threshold parameters were 13.81 and 2.66 [33]. The window-based method used false alarm

rate 0.01, unless otherwise stated, and the used threshold parameter depends on M. The probability of

detection was defined so that the signal is detected if its center frequency is detected. 1,000 Monte Carlo

iterations were performed.



5.1.2.1 One-signal case

In Figure 12, probability of detection vs. SNR and the number of detected signals vs. SNR are presented

for a signal with 5% (52 samples) of the overall bandwidth. The window sizes are now one (LAD), 4,

10, 52 and 102 so that there are too short (4 and 10) and too long (102) windows. It can be seen that the

window-based method has better performance than the LAD method, regardless of the window size.

The best performance is achieved when using the optimal window size in terms of detection performance

(52). That is, when window size (in samples) equals with wide of the signal (in samples). If the window

size is increased, the performance begins to drop, as can be seen for the long window (102) curve. With

too short window (4 and 10), the detection probability is about 4 or 8 dB worse than the detection

performance with optimal window size. Both the optimal length window and too long window estimated

the number of detected signal correctly as too short windows found too many signals at low SNR values

[32].

Figure 12: Probability of detection vs. SNR (left) and the number of detected signals vs. SNR. The signal

bandwidth is 5% of the overall bandwidth [34].

Next, bandwidth estimation accuracy is studied. Relative mean square error (RMSE) for bandwidth is

defined to be

RMSE = √1

𝑁∑(

𝛾 − 𝛾

𝛾)2𝑁

𝑖=1

(7)

where γ is the real bandwidth and 𝛾 is the estimated bandwidth. In Table 3, results are presented when

there is signal with 10, 20 or 30% bandwidth, and window length is 52, 102, 204 and 306. For example,

when signal bandwidth is 10% and M = 102, RMSE is 100% for window-based method. Instead, RMSE

for LAD method is 8%. This is illustrated in Figure 13, where it can be seen that the LAD method has

better bandwidth estimation accuracy.

Table 3: Comparison of RMSE for LAD and WIBA with different windows size.

BW % (samples) WIBA M=52 WIBA M=102 WIBA M=204 WIBA M=306 LAD

BW 10% (102 58% 100% 300% 500% 8%

BW 20% (204 15% 50% 100% 198% 6%

BW 30% (306) 7% 15% 33% 100% 13%



Figure 13: Snapshot of a detection of one signal with 10% bandwidth.

Figure 14: RMSE vs. SNR results for a signal with 10 % bandwidth.

In Figure 14, RMSE vs. SNR is presented when bandwidth of the signal is 10% (corresponding first line

in Table 3). It is also marked at what SNR values the methods find the signal (Pd > 0.9). Note that the

window-based method has Pd > 0.9 when SNR > - 13 dB, as the LAD method has Pd > 0.9 when SNR

> 5 dB. That is, the window-based method finds the signal when SNR > – 13 dB, as the LAD method



finds the signal when SNR > 5 dB. It can be noticed that RMSE for the window-based method rises

when SNR rises.

5.1.2.2 Multipath case

Multipath signal includes possible line-of-sight (LOS) and scattered (multipath) components. If there is

one LOS component and scattered components, the channel samples can be considered as Rician

distributed. If there are only scattered components, the channel samples can be considered as Rayleigh

distributed. Let ai, i = 1, …, M be the average amplitude of each multipath component. The energy can

be defined to be E = a12 (only LOS energy) or E = ∑ ai

2, i = 1, …, M (energy of LOS + scattered

components). Multipath affects the phase and amplitude of the received signal.

The LAD method has been earlier studied in multipath situation. On [64], the performance of the LAD

method was studied in the presence of ETSI Broadband Radio Access Network (BRAN)/Wireless Local

Area Network (WLAN) indoor multipath channels B and C. Therein, it was noticed that the LAD

method had 1% up to 3% loss in the detection performance. Here, also the performance of the sum

method is studied.

In the simulations, there are LOS component and two scattered components. The first scattered

component is 3 dB below the LOS component’s SNR, as the second scattered component is 6 dB below

the LOS component’s SNR. In Figure 15 it can be seen that the multipath enhances the performance by

1 dB ~ 2 dB. This is because constructive summation increases the energy of the signal, and this affects

to the detection when using energy detection-based methods.

Here, SNR is defined to include only LOS energy. If SNR includes energy of LOS + scattered

components, the performance is 1 dB ~ 2 dB worse. In that case, the performance equals to the non-

multipath performance.

Figure 15. Pd vs. SNR results in multipath case. SNR is calculated for the LOS component.



In Table 4 and Figure 16, RMSE is presented for 10% BW signal when M = 102 and there are multipath

components. It can be noticed that the bandwidth estimation accuracy of the window-based method does

not suffer from multipath.

Table 4: Relative mean square error (RMSE) [%] for 10% bandwidth when M=102. SNR is for LOS

component.

Multipath WIBA method LAD method

2+10 samples 100 10

20+60 samples 75 15

60+80 samples 55 10

No multipath 100 8

Figure 16: RMSE vs. SNR results in multipath case. Signal BW is 10% and M = 102.

The number of deected signals is always 1 when using window-based method. Instead, when using the

LAD method, the number of detected signals was about 1.5 when there were multipath present.

Table 5 presents the minimum SNR necessary to achieve Pd = 0.9, assuming now that one or two RRC-

BPSK signals might be present. There are one or two signals with 10% and 5% BWs. For example,

when M = 102 and there are two signals with 10% and 5% BWs, performance of the window-based

method is at most ± 2 dB when compared to the situation that there is only one signal with 10% or 5%

BW. Optimal values for M are 102 for 10% BW signal and 52 for 5% signal. Notice that M does not

affect to the performance of the LAD method because there is no windowing. Based on Table 5, multi-

signal situation has only a slight effect in the detection performance of the methods.



As can be seen from Figure 17, the bandwidth estimation accuracy may suffer if M is too wide. Wide

block M means that closely spaced signals can be merged from detection point of view to be seen as one

signal.

Table 5: Required SNR for Pd = 0.9 when there is one or two signals present.

N:o of signals M Signal BWs WIBA method, Pd=0.9 LAD method, Pd=0.9

Two

102 10%

5%

-13

-13

3

-1

One 102 10% -13 1

One 102 5% -14 -2

Two

52 10%

5%

-12

-14

3

-1

Two

40 10%

5%

-11

-14

3

-1

Two

10 10%

5%

-5

-10

3

-1

One 10 10% -5 1

One 10 5% -11 -2

Figure 17: Snapshot of two simulated signals with 5 and 10% bandwidth. M = 52, 102 and 204.



Figure 18: Number of detected signals vs. SNR results. There are two signals with 10% and 5% bandwidths.

In Figure 18, the number of detected signals vs. SNR is presented. Here, M = 10, 40, 52, 102 and 204

has been used. It is also marked at what SNR values the methods find both signals (Pd = 0.9). As M =

10 and M = 40 are both too short windows, M = 52 is optimal for 5% BW signal (=52 samples) and M

= 102 is optimal for 10% signal (=102 samples). It can be seen that short windows (M=10) estimate the

number of detected signals correctly at high SNR: when it Pd=0.9 at SNR = -5, the number of detected

signals is 2.7. This corresponds the behaviour of the LAD ACC method. When window is too wide (M

= 204), signals may merge from detection point of view (see Figure 17) and, therefore, only one signal

is found.

5.1.2.3 5G-RANGE Channel Model Results

This section provides results for the case when using 5G-RANGE channel model defined in WP3. Total

bandwidth of the channel is 23.4 MHz and carrier frequency is 700 MHz. In the simulations, transmit

power [dBm], bandwidth of the signal to be detected and distance between Tx (signal transmitter) and

Rx (signal detector at UE) are varied. It is assumed here that the Rx performs sensing of the channel to

find out which channels are free.

5.1.2.3.1 Single sensing case

It is desired that Pd is as large as possible. Typical requirement is that Pd > 0.9. It is also desired that Pf

is as small as possible. Miss detection probability Pmc = 1- Pd means that signal is not detected when it

is present at the Rx input. Here it is assumed that the detection probability Pd must be at least 0.9. In

single sensing case, individual detection probability Pd,i is considered. Both the WIBA and LAD ACC

methods are studied here.

At first, detection probability vs. distance between Tx and Rx was studied. Used signal bandwidth (BW)

values are 2, 4 or 6 MHz (corresponding to 8.6%, 17.1% and 25.6% of the bandwidth, respectively).

Transmitted signal power values were 53, 46, 30, 20 and 10 dBm. Results are presented in Figure 19 -



Figure 23. It can be seen that the narrower the signal is, and the higher the transmit power is, the longer

is the Tx - Rx distance that the signal can be detected. In Table 6, it is presented what is the maximum

distance between Tx and Rx for different transmit power values and signal bandwidth values with

detection probability requirement Pd,i ≥ 0.9. For example, when transmit power is 53 dBm, signal BW

is 2 MHz and WIBA method is used, signal can be detected (final detection probability Pd,i = 0.9) when

Tx-Rx distance is at most 34 km (first line in Table 6).

Figure 19: Pd vs. detection distance results when transmit power of the signal is 53 dBm.



Table 6: The maximum distance between Tx and Rx, with a requirement that detection probability Pd,i is at

least 0.9, for different transmit power [dBm] and signal bandwidth values [MHz].

Transmit power

[dBm]

Signal bandwidth

[MHz]

Distance between Tx and Rx

WIBA method


LAD ACC method

53

2 <34 km <7 km

4 <20 km <3 km

6 <15 km <1 km

46

2 <15 km <6 km

4 <9 km <3 km

6 <6 km <1 km

30

2 <3 km -

4 < 2 km -

6 < 1 km -

5.1.2.3.2 False alarm probability

Probability of false alarm is the probability of incorrectly detecting that the signal is present even though

the channel is free. It means that a possibility to use a free channel is lost. The larger the false alarm

probability is the higher is the number of lost spectrum opportunities.

The LAD ACC and WIBA methods are both CFAR methods. It means that the probability of false alarm

is set to be constant in the noise-only case, and it is called as a desired false alarm probability (rate)

Pfa,des. In the LAD ACC method, used upper threshold parameter 13.81 corresponds desired false alarm

probability 10-6. In the WIBA simulations, there is only one threshold, and used desired false alarm

probability Pfa,des =0.01 (=1%). That is, when there is only noise present, 1% of the samples are above

the threshold.

Let us assume that we have an experiment with length of N samples, and the experiment consist of tests.

The LAD ACC method compares every sample to the threshold. Thus, in one experiment there are N

tests. Because the WIBA method divides signal into L overlapping blocks with length M and sums up

samples in each block so that each block produces one value that is compared to the threshold, the one

test can be seen as the energy of M samples. Therefore, in one experiment there are N/M = L tests (with

50% overlapping, there are approximately 2(N/M) tests). Total false alarm rate for WIBA method after

experiments is PFA = Pfa L, where PFA is total false alarm rate and Pfa = 0.01 is desired false alarm rate

in one test.

For fare comparison of the LAD ACC and WIBA methods, equal PFA must be used. Therefore, when

there are N=1024 samples and Pfa,desLAD = 10-6, it follows that LAD ACC 𝑃𝐹𝐴 = 𝑃𝑓𝑎,𝑑𝑒𝑠𝐿𝐴𝐷𝑁 =

10−61024~10−3. Because PFA = Pfa L, it follows that Pfa = PFA / L = 10-3 / L. For example, when M =

102, L = 20, Pfa = 10-3 / 20 = 5 .10-5 and, when M = 52 and L = 39, Pfa = 2.5.10-5.

Next, it is studied what is the performance loss for the WIBA method, if equal PFA for the WIBA and

LAD ACC methods is used instead of WIBA Pfa = 0.01 in an AWGN channel. In Figure 24, probability

of detection vs. SNR is presented in an AWGN channel. There is one signal with 10% BW, M = 102

and L = 20. Used threshold parameter values for WIBA method are Pfa = 0.01 and Pfa = 5.10-5

(corresponding LAD ACC PFA value), and for LAD ACC Pfa = 10-6. In Figure 25, the signal bandwidth

is 5%, M = 52, L = 39, and WIBA Pfa = 0.01 and Pfa = 2.5.0-5 (corresponding LAD ACC PFA value). It

can be noticed that when the WIBA and LAD ACC methods have equal PFA values, the performance

degradation is around 1 dB ~ 2 dB.

In Figure 26, probability of detection vs. distance [km] is presented in 5G-RANGE channel. There is

one signal with transmit power 53 dBm. Bandwidth of the signal is 2 and 6 MHz, and both the WIBA

and LAD ACC methods are used. For 2 MHz signal, M = 102, L = 20, WIBA Pfa = 0.01 and Pfa = 5.10-5

(corresponding LAD ACC PFA value). For 6 MHz signal, M = 264, M = 7, WIBA Pfa = 0.01 and Pfa =

1.4.10-4 (corresponding LAD ACC PFA value). When signal BW is 2 or 6 MHz and the WIBA and LAD

ACC methods have equal PFA values, signal can be detected using the WIBA method (final detection



probability Pd,i = 0.9) when Tx-Rx distance is at most 24 or11 km, respectively. That is, the detection

distance is 11 km and 4 km less when compared to results for WIBA Pfa = 0.01, respectively. In

cooperative sensing, when using OR rule which means that the signal is decided to be present when any

of the cooperative sensing nodes reports that signal is detected, and the number of cooperative nodes is

at least 5, Tx-Rx distances are 60 km and 48 km, respectively. It means that the detection distance is

then 0 and 12 km less when compared to results for WIBA Pfa = 0.01. In Figure 27, there is 5G-RANGE

channel, where transmit power is 30 dBm and signal BW is 6 MHz. When the WIBA and LAD ACC

methods have equal PFA values, signal can be detected (final detection probability Pd,i = 0.85 because

0.9 is not possible to be achieved) when Tx-Rx distance is at most 1 km. The detection distance is <1

km less when compared to results for WIBA Pfa = 0.01. In cooperative sensing, when using OR rule and

the number of nodes is at least 5, Tx-Rx distance is <4 km, and the detection distance is about 1 km less

when compared to results for WIBA Pfa = 0.01. In any case, the WIBA method still outperforms the

LAD ACC method. However, it depends on the situation which false alarm rate value is used. As Pfa =

0.01 gives better detection performance and, thus, larger detection distance, Pfa = 10-3 / L gives smaller

false alarm rate and, thus, less lost spectrum opportunities.

Figure 24: Probability of detection vs. SNR [dB] in AWGN channel case. The signal bandwidth is 10% (102

samples), M = 102 and L = 20.



Figure 25: Probability of detection vs. SNR [dB] in AWGN channel case. The signal bandwidth is 5% (52

samples), M = 52 and L = 39.

Figure 26: Probability of detection vs. distance [km] results. Transmit power of the signal to be detected is

53 dBm.



Figure 27: Probability of detection vs. distance [km] results. Transmit power of the signal to be detected is

30 dBm.

The performance of the WIBA method depends on the transmit power, SNR of the signal, length of the

detection window, and on the BW of the signal. The narrower the signal is, the wider the unutilized

spectrum (noise) is, and the better the WIBA method is able to operate. To ensure proper threshold

setting, unutilized frequency spectrum (UFS), defined as UFS = 1-BW, where BW is given as

percentage, must be large enough. For example, when signal has 40% BW, UFS=1-0.4=0.6.

Next we study the case of one signal with 75% BW. This corresponds one 6 MHz channel with effective

bandwidth 4.5 MHz (like DTV signal), which means that the signal has a 4.5 MHz BW, and 1.5 MHz

is used as guard band, as depicted in Figure 28A. In this case, UFS = 0.25. That is, 25% of the total

number of samples (256 samples) of the considered channel contains noise only. In Figure 29, Pd vs.

SNR is presented when M = 52, 102, 128 and 256 samples. M = 256 corresponds UFS. It can be noticed

that when using proper M, this kind of signal can be detected when SNR = -6 dB.

In the next case, signal is 37.5% BW. This corresponds to two 6 MHz channels with effective

bandwidths 2.4.5 MHz = 9 MHz, one signal with 4.5 MHz BW, and guard bands 2.1.5 MHz = 3 MHz,

as depicted in Figure 28B. In Figure 30, Pd vs. SNR is presented when M = 52, 128, 256 and 384. The

case were M = 384 corresponds to the signal BW. It can be noticed that when using proper M, this kind

of signal can be detected when SNR = -12 dB.

Figure 28: A: One 6 MHz channel with effective BW 4.5 MHz and one signal with 4.5 MHz BW. B: Two 6

MHz channels with effective BWs 9 MHz and one signal with 4.5 MHz BW.



Figure 29: Pd vs. SNR results when signal BW is 75% (768 samples).

Figure 30: Pd vs. SNR results when signal BW is 37.5% (384 samples).



5.1.2.3.3 Cooperative sensing case

Let us assume that individual detection probability for one node is Pd,i and there are n cooperating nodes

which perform sensing and provide results to fusion center (at BS). Let us also assume that all the nodes

have same individual Pd,i, i.e., Pd,1 = Pd,2 = Pd,3 = … = Pd,n. Final cooperative detection probability Pd

depends on the number of cooperating nodes (n) and also on the used decision rule. We assume a hard

decision in which each node decides if the signal is present or not, and sends a one-bit information about

the decision to the fusion center.

OR rule means that the signal is decided to be present when any of the cooperative sensing nodes

reports that signal is detected. In this case, the final detection probability at the fusion center is [65]

𝑃d = 1 −∏ (1 − 𝑃d,𝑖𝑛𝑖=1 ) = 1 − (1 − 𝑃d,𝑖)

𝑛. (8)

Final cooperative false alarm probability is 𝑃f = 1 −∏ (1 − 𝑃f,𝑖𝑛𝑖=1 ) = 1 − (1 − 𝑃f,𝑖)

𝑛 , where Pf,i is

individual false alarm probability.

Results for Pd,i = 0.2 ~ 0.9 and n = 3 ~ 6, 10 and 15 are presented in Table 7. It can be seen that the

accepted final cooperative detection probability Pd = 0.9 is achieved when individual detection

probability Pd,i is at least 0.4 and n ≥ 5, i.e., cooperative detection increases detection probability

remarkably in this case.

AND rule means that the signal is decided to be present only when all the cooperative nodes report

that a signal is present. This method improves spectrum access opportunities but decreases incumbent

protection. Then, the final detection probability at the fusion center is [65] 𝑃d = ∏ 𝑃d,𝑖𝑛𝑖=1 . Final

cooperative false alarm probability is 𝑃f = ∏ 𝑃f,𝑖𝑛𝑖=1 . Results for Pd,i = 0.8 ~ 0.95 and n = 2 ~ 6 are

presented in Table 8. It can be seen that the accepted final cooperative detection probability Pd = 0.9 is

achieved only if Pd,i ≥ 0.95 and n ≤ 2.

k-out-of-n rule means that the signal is decided to be present in the channel if k users out of n users

report that there is a signal at the detector input. In this case, the final detection probability at the fusion

center is [66] [67] 𝑃d = ∑ (𝑛𝑖)𝑛

𝑖=𝑘 𝑃d,𝑖𝑘 (1 − 𝑃d,𝑖)

𝑛−𝑖. When k = 1, this corresponds to OR rule and when

k = n, this corresponds AND rule. Final cooperative detection probability lies between OR and AND

rule probabilities. In [67] the authors show that 𝑃f = ∑ (𝑛𝑖)𝑛

𝑖=𝑘 𝑃f,𝑖𝑘 (1 − 𝑃f,𝑖)

𝑛−𝑖. The larger the number

of cooperative users n is, the better is the sensing performance. Notice that Pf increases with n when

assuming that Pf,i is fixed. However, as n increases, higher is the reporting time and the overhead, which

also increases the energy consumption.

Table 7: Final cooperative Pd when using OR rule. Pd,i n = 3 n = 4 n = 5 n = 6 n = 10 n = 15

0.9 0.9990 0.9999 1.0 1.0 1.0 1.0

0.8 0.9920 0.9984 0.997 0.999 1.0 1.0

0.7 0.9730 0.9919 0.9976 0.993 1.0 1.0

0.6 0.9360 0.9744 0.9898 0.9959 0.9999 1.0

0.5 0.8750 0.9375 0.9688 0.9844 0.9990 1.0

0.4 0.7870 0.8704 0.9222 0.9533 0.9940 0.9995

0.3 0.6570 0.7599 0.8319 0.8842 0.9718 0.9953

0.2 0.4880 0.5904 0.6723 0.7379 0.8926 0.9648

Table 8: Final cooperative detection probability when using AND rule (the signal is present only if all

cooperative sensing nodes reports that there is a signal).

Pd,i n=2 n=3 n=5 n=6

0.95 0.9025 0.8574 0.7738 0.7351

0.94 0.8836 0.8306 0.7339 0.6899

0.9 0.81 0.7290 0.5905 0.5314

0.8 0.64 0.5120 0.3277 0.2621



When combining OR rule (Table 7; n ≥ 5 and Pd,i ≥ 0.4 to achieve final detection probability Pd ≥ 0.9)

and single sensing results using WIBA and LAD ACC methods (Figure 19-Figure 23), we achieve the

results presented in Table 9. Transmit power values were 53, 46, 30, 20 or 10 dBm and signal bandwidth

was 2, 4 or 6 MHz. Cooperative sensing was used with at least 5 nodes and using OR rule. For example,

when transmit power is 53 dBm, signal BW is 4 MHz and WIBA method is used, signal can be detected

(final cooperation detection probability Pd = 0.9) when Tx-Rx distance is 60 km (second line in Table

8).

Table 9: For different transmit power values [dBm] and signal bandwidths [MHz], the maximum distance

between Tx and Rx so that final cooperative detection probability Pd is at least 0.9. OR rule is used. Number

of cooperating nodes n ≥ 5 and Pd,i ≥ 0.4.

Transmit power

[dBm]

Signal bandwidth

[MHz]


WIBA method


LAD ACC method

53

2 60 km <32 km

4 60 km <14 km

6 60 km <7 km

46

2 60 km <16 km

4 <45 km <6 km

6 <30 km <3 km

30

2 <12 km <2 km

4 <8 km <1 km

6 <5 km -

20

2 <4 km -

4 <3 km -

6 <2 km -

10 2 <1 km -

4 - -

Next, k-out-of-n rule is discussed. In Figure 31, final cooperative detection probability Pd vs. individual

detection probability Pd,i is presented. AND, OR and k-out-of-n rules are presented when n = 10 and k =

1, …, 10. The smaller k is, the larger the final cooperative detection probability Pd is, and for smaller

individual detection probability Pd,i cooperative detection probability of least 0.9 is achieved. In

addition, higher k requires higher individual detection probability Pd,i to get final cooperative Pd ≥ 0.9.

For example:

k = 2, final cooperative Pd ≥ 0.9 is reached when Pd,i ≥ 0.4;




k = 6, final cooperative Pd ≥ 0.9 is reached when Pd,i ≥ 0.8.



Figure 31: OR, AND and k-out-of-n rules when n=10 and k=1~10.

In Figure 32, final cooperative detection probability Pd vs. individual detection probability Pd,i is

presented. AND, OR and k-out-of-n rules are presented when n = 5 and k = 1,…,5. The smaller k is, the

larger Pd is, and for smaller Pd,i, smaller must be k for achieving Pd > 0.9.

Figure 32: Performance of the OR, AND and k-out-of-n rules when n = 5 and k = 1 ~ 5.

Let us assume that n = 5 and k = 3. In this case, the final cooperative Pd ≥ 0.9 is reached when Pd,i ≥ 0.8

(Figure 32). When combining this to single sensing results using WIBA and LAD ACC methods (Figure

19 to Figure 23), we get results presented in Table 10. This table presents the maximum distance between

Tx and Rx where Pd ≥ 0.9 is achieved when 3 out of 5 rule is used, assuming different transmit powers.



Table 10: Results for the maximum distance between BS and UE for final cooperative detection probability

requirement, Pd at least 0.9. 3-out-of-5 rule is used and Pd,i ≥ 0.8.

Transmit power

[dBm]

Signal bandwidth

[MHz]


WIBA method


LAD ACC method

53 dBm

2 MHz <50 km <10 km

4 MHz <33 km <4 km

6 MHz <22 km <2 km

46 dBm

2 MHz <24 km <4 km

4 MHz <15 km <2 km

6 MHz <10 km -

30 dBm

2 MHz <4 km -

4 MHz <2.4 km -

6 MHz <1.5 km -

20 dBm

2 MHz <1 km -

4 MHz - -

6 MHz - -

Table 11 and Table 8 present cooperative false alarm probabilities using OR and AND rules,

respectively, for different values of individual Pf,i and n. It can be noticed that using OR rule, final

cooperative false alarm probability rises when n rises. For example, when individual Pf,i is 0.1 and n=4,

final cooperative false alarm probability is 0.3439. This means that individual Pf,i should be small when

using cooperative sensing with OR rule.

Table 11: Final cooperative false alarm probability when using OR rule.

Pf,i n = 3 n = 4 n = 5 n = 6 n = 10 n = 15

0.3 0.6570 0.7599 0.8319 0.8824 0.9718 0.9953

0.25 0.5781 0.6836 0.7627 0.8220 0.9437 0.9866

0.2 0.4880 0.5904 0.6723 0.7379 0.8926 0.9648

0.15 0.3859 0.4780 0.5563 0.6229 0.8031 0.9126

0.1 0.2710 0.3439 0.4095 0.4686 0.6513 0.7941

0.05 0.1426 0.1855 0.2262 0.2649 0.4013 0.5367

0.01 0.0297 0.0394 0.0490 0.0585 0.0956 0.1399

Table 12: Final cooperative false alarm probability when using AND rule.

Pf,i n = 2 n = 3 n = 5 n = 6

0.3 0. 0900 0. 0270 0. 0024 0.00072

0.2 0. 0400 0. 0080 0.00032 0.00006

As an example, single and cooperative sensing results from Table 6, Table 9 and Table 10 are compared

when transmit signal power is 53 dBm, signal bandwidth is 2, 4 and 6 MHz, and WIBA method was

used. Results are presented in Figure 33, showing the distance that single sensing, cooperative OR

sensing and cooperative 3 out of 5 sensing methods are able to find the signals, assuming a transmit

power of 53 dBm and signal bandwidth of 2, 4 or 6 MHz. It can be noticed that cooperative OR sensing

gives best results, as expected. It can also be seen that the narrower the signal can be detected at longer

distances. Results from Figure 33 illustrate clearly that cooperative sensing gain increases with

bandwidth of the signal to be detected.



Figure 33: Illustration of the distances where single sensing and cooperative sensing using OR and 3 out of

5 rules can detect the signal. Transmit power is 53 dBm and signal bandwidth is 2,4 and 6 MHz. WIBA

method was used in this case.

5.1.2.4 Projection of results with respect to use cases

Figure 34 illustrates a generic spectrum sensing scenario for different use cases defined in [62]. Core

use cases includes wireless backhaul, voice and data connectivity, agribusiness & smart farming and

remote health care. In Figure 34, use case 1 includes 5G-RANGE BS connected to wireless backhaul,

and there are several UEs (blue boxes) in the covered area. Use case 2 can be considered as a remote

healthcare, or voice and data connectivity, scenario at rural village where there are also programme-

making and special events (PMSE) signals (like wireless microphone) signals present that affect some

UEs (red boxes) in that cell. Use case 3 can be considered as smart farm deployed using 5G-RANGE

technology. As Figure 34 illustrates, all cells are affected by DTV signals (official and/or pirate DTV).

For example, typical primary signals in Brazilian TV broadcasts use 12 dBm ~ 49 dBm transmit power,

depending on class (A/B/C/E) and channels (high VHF/UHF). There are also much larger power TV

broadcasts (80 kW in class E), i.e., they can be detected easier by spectrum sensing.

In the wireless backhaul use case (use case 1), the goal is to use existing infrastructure, e.g. TV broadcast

towers for 5G-RANGE backhaul BS installation to create LOS links to remote locations. In this case,

rural communities, such as villages, industrial areas (e.g. mines or oil fields), tourist venues etc. can

benefit from long-range communication. LOS link with 50 km distance uses vacant VHF/UHF bands.

The link is between 5G-RANGE BS that is located in an existing TV-tower, and a local small cell BS



located at the rural location. Single local BS (5G-RANGE BS in Figure 34) can provide local high-

quality connections for UEs that are in the cell areas.

In remote health care (e-Health) use case (use case 2), health and/or medical assistance and remote

monitoring is considered, including video conference, real-time information and video image etc.

Therein, broadband communication and acceptable latency are assumed, so real-time assistance can be

provided. In this cell area, it is illustrated that there can also be PMSE signals which are typically from

professional wireless microphones. From the spectrum sensing point of view, the voice and data

connectivity for remote area includes same kind of challenges, i.e., there may be protected PMSE

devices that are not allowed to be interfered by 5G-RANGE devices. Therefore, use case 2 represents

here the both scenarios.

Smart farm (use case 3) includes collection and analysis of data, crop monitoring, production

traceability, cattle, remote maintenance and diagnosis, etc. IoT devices from clusters of sensors,

actuators, wearables etc. can connect to the 5G-RANGE network through some specific UE that is used

as a gateway. In some cases, (harvest fronts, garage) there might be a high concentration of users and,

in this case, mobile BS can be installed also in vehicles.

In all the use cases, there can be several types of signals that the LAD ACC and WIBA methods are able

to detect without knowing any a priory information about the signal. IoT signals are usually narrowband

(even 200 kHz) and can be also above the noise level. Wireless microphone signals have 200 kHz BW

and most regulators establish that they should be detected above -107 dBm level with Pd > 0.9.

Previously, it was found that LAD ACC method is able to find wireless microphone signals with strength

about -107 dBm [68], as required in this situation. Because the WIBA method outperforms the LAD

ACC method, the WIBA method is also able to satisfy this requirement. In the LAD ACC and WIBA

simulations presented in this document, it was noticed that narrowband signals (5% ~ 25% bandwidth,

corresponding, e.g., to IoT signals) are easily detected, even at SNR = -10 dB when using the WIBA

method (AWGN channel with multipath/multiple signals). It is also important to detect other 5G-

RANGE signals and / or similar type of signals transmitted by other systems. Based on the results for

5G-RANGE channel model and assuming transmit power between 10 dBm and 53 dBm, it can be said

that when using cooperative sensing, detection range for the WIBA method can be even more than 60

km, depending on the parameters. For example, if there are 5 cooperative UEs and 3 out of 5 sensing

results are considered, a 2 MHz signal (8.6% BW) can be detected as far as 50 km, if transmit power is

53 dBm. Cooperative sensing is important to all use cases due to large cell area (variations in signal

propagation) and / or mobility of devices.

In all the use cases, there can be also primary and/or pirate DTV signals present. Simulations for the

WIBA method show that this method can detect a 6 MHz signal, corresponding to a DTV signal, even

when signal covers 75% of the BW, which is the case that includes one DTV band + its guard bands.

However, detection performance is not very good, being necessary to have SNR > -6 dB to achieve Pd

= 0.9. Based on 5G-RANGE channel model, it can be said that a 6 MHz signal (25.6% BW

corresponding 1 signal in 4 bands) with 53 dBm transmit power can be detected at 15 km from its source

for single sensing case. This distance increases to 22 km when 3 out of 5 cooperative sensing is

employed, and to 60 km when a cooperative sensing with the OR rule is used.

The performance of the LAD ACC and WIBA method depend on the parameters like signal relative

bandwidth, transmit power, length of the WIBA detection window, and used cooperative sensing

scheme. The wider the analyzed bandwidth is, the narrower the signal is with respect to the overall

bandwidth. For example, 6 MHz signal is 100% wide if the analyzed BW is 6 MHz. However, the same

6 MHz signal present only 25% BW if the analyzed BW is 24 MHz. Notice that, in order to ensure

proper threshold setting, unutilized spectrum UFS must be large. For example, in case of the LAD ACC

method, UFS > 0.1, and it is recommended that UFS > 0.5. It means that 10% up to 50% of the bandwidth

has to be signal free. In the WIBA method, UFS depends on the length of the detection window. For

large UFS, the performance of the detection threshold increases. In general, it can be stated that the

sensing performance is improved as the signal BW decreases compared with the analyzed BW, and the

signal power increases.



Figure 34: Generic spectrum sensing scenario of 5G-RANGE use cases.

5.2 The GRCR and Eigenvalue-based detection

5.2.1 Method descriptions

Consider a cooperative spectrum sensing with data fusion as illustrated in Figure 35. In this scenario,

the spectrum sensing of the signals transmitted by P primary users is performed individually from M

SUs. Therefore, at the end of the range of sensing, the sample matrix Y is available at the FC as follows

𝐘 = {𝐕 ∶ 𝐻0𝐇𝐗 + 𝐕 ∶ 𝐻1

, (9)

where the samples of the transmitted signals are arranged in a matrix XℂP×N and the samples of the

AWGN noise with zero mean and variance σ2 are arranged in a matrix VℂM×N. The channel matrix

HℂM×P contains hi,j elements that represent the channel complex gain between the jth PU and the

ith SU, where i = 1,2,…,M and j = 1,2,…,P, which implies in a flat channel.

Alternatively, it can be assumed that a matrix Y is formed in each SU, e.g., if each SU is equipped with

J antennas. In the latter case, the matrix Y will have J × N dimensions and each SU may make its own

local decision to implement the cooperative sensing with decision fusion.

Figure 35. Cooperative spectrum sensing scenario for analysis of techniques based on eigenvalues.



In a centralized cooperative spectrum sensing with eigenvalues and sample fusion, the white spaces are

detected based on the eigenvalues of the covariance matrix of the signal received by the M secondary

users, which is estimated by

𝐑𝐘 =1

𝑁𝐘𝐘† , (10)

where the operator (·)† denotes the Hermitian operation. The eigenvalues {λ1 > λ2 > λ3 > … > λM} of RY

(without loss of generality, sorted in decreasing order) are then computed and, considering

P = 1 primary user, the GLRT, MMED and MED test statistics are respectively calculated at the FC

according to [15]

𝑇GRLT = 𝜆1

1𝑀∑ 𝜆𝑖𝑀𝑖=1

,

𝑇MMED =𝜆1𝜆𝑀 ,

𝑇MED =𝜆1𝜎2 .

(11)

If it is desired to implement centralized cooperative sensing with decision fusion, the above-mentioned

test statistic is formed in each SU. To form one of the test statistics of local sensing by eigenvalues, the

matrix Y is constructed in each SU, as previously described, through samples taken by multiple

antennas.

According to the [69], the Gershgorin circle theorem [70] states that the eigenvalues λi of matrix R are

located in the union of the M disks as follows

|𝜆 − 𝑟𝑖𝑖| ≤∑|𝑟𝑖𝑗|

𝑗≠𝑖

, (12)

where rij is the element in the ith row and jth column of R. The value Ci = rii is the ith center and the

quantity 𝑅𝑖 = ∑ |𝑟𝑖𝑗|𝑗≠𝑖 is the corresponding radius of the Gershgorin disk denoted as D(Ci,Ri). In other

words, the Gershgorin circle theorem identifies a region in the complex plane containing the eigenvalues

of a complex square matrix. Since every covariance matrix is positive semi-definite, then Ci ℝ+,

meaning that the Gershgorin centers are located in the non-negative part of the real axis.

In [69] the author has observed that the ratio between the sum of the radius of the Gershgorin disk (Ri)

and the sum of the Gershgorin centers (Ci) of a matrix R can be used to distinguish the situations of

presence (H1) and absence (H0) of a PU transmission signal. Therefore, the new test statistic proposed

in [69], denoted as Gershgorin radii and centers ratio (GRCR), is defined as

𝑇GRCR =∑ 𝑅𝑖𝑀𝑖=1

∑ 𝐶𝑖𝑀𝑖=1

. (13)

Observe that this test statistic is a full-blind technique, which it does not need to know the power of the

noise. In addition, it is simpler than the tests statistic that needs to calculate the eigenvalues from a

matrix R, as GLRT and MMED.

Figure 36 illustrates the Gershgorin disks under different hypotheses H0 and H1 for a sample of the

covariance matrix R assuming a scenario with P = 1, M = 5, N = 5000 and all SUs having the same

SNR = –13 dB of the sensing signal. It is possible to note that the behavior of the Gershgorin disks is

different for hypotheses H0 and H1. This analysis corroborates to show that the GRCR test statistic can

serve as a spectrum sensing technique. It is worth to mention that the value for the test statistic is

TGRCR=0.0211 for H0 and TGRCR = 0.0916 for H1 (four times higher) for the results shown in Figure 36.



Figure 36. Gershgorin disks under H0 (a) and H1 (b) hypotheses for a sample covariance matrix R.

The author in [69] draws attention to the fact that there is the possibility of taking the statistical test

using only the numerator in (13), ∑(Ri). However, the division by ∑(Ci) brings the CFAR (Constant

False Alarm Rate) property to the test statistics in (13), desirable in some systems as radar operation.

5.2.2 Performance Analysis

Subsection 5.2.1 presented the main spectrum sensing techniques based on eigenvalues, GLRT, MMED

and MED. Firstly, here it will be made a performance comparison among these techniques. The

performance comparison is made in terms of ROC (Receiver Operation Characteristics) curves. Figure

37 presents a preliminary ROC performance assessment comparing the techniques GLRT, MMED,

MED and the energy detector. The ED technique was chosen in order to provide a comparison reference.

Each value on the ROC curves was obtained from 100,000 Monte Carlo events. Each event corresponds

to sending a zero-mean white Gaussian-distributed PU signal through M independent AWGN channels

to the SUs. All SUs sent the collected data to the FC which form the received signal matrix Y, as

described in (9). For a first approach, it is considered that the CCC between SUs and FC is only affected

by AWGN. A uniform noise matrix V is formed by i.i.d Gaussian noise samples with zero mean and

variance σ2 = (10 (SNR/10))–1.

From Y, the FC can compute the test statistic of each analyzed technique, using (11). To take a decision,

the test statistic is compared with a decision threshold ξ. If the computed test statistic is greater than ξ,

the hypothesis H1 is declared, on the contrary, the hypothesis H0 is declared. The decision threshold was

varied from the minimum to the maximum values of each corresponding test statistic in 40 equally-

spaced values. The global decision at the FC is used for computing false alarm and detection rates, which

are the estimates of the associated probabilities. This procedure is repeated by varying ξ, so that the

ROC curves are traced-out.

The parameters used to generate the result presented in Figure 37 are M = 6 SUs, N = 50 and, finally, a

single PU signal with SNR = −10 dB.

The MED test has presented the best performance followed by the tests ED, GLRT and MMED.

However, both MED and ED need to know the power noise in each SU, while GLRT and MMED do

not need this knowledge. It is worth to mention that the presented results are not general. Different

parameters and scenarios can change the absolute and relative performances of any analyzed technique.



Figure 37. ROC performance for spectrum sensing techniques MED, ED, GLRT and MMED.

Figure 38. Performance of MED, GLRT, MMED and GRCR techniques under non-uniform and dynamical

noise.

Figure 38 shows the ROC performance curves for all techniques considered in the Subsection 5.2.1 and

compare them with the GRCR. For this comparison, a more realistic scenario was assumed, where the

SUs have different SNR of the sensing PU signal. The same consideration is made in [69]. Therefore,

the simulation have been made in the scenario under non-uniform noise, with noise variances draw from

a uniform distribution in each sensing round, that is σi2 ~ U [0.05 σ2

avg, 1.95 σ2avg], with σ2

avg = 1.78 being

the average noise variance given by, 𝜎avg2 =

1

𝑀∑ 𝜎𝑖

2𝑀𝑖=1 . In addition, it has been adopted M = 6, N = 10,

SNR = – 3 dB, single i.i.d Gaussian PU signal, and uniform received powers. It can be seen Figure 38

that the MED has the best ROC performance, followed by the GRCR, GLRT and MMED, in this order.



However, MED is a non-blind technique and, therefore, needs to know the noise power in each SU. So,

GRCR is the best full-blind method among the analyzed techniques.

As a conclusion, GRCR can be seen as a simple test statistic for cooperative or multi-antenna spectrum

sensing. Although simple and full-blind, GRCR is robust against dynamical noise and received signal

powers, exhibits CFAR property and outperforms the most commons full-blind detectors in the

literature, GLRT and MMED, under non-uniform and dynamical noise variances.

5.3 The CSS scheme with high bandwidth and energy efficiency

As mentioned above and illustrated in Figure 35 in the cooperative spectrum sensing systems there are

M secondary users that perform independent spectrum sensing of primary channel occupancy. The

sensing measurements or local decisions of SUs are sent to the FC, where the global decision of channel

occupancy is performed by combining the information provided by the SUs. In traditional CSS networks

there is a dedicated common control channel for each SU to transmit its information and, normally, are

used some multiple access techniques, such as time division or frequency division multiple access.

Although the increase in the number of SUs in the CSS scheme contributes to the global decision

accuracy, it consumes more resources in the CCC, such as bandwidth and energy. Some researchers

have been proposed some techniques that aim the reduction of energy consumption in CSS networks.

Normally, we can divide these techniques into three distinct groups:

i) the ones that reduce the number of sensing users [71], [72];

ii) techniques that reduce the sensing time [73], [74], [75] and;

iii) techniques that reduce the amount of reported data to the fusion center [48], [76], [66].

Also, some works introduce techniques to reduce the consumption of bandwidth in the report channel

[77], [78], [79], [80]. Specifically, in [80], the author introduces a new CSS scheme where all M

cognitive radios are able to report their respective local hard decision on the same carrier frequency, at

the same time. Therefore, after the report phase, the received signal in the FC will be equal to the non-

coherent summation of each reported local decision. According to this scenario, a new fusion center rule

is also provided in [80], allowing the FC making an accurate global decision about the channel

occupancy. In [81], the authors propose a cooperative spectrum sensing based on the combination of the

simultaneous local decision transmission with pre-distortion and censoring of some SUs, in order to

achieve a high bandwidth and energy efficiency in the CSS network. This section presents the main

principles and results presented in [81], which is a state-of-art approach for high-efficiency cooperative

spectrum sensing systems.

In [80], the M secondary users made a local spectrum sensing that results in a local binary decision

where, mk = 0 represents the hypothesis H0 and mk = 1 represents the hypothesis H1. Later, the local

decision is mapped into a BPSK symbol at each SU, by 𝑠𝑘 = (2𝑚𝑘 − 1)√𝐴, where A represents the

energy per transmitted bit, Eb. Assuming that the CCC between the k-th SU and FC has a complex gain

hk, the received signal at the FC is given by the incoherently summation of the all BPSK transmitted

symbols plus the noise, as follows

𝑟 = ∑ℎ𝑘𝑠𝑘 + 𝑢s ,

𝑀

𝑘=1

(14)

where us is the zero-mean AWGN sample with variance σ2.

It is assumed in [80] that the FC knows the complex CCC gain vector h = [h1,h2, … ,hM]T, where (·)T

denotes vector transposition. It allows for the classification of the noiseless received samples into the

sets D0 and D1, which are the sets associated to the local decision vector s = [s1,s2, … ,sM]T that would

lead to the choice of H0 and H1, respectively, under the k-out-of-m rule. In mathematical terms,



𝐷0 = {𝐬|∑ 𝑚𝑘 < 𝐾𝑀

𝑘=1} ; 𝐷1 = {𝐬|∑ 𝑚𝑘 ≥ 𝐾

𝑀

𝑘=1} . (15)

The global decision rule proposed in [80] determines that FC will decide in favor of H1 if

∑ exp {−|𝑟 − 𝐡T𝐬|2

2𝜎2}

𝐬∈𝐷1

≥ ∑ exp {−|𝑟 − 𝐡T𝐬|2

2𝜎2}

𝐬∈𝐷0

. (16)

Otherwise, it will decide in favor of H0.

The necessary channel gains are obtained from a sounding signal sent to the FC by each SU, in the

uplink direction. It is important to remember that, in the case of [80], estimating the control channel gain

in the FC by means of pilot signals in the uplink direction requires a great amount of time or frequency

resources, since the pilot signals of each SU must be orthogonal to the signals of the other SUs in the

network.

To solve the problem of requesting many resources in the process of the channel estimating, in [82], the

task of channel estimation is shifted from uplink to the downlink. In other words, instead of each SU

transmitting individual sounding signals to the FC, the fusion center broadcasts this signal to the SU.

The channel estimates are made by each SU and used to pre-compensate the channel phase rotation and

to partially compensate the reporting channel gain. This new concept can reduce the system

implementation complexity when compared with the original one and achieve higher performance gains

over fading CCC.

The kth CCC complex gain is defined as ℎ𝑘 = 𝛼𝑘𝑒−𝑗𝜃𝑘 , where αk and ϴk are the absolute value and the

phase of the kth CCC, respectively. Before reporting the local decision, each SU compensates the

channel phase rotation and gain. However, when αk is too small, the gain compensation process can lead

the amplitude of the transmitted signal to very high values, thus significantly increasing the relationship

between the maximal and mean amplitude of the transmitted signal. Normally, this relationship is given

in terms of the peak-to-average power ratio (PAPR). Therefore, to limit the signal PAPR, it is defined a

transmitter clipping threshold C, that controls the maximal amplitude of the transmitted signals. Without

this clipping, high PAPR levels would impose a strong limitation on the design of the power amplifiers

in the SU. Also, the energy consumption could reach very high values.

As the result of phase rotation compensation and clipping, a symbol transmitted by the kth SU can be

written as

𝑠𝑘 = (2𝑚𝑘 − 1)min(1

𝛼𝑘, 𝐶) 𝑒𝑗𝜃𝑘√𝐴 , (17)

where A defines the mean amplitude per transmitted symbol.

Since all M secondary users must transmit their local decision at the same time in the same carrier

frequency, the receiver signal at the FC is given by

𝑟 = ∑ℎ𝑘𝑠𝑘 + 𝑢s =

𝑀

𝑘=1

∑(2𝑚𝑘 − 1)𝛼𝑘min (1

𝛼𝑘, 𝐶) √𝐴 + 𝑢s

𝑀

𝑘=1

, (18)

Observe that, because the phase compensation at the SU, the received signal samples are real-valued.

When the clipping value C > 1/αk, the noiseless received symbol at the FC will follow a Binomial

distribution with M + 1 values that can be represented in two different sets. The points that represents

the H1 event are { (2𝐾 −𝑀)√𝐴 ,···,𝑀√𝐴 } while, the points that represents the H0 event are

{−𝑀√𝐴,···,(2𝐾 −𝑀 − 2)√𝐴}.

As the value of C is reduced, in order to limit the PAPR of transmitted signals, the clipping effect starts

to be observed in received signal r. This effect can be modeled as a continuous component in the

probability density function (PDF) of r. This continuous component is derived by a scaled two-sided



truncated Rayleigh random variable with values occurring according with the probability of occurrence

of clipping [82].

This new approach simplifies the analysis of the decision regions at the FC, thus allowing the FC to

make the global decision based on a simple comparison of the received sample with a threshold. In other

words, the FC will decide in favor of H1 if the received signal sample r is greater than the threshold λFC

and will decide in favor of H0 otherwise. As shown in [82], the global decision threshold in the FC is

given by

𝜆FC = (2𝐾 −𝑀 − 1)√𝐴 , (19)

where K and M are given by the K-out-of-M fusion rule.

Based on the new threshold comparison rule, equations for the global detection and false alarm

probabilities of the pre-compensated scheme are presented in [82] and, it has been showed that this new

fusion and decision scheme can produce huge performance improvements over the original scheme

proposed in [80]. Despite these advantages, the pre-compensated scheme still preserves the negative

characteristic of the CSS systems regarding high energy consumption.

Aiming to promote a reduction of energy consumption of the scheme presented in [82], in [81] is

proposed a new pre-compensated CSS system with censored reporting local decisions transmissions.

The main difference between [82] and the new energy-efficient proposed scheme is in the reporting

phase [81]. In [82], all M SUs must convey to the FC their local decisions mk, informing the presence or

absence of the PU. The censored reporting rule defines that only SUs that decides by the existence of

PU can transmit their local decision to the FC. The remaining of SUs does not transmit, saving energy

during this specific reporting period.

It is important to notice that the new scheme also uses the concept of pre-compensation of the report

channel. Therefore, assuming that ℎ𝑘 = 𝛼𝑘𝑒−𝑗𝜃𝑘 is known by the kth SU, after local spectrum sensing,

each SU has its hard local decision mk that is modulated into a symbol with total compensation of phase

and partially compensation of the gain. Assuming the censoring report rule, the signal transmitted by

each SU is given by

𝑠𝑘 = 𝑚𝑘min (1

𝛼𝑘, 𝐶) 𝑒𝑗𝜃𝑘√𝐴 , (20)

and the received sample at the FC is then

𝑟 = ∑𝑚𝑘𝛼𝑘

𝑀

𝑘=1

min(1

𝛼𝑘, 𝐶)√𝐴 + 𝑢s . (21)

From (21) it is possible to note that the received signal samples are real-valued. In addition, even under

the influence of the clipping, it is possible to note that the majority of the noiseless received samples has

value given by 𝑟𝑖 = 𝑖√𝐴 for 𝑖 = 1,2, … ,𝑀. Under the K-out-of-M rule, the indexes i < K correspond to

the global choice in favor of H0, while the indexes i ≥ K correspond to the global choice for H1. It

suggests a decision threshold λFC somewhere in-between (𝐾 − 1)√𝐴 and 𝐾√𝐴, meaning that the FC

will choose for H1 if r ≥ λFC and will choose for H0 otherwise. In [81], a simplified sub-optimal threshold

is proposed for the most widely used K-out-of-M rules, as follows

𝜆FC =

{

√𝐴 ∶ for 𝐾 = 1

(𝑀 − 1)√𝐴 ∶ for 𝐾 = 𝑀

(⌈𝑀

2⌉ −

1

2)√𝐴 ∶ for 𝐾 = ⌈

𝑀

2⌉

. (22)

For the purpose of energy consumption analysis, the SU operation time-frame can be divided into three

steps:



i) local sensing;

ii) report and;

iii) decision and opportunistic data transmission.

In the first step, all SUs perform spectrum sensing and decide on the presence or absence of the PU

signal. In the second step, the SUs transmit their decisions to the FC. In the third step, the FC makes the

global decision about the state of the PU transmitter and, if the channel is considered idle, the SUs are

allowed to start their opportunistic communication. The SUs remain silent if the channel is assumed to

be occupied by the PU.

One of the main points of the techniques proposed in [81] and [82] is the coherent sum of the symbols

received by the FC. The sensing performances presented and analyzed in [81] and [82], and revisited in

this document, depicting a scenario in which there is the perfect temporal alignment between the M SUs

and, therefore, all the signals received in the FC are coherently added. In the context of the 5G-RANGE,

it is planned to use a dedicated and licensed common control channel at the frequency of 450 MHz or

700 MHz that provides the use of time alignment techniques similar to those already used by LTE [83],

which will permit the signals of different SUs to always reach the FC in synchronism.

It is important to mention that the uplink time alignment techniques used in LTE allows for aligning

UEs that are up to 100 km away from the radio base [83], making these techniques perfectly suitable for

the 5G-RANGE scenario, in which cells up to 50 km are expected.

5.3.1 Performance Analysis

Figure 39 illustrates the ROC performances of the efficient schemes introduced in Section 5.2.1. The

scenario shown in Figure 35 with P = 1 was considered. In this scenario, it is assumed that FC is the

5G-RANGE BS. A licensed CCC can be used by all SUs to report their local decisions at the same time

to the FC. It is assumed that, in this channel, there is a temporal alignment process, as in the LTE, which

allows the signals of the SUs to be added coherently in the FC. It is important to mention that the ROC

performance is affected by all parameters of different manners. For the results shown in Figure 12, the

following parameters were chosen arbitrarily: M = 5, K = 1 and K = 3, C = 3, SNR of the primary signal

equal to –5 dB and the SNR of the common control channel equal to 5 dB. AWGN channels were

considered in the local spectrum sensing, between PU and SUs. The CCC between SUs and FC follows

a flat and slow Rayleigh fading profile.

The scheme that uses orthogonal CCC is named as Traditional CSS, the scheme proposed in [80] is

named as Reference, the scheme proposed in [82] is named as Pre-comp. while the scheme in [81] is

named as Censored.

Figure 39. ROC performance of the efficient schemes.



For K = 1, the pre-comp. scheme presents the best ROC performance, followed by censored, traditional

and reference, in this sequence. For K = 3, all schemes have an improvement in the ROC performances.

The traditional scheme outperforms the censored one. However, we can see that the pre-comp.,

traditional and censored schemes have almost equal ROC performances. It is worth to mention that the

approaches in [80], [82] and [81] present a higher bandwidth efficiency than traditional schemes.

In addition to ROC performance, an analysis should be made of the energy efficiency of the analyzed

schemes, since ROC performance is reached for different values of energy consumption for the different

presented schemes.

The energy efficiency will be evaluated according to

𝜂 =𝐸css𝐷 , (23)

where Ecss is the total energy consumption of the secondary network and D is the average number of fair

opportunistic transmitted bit [81], which is given by

𝐷 = 𝑃𝐻0(1 − 𝑃fa)𝑅b𝑇t , (24)

where Tt is the opportunistic transmission interval, and that during this interval, the bit rate achieved by

the SUs is Rb bit/s. The energy efficiency is measured in joules per bit and more energy-efficient

secondary network is the one that achieves lower values of η.

Figure 40 shows the energy efficiency of the schemes introduced in Section 5.3. Parameters have been

configured in order to achieve the best energy efficiency in each scheme [81]. Therefore, for the

traditional and reference schemes were set M = 5 and K = 3. For pre-comp scheme, M = 5, K = 3 and

C = 0.5, while for censored scheme were set the parameters M = 5, K = 1 and C = 0.5. The SNR of the

primary signal is equal to –5 dB and the SRN of the common control channel is equal to 5 dB.

It can be concluded from Figure 40that the energy efficiency of the censored scheme outperforms the

other three. For instance, again assuming Pfa = 0.1, the energy consumed per RbTt transmitted bits is

η = 1.3 joules for the censored scheme, being η = 3 joules for the pre-comp. scheme. The traditional and

reference scheme of [80] have a quite lower energy efficiency, reaching to η = 12 joules per RbTt fair

opportunistic transmitted bits in its best configuration. Therefore, for the best energy efficiency

configurations, the censored scheme achieves an energy efficiency almost 2.3 times larger than pre-

comp. and 8.5 times larger than the traditional and the reference schemes.

Figure 40. Lowest energy consumption per RbTt fair opportunistic transmitted bits.



5.4 Selected methods

This document has introduced spectrum sensing performance results for two energy detection based

methods (LAD and WIBA) and for GRCR and eigenvalue-based detection. Energy detection-based

methods does not need information about the features of the signal to be detected. However, typically

they must have estimation of the noise level to set threshold for signal detection decision making. The

studied ED based methods, LAD and WIBA, can estimate the noise level by themselves, i.e. it does not

need to be provided beforehand. GLRT has further advantage, since it does not need the noise power

level. However, GLRT has higher calculation complexity than ED-based methods. The computational

capabilities of the SUs devices shall be taken into consideration for the definition of each type of

detection method is more feasible to implement and this can be a vendor-specific decision.

According to the specifications defined for the IEEE 802.22, the DTV signals should be detected from

-116 dBm level when satisfying Pd > 0.9 requirement, and wireless microphone signals should be

detected from -107 dBm level. Further, FCC has defined that -114 dBm threshold should be used for

DTV signal detection. Therefore, they are defined to be class 1 signals in this work. Based on the results

of this work and [68], LAD ACC and WIBA method can detect the wireless microphone signal from

₋107 dBm level, satisfying the requirement. The IEEE 802.22 (and FCC) requirement for DTV signal

detection is very tight and the studied blind methods cannot satisfy it. However, it is important to notice

that DTV receivers need approximately SNR > 15 dB to decode the signal correctly. Therefore, the

studied blind methods can detect the DTV signals much below the practical SNR values. In order to

satisfy the IEEE 802.22 requirement for DTV signals detection, more complex detection-based methods

can be used. For example, in [11], stripped-down version of transmitter parameter signaling (TPS) was

proposed to be used. Wireless microphone signals usually employs FM modulation, so cyclostationarity

cannot be used. Waveform based detection requires information about the users, and this information is

not available because there is no general standard for wireless microphones. Wavelet transform based

detection requires a lot of power and is not suitable when energy is limited. Thus, proper sensing

methods are covariance and eigenvalue-based methods. For example, in [11], covariance absolute value

detection algorithm (CAV) and maximum eigenvalue to trace detection algorithm (MET) were proposed

to be used.

In this work, among the ED methods, WIBA was found to provide better performance than LAD. In

eigenvalue-based detection case, GRCR was found to be best and fairly simple test statistic for

cooperative or multi-antenna spectrum sensing. Although simple and full-blind, GRCR is robust against

dynamical noise and received signal powers, it exhibits CFAR property and outperforms the most

common full-blind detectors in the literature. Therefore, WIBA and GRCR method are selected to be

studied in more details for different use cases of 5G-RANGE. Results will be reported in D4.3 where

simulations will be done considering the cognitive cycle.

Cooperative spectrum sensing performance was also studied and results clearly illustrates that it will

improve the performance of the studied methods. Due to characteristics (mobility of UEs and varying

terrains) of 5G-RANGE scenarios, it is important to consider cooperative spectrum sensing techniques

in the further performance analysis that will be done for the above-mentioned techniques in 5G-RANGE.

Technology Readiness Levels (TRL) of the selected techniques are currently at TRL 2 or TRL 3 [84]

[85], since the WIBA and GRCR methods, neither cooperative approach, have not been experimentally

tested. Instead, the concept and application of the methods has been formulated as well as performance

has been verified by analytical calculations and computer simulations, which fulfills partly the TRL 3

requirements. Furthermore, it can be noted that the WIBA method is derived from the LAD method,

which has been already demonstrated to be operating correctly in relevant environment when the world’s

first phone call over a cognitive radio network [86]. The method(s), which will be finally implemented

in the 5G-RANGE proof-of-concept (PoC), target to achieve the TRL 6 since then the method(s) will

be tested in a relevant environment.



6 Functional Parameters

Spectrum sensing function (SSF) input and output parameters must be defined to enable efficient

operation as a part of 5G-RANGE cognitive cycle, enabling dynamic spectrum access orchestrated by

cognitive MAC layer. SSF has crucial role in cognitive cycle to find out free channels (white spaces)

from TV spectrum and to coexist with other secondary users. Base station (gNB) gets spectrum

allocation information from database which will be complemented by spectrum sensing by UEs. Use of

database and spectrum sensing has been introduced in [3]. Based on 5G-RANGE architecture definition

in D2.2, database information will be acquired by gNB using S1-GDB interface. In [4] it has been

defined that the UEs will report spectrum sensing information (SSI) to BS over the licensed control

channels. OAM SAP interface of MAC layer architecture is used to exchange database and SSI

information [4]. The cognitive engine at BS MAC layer processes SSI reported by UEs to decide for the

best TVWS spectrum setup, providing reliable radio resource blocks and supporting adaptive bandwidth

usage. Here, the functional parameters related to database usage and spectrum sensing will be listed.

These parameters can be used as an input for Task 4.3 in order to define cognitive cycle and dynamic

spectrum sensing functions in details and in Task 4.4, where simulations of cognitive cycle will be

performed.

In 5G-RANGE architecture, the Dynamic Access and Resource Allocation (DARA) block will collect

information from database. The 5G-COSORA block, which implements the SSF, will perform adaptive

spectrum allocation for free frequencies. Below, the parameters that can be queried from GDB and SSF

are defined.

Information from database:

• Authorized TV signals;

• Registered programme-making and special events (PMSE) signals (e.g. professional wireless

microphones, talkback systems and/or in-ear monitors);

• List of vacant frequencies;

• Location, bandwidth, maximum allowed power, period of usage for each registered user;

• Co-channel adjacent channel ratio;

• Protected area for registered users (is calculated based on centre frequency, output power,

antenna gain and characteristics of the channel);

• Regulation limitations;

• Registered 5G-RANGE BS and UEs will be added to the database.

Outputs from spectrum sensing function of COSORA:

• Free / occupied frequency channel decision based on sensing result;

• Bandwidth of the detected signal (if enabled by used sensing technique);

• Power level of detected signal;

• Signal type (if possible to detect it with used spectrum sensing method);

• Noise level of the free channel.



7 Conclusions

This report considers different spectrum sensing methods and their feseability to complement database

approach for 5G in remote areas as input for furher work, especially the one to be done in Task 4.3,

which defines the cognitive cycle for 5G-RANGE. In 5G-RANGE architecture, the spectrum sensing

function is performed in the COSORA block, which operates as a part of cognitive cycle.

At first, the different spectrum sharing concepts and white space detection methods were introduced at

the high-level, in this document. Then a review about different spectrum sensing algorithms was

presented to introduce state-of-the-art techniques. A spectrum sharing model was proposed and

performance evaluation of selected spectrum sensing techiques for 5G-RANGE was performed. Suitable

spectrum sensing algorithms based on proposed spectrum sharing model, project scenarios, core use

cases and present signals were proposed based on the performance evaluation. Result of this work show

that the window-based energy detection (WIBA) and eigenvalue-based detection (GRCR) provide

sufficient signal detection performance with a moderate complexity to be used in the SU devices. The

results show further that cooperative sensing can improve the detection performance remarkably and it

is seen to be important for 5G-RANGE scenarios, especially in the case of mobility and shadowing in

long-range links of remote areas. Future work includes further performance analysis of the selected

spectrum sensing techniques (WIBA and GRCR) as a part of cognitive cycle and the results will be

reported in D4.3 for detailed 5G-RANGE use cases.



8 Bibliography

[1] V. Valenta, R. Maršálek, G. Baudoin and M. Villegas, “Survey on spectrum utilization in Europe:

Measurements, analyses and observations,” in Proceedings of the Fifth International Conference

on Cognitive Radio Oriented Wireless Networks and communications, Cannes, 2010.

[2] M. R. Alonso, D. Pletz, M. Deruyck, L. Martens, G. G. Nieto and W. Joseph, “TV white space

and LTE networks optimization toward energy efficiency in suburban and rural scenarios,” IEEE

Transactions on Broadcasting, vol. 64, no. 1, pp. 164-171, March 2018.

[3] 5GRANGE, “D2.2 - Architecture, system and interface definitions of a 5G for Remote Area

network,” 2018.

[4] 5GRANGE, “D4.1 - Definition of Cognitive MAC Reference Architecture,” 2018.

[5] FCC, “3.5 GHz band/citizens broadband radio service.,” [Online]. Available:

https://www.fcc.gov/wireless/bureau-divisions/broadband-division/35-ghz-band/35-ghz-band-

citizens-broadband-radio.

[6] M. Matinmikko, H. Okkonen, M. Palola, S. Yrjölä, P. Ahokangas and M. Mustonen, “Spectrum

sharing using licensed shared access: the concept and its workflow for LTE-Advanced networks,”

IEEE Wireless Communications, 2014.

[7] M. Mustonen, M. Matinmikko, D. Roberson and S. Yrjölä, "Evaluation of recent spectrum sharing

models from the regulatory point of view," in International Conference on 5G for Ubiquitous

Connectivity, Finland, 2014.

[8] M. Matinmikko, M. Mustonen, D. Robertson, J. Paavola, M. Höyhtyä and S. Yrjölä, "Overview

and comparison of recent spectrum sharing approaches in regulation and research - From

opportunistic unlicensed access towards licensed shared access," in DYSPAN, 2014.

[9] S. Yrjölä, M. Matinmikko and M. Mustonen, "Analysis of Sharing Economy Antecedents for

Recent Spectrum Sharing Concepts," in COCORA, 2017.

[10] J. Mitola, Cognitive Radio: An Integrated Agent Architecture for Software Defined Radio,

Stockholm: Doctoral thesis, Royal Institute of Technology (KTH), Sweden, 2000.

[11] S. Haykin, “Cognitive Radio: Brain-Empowered Wireless Communications,” IEEE Journal on

Selected Areas in Communications, vol. 23, no. 2, pp. 201-220, 2005.

[12] I. F. Akyildiz, W.-Y. Lee, M. C. Vuran and S. Mohanty, “NeXt generation/dynamic spectrum

access/cognitive radio wireless networks: A survey,” Computer Networks, vol. 50, no. 13, pp.

2127-2159, 2006.

[13] CogEU project, “http://www.ict-cogeu.eu/,” CogEU project .

[14] J. Vartiainen, M. Höyhtyä and R. Vuohtoniemi, "The future of spectrum sensing," in ICUFN,

Vienna, Austria, 2016.

[15] I. F. Akyildiz, B. F. Lo and R. Balakrishnan, “Cooperative Spectrum Sensing in Cognitive Radio

Networks: A survey,” Physical Communication, vol. 4, no. 1, pp. 40-62, 2011.

[16] B. Nadler, F. Penna and R. Garello, “Performance of eigenvalue-based signal detectors with

known and unknown noise level,” in IEEE International conference in communications, 2011.



[17] R. Balamurthi, H. Joshi, C. Nguyen and A. K. Sadek, "A TV White Space Spectrum Sensing

Prototype," in DYSPAN, 2011.

[18] D. Riviello, R. Garello, S. Benco and F. Crespi, "Spectrum Sensing in the TV White Spaces,"

International Journal on Advances in Telecommunications, vol. 6, no. 3, pp. 109-122, 2013.

[19] B. M. Lee, H. Song and J. S. Lee, “Implementation of a Regional Spectrum Sensing Based

Cognitive Radio System for Digital TV White Space,” Taylor & Francis Online, 2017.

[20] A. Ali and W. Hamouda, "Spectrum monitoring using energy ratio algorithm for OFDM-based

cognitive radio networks," IEEE Transactions on Wireless Communications, vol. 14, no. 4, 2015.

[21] F. Salahdine, Compressive Spectrum Sensing for Cognitive Radio Networks, Doctoral Thesis:

National Institute of Posts and Telecommunications, Maroc, 2018.

[22] H. S. Chen and W. Gao, "Spectrum Sensing for FM Wireless Microphone Signals," in IEEE

Symposium on New Frontiers in Dynamic Spectrum, 2010.

[23] D. Zhang, L. Dong and N. Mandayam, “Sensing Wireless Microphone with ESPRIT from Noise

and Adjacent Channel Interference,” in IEEE Globecom, 2010.

[24] X. Zhao, H. Zhao, J. Xu and D. Li, “A multi-threshold spectrum sensing algorithm based on the

phototropism theory for cognitive radio,” in International Conference on Information Science and

Technology, 2014.

[25] G. Betta, D. Capriglione, G. Cerro and L. Ferrigno, “The effectiveness of savitzky-golay

smoothing method for spectrum sensing in cognitive radios,” in XVIII AISEM Annual Conference,

2015.

[26] G. Chu, K. Niu, W. Wu, F. Yang and W. Jiang, “Performance evaluation of spectrum sensing

based on wavelet entropy for cognitive radio networks,” in Int. Symp. on Wireless Personal

Multimedia Communications, 2014.

[27] Y. Zeng and Y. C. Liang, “Covariance Based Signal Detections for Cognitive Radio,” in DYSPAN,

2007.

[28] Y. Zeng, Y. C. Liang and R. Zhang, “Blindly Combined Energy Detection for Spectrum Sensing

in Cognitive Radio,” IEEE Signal Processing Letters, vol. 15, 2008.

[29] T. Yuccek and H. Arslan, “OFDM Signal Identification and Transmission Parameter Estimation

for Cognitive Radio Applications,” in IEEE Globecom, 2007.

[30] N. Swetha, P. N. Sastry and Y. R. Rao, “Analysis of spectrum sensing based on energy detection

method in cognitive radio networks,” in Int. Conf. on IT Convergence and Security, 2014.

[31] Y. Ahmed, F. Hu and H. M. Noras, “Enhanced Energy Detection Based Spectrum Sensing In

Cognitive Radio Networks Using Random Matrix Theory,” in International Symposium on

Communication Systems, Networks & Digital Sign, 2014.

[32] J. Vartiainen, J. Lehtomäki and H. Saarnisaari, “Double-Threshold based Narrowband Signal

Extraction,” in IEEE Vehicular Technology conference, Stockholm, Sweden., 2005.

[33] J. Vartiainen, Concentrated Signal Extraction Using Consecutive Mean Excision Algorithms,

University of Oulu, Department of Electrical and Information Engineering: Doctoral Thesis, 2010.

[34] H. Saarnisaari and J. Vartiainen, “Spectrum Window Based Signal Detection at Low SNR,” in

International Conference on Military Communications and Information Systems, 2018.



[35] Z. Khan, Coordination and Adaptation Techniques for Efficient Resource Utilization in Cognitive

Radio Networks, University of Oulu: Doctoral Thesis, 2011.

[36] S. M. Mishra, A. Sahai and R. W. Brodersen, “Cooperative Sensing among Cognitive Radios,” in

IEEE International Conference on Communications, 2006.

[37] T. Yucek and H. Arslan, “A Survey of Spectrum Sensing Algorithms for Cognitive Radio

Applications,” IEEE Communications surveys & tutorials, vol. 11, no. 1, 2009.

[38] G. Ganesan and Y. Li, “Cooperative spectrum sensing in cognitive radio networks,” in Proc. IEEE

Int. Symposium on New Frontiers in Dynamic Spectrum Access Networks, Baltimore, Maryland,

USA, 2005.

[39] G. Ganesan and Y. Li, “Cooperative Spectrum Sensing in Cognitive Radio, Part I: Two User

Networks,” IEEE transactions on Wireless Communications, vol. 6, no. 6, 2007.

[40] V. Balaji, T. Nagendra, C. Hota and G. Raghurama, “Cooperative Spectrum Sensing in Cognitive

Radio: An Archetypal Clustering Approach,” in IEEE WISPNET, 2016.

[41] C. Song, M. Zhou, P. H. Lin and H. Harada, “A cooperative spectrum sensing method for sensing

under noise uncertainty and interference,” in Int. Conf. on Advanced Technologies for

Communications, 2014.

[42] J. Guo and e. al., “An improved weighted cooperative spectrum sensing in cognitive radio

networks,” in IEEE Int. Conf. Consumer Electronics, Communications and Networks, 2013.

[43] S. Jin and X. Zhang, “An efficient method for collaborative compressive spectrum sensing in

cognitive radio networks,” in IEEE Global Communications Conference, 2014.

[44] T. Zhang, Z. Li and R. Safavi-Naini, “Incentivize cooperative sensing in distributed cognitive

radio networks with reputation-based pricing,” in Int. Conf. on Computer Communications, 2014.

[45] Y. Liao, T. Wang, L. Song and B. Jiao, “Cooperative spectrum sensing for full-duplex cognitive

radio networks,” in IEEE Int. Conf. on Communication Systems, 2014.

[46] Z. Khan, J. Lehtomäki, M. Mustonen and M. M, “Sensing Order Dispersion for Autonomous

Cognitive Radios,” in The 6th International ICST Conference on Cognitive Radio Oriented

Wireless Networks and Communications (CROWNCOM), Osaka, Japan, June 2011.

[47] Y. Selen, H. Tullberg and J. Kronander, “Sensor selection for cooperative spectrum sensing,” in

IEEE DYSPAN, 2008.

[48] C. Sun, W. Zhang and K. Ben, “Cluster-based cooperative spectrum sensing in cognitive radio

systems,” in IEEE ICC, 2007.

[49] A. Malady and C. da Silva, “Clustering methods for distributed spectrum sensing in cognitive

radio systems,” in Proc. of IEEE MILCOM, 2008.

[50] C. Guo, T. Peng, S. Xu, H. Wang and W. Wang, “Cooperative spectrum sensing with cluster-

based architecture in cognitive radio networks,” in IEEE Vehicular Technology Conference, 2009.

[51] A. S. Cacciapuoti, I. F. Akyildiz and L. Paura, “Correlation-Aware User Selection for Cooperative

Spectrum Sensing in Cognitive Radio Ad Hoc Networks,” IEEE JOURNAL ON SELECTED

AREAS IN COMMUNICATIONS, vol. 30, no. 2, 2012.

[52] K. Srisomboon, A. Prayote and W. Lee, “Two-stage spectrum sensing for cognitive radio under

noise uncertainty,” in Int. Conf. on Mobile Computing and Ubiquitous Networking, 2015.



[53] W. Lee, K. Srisomboon and K. Thakulsukanant, “Adaptive Two-stage Spectrum Sensing under

Noise Uncertainty in Cognitive Radio Networks,” ECTI Transactions on Electrical Engineering,

Electronics & Communications, vol. 14, no. 1, 2016.

[54] G. Wang, C. Guo, S. Feng, C. Feng and S. Wang, “A two-stage cooperative spectrum sensing

method for energy efficiency improvement in cognitive radio,” in Symp. on Personal, Indoor and

Mobile Radio Communications, 2013.

[55] S. Maleki, A. Pandharipande and G. Leus, “Two-stage spectrum sensing for cognitive radios,” in

IEEE International Conference on Acoustics Speech and Signal Processing, 2010.

[56] M. Höyhtyä, J. Vartiainen, H. Sarvanko and Mämmelä, “Combination of short term and long term

database for cognitive radio resource management,” in International Workshop on Cognitive

Radio and Advanced Spectrum Management, Rome, Italy, 2010.

[57] Y. Ma, X. Zhang and Y. Gao, “An Efficient Joint Sub-Nyquist Spectrum Sensing Scheme with

Geolocation Database over TV White Space,” in IEEE ICC, 2017.

[58] O. Holland, B. Bochow and K. Katzis, “IEEE 1900.6b: Sensing Support for Spectrum Databases,”

in IEEE Conf. on Standards for Communications and Networking, 2015.

[59] F. Paisana, N. Marchetti and L. A. daSilva, “Radar, TV and Cellular Bands: Which Spectrum

Access Techniques for Which Bands?,” IEEE Communications Surveys & Tutorials, vol. 16, no.

3, 2014.

[60] H. Mauwa, A. Bagula, M. Zennaro and E. Pietrosemoli, “Ssystematic analysis of geo-location and

spectrum sensing as access methods to TV white space,” in ITU Kaleidoscope Academic

Conference, 2016.

[61] C. Kocks, A. Viessmann, P. Jung, L. Chen and Q. Jing, “On Spectrum Sensing for TV White

Space in China,” Journal of Computer Networks and Communications, vol. 2012, 2012.

[62] 5GRANGE, “D2.1 - Application and requirements report. 5G-RANGE: Remote Area Access

Network for 5th Generation,” 2018.

[63] J. G. Proakis, Digital Communications, McGraw-Hill, Inc., 1995.

[64] J. Vartiainen, J. Lehtomäki and R. Vuohtoniemi, “The LAD Methods in WLAN Indoor Multipath

Channels,” in Crowncom, Stockholm, Sweden, 2012.

[65] S. Bae, J. So and H. Kim, “On Optimal Cooperative Sensing with Energy Detection in Cognitive

Radio,” Sensors (Basel), 2017.

[66] S. Althunibat and F. Granelli, “An Objection-Based Collaborative Spectrum Sensing for Cognitive

Radio Networks,” IEEE Communications Letter, vol. 18, pp. 1291-1294, 2014.

[67] O. Meena and A. Somkuwar, “Comparative Analysis of Information Fusion Techniques for

Cooperative Spectrum Sensing in Cognitive Radio Networks,” in Recent Trends in Information,

Telecommunication and Computing, 2014.

[68] J. Lehtomäki, S. S, J. Vartiainen, J.-P. Mäkelä, R. Vuohtoniemi and M. Juntti, “Measurement

studies of a spectrum sensing algorithm based on double-thresholding,” in CogART, 2009.

[69] D. A. Guimarães, “Robust test statistic for cooperative spectrum sensing based on the Gershgorin

Circle Theorem,” IEEE Access, vol. 6, pp. 2445-2456, 2018.

[70] J. W. Demmel, Applied numerical linear algebra, Philadelphia, PA, USA: SIAM, 1997.



[71] S. Maleki, S. P. Chepuri and G. Leus, “Energy and throughput efficient strategies for cooperative

spectrum sensing in cognitive radios,” in Proceedings of Signal Processing Advances in Wireless

Communications.

[72] H. N. Pham, Y. Zhang, P. E. Engelstad, T. Skeie and F. Eliassen, “Energy minimization approach

for optimal cooperative spectrum sensing in sensor-aided cognitive radio networks,” in Proc. of

Wireless Internet Conference, 2010.

[73] S. Maleki and G. Leus, “Censored Truncated Sequential Spectrum Sensing for Cognitive Radio

Networks,” IEEE Journal on Selected Areas Communications, vol. 31, no. 1, pp. 364-378, 2013.

[74] N. Zhao, F. R. Yu, H. Sun and A. Nallanathan, “An energy-efficient cooperative spectrum sensing

scheme for cognitive radio networks,” in Proc. of Global Communications Conference, 2012.

[75] X. Feng, X. Gan and X. Wang, “Energy-constrained cooperative spectrum sensing in cognitive

radio networks,” in Proc. of the Wireless Communications and Networking Conference, Kowloon,

Hong Kong, 2007.

[76] Q. Zou, S. Zheng and A. Sayed, “Cooperative Sensing via Sequential Detection,” IEEE

Transaction on Signal Proccessing, vol. 58, pp. 1147-1152, 2012.

[77] Y. Zou, Y. D. Yao and B. Zheng, “A Cooperative Sensing Based Cognitive Relay Transmission

Scheme without a Dedicated Sensing Relay Channel in Cognitive Radio Networks,” IEEE

Transaction on Signal Processing, vol. 59, pp. 854-858, 2011.

[78] Y. Zou, Y. D. Yao and B. Zheng, “A Selective-Relay Based Cooperative Spectrum Sensing

Scheme without Dedicated Reporting Channels in Cognitive Radio Networks,” IEEE Transaction

on Wireless Communications, vol. 10, pp. 1188-1198, 2011.

[79] M. Ghaznavi and A. Jamshidi, “Efficient method for reducing the average control bits in a

distributed cooperative sensing in cognitive radio system,” IET Communications, vol. 7, pp. 867-

874, 2013.

[80] C. H. Lim, “Resource-efficient transmission for report channel in cooperative spectrum sensing,”

Electronic Letters, vol. 50, pp. 1171-1173, 2014.

[81] G. P. Aquino, D. A. Guimarães, L. L. Mendes and T. C. Pimenta, “Combined Pre-Distortion and

Censoring for Bandwidth-Efficient and Energy-Efficient Fusion of Spectrum Sensing

Information,” Sensors, vol. 17, no. 3, p. 654, 2017.

[82] D. A. Guimarães, G. P. Aquino and M. E. Cattaneo, “Resource-Efficient Fusion with Pre-

Compensated Transmissions for Cooperative Spectrum Sensing,” Sensors, vol. 15, no. 5, pp.

10891-10908, 2015.

[83] A. Ghosh, J. Zhang, J. G. Andrews and R. Muhamed, Fundamentals of LTE, Upper Saddle River,

NJ: Prentice Hall Press, 2010.

[84] EU, “Horizon 2020 - Work Programme 2016-2017, "Technology readiness levels (TRL)", Annex

G,” 2017. [Online]. Available:

http://ec.europa.eu/research/participants/data/ref/h2020/other/wp/2016_2017/annexes/h2020-

wp1617-annex-g-trl_en.pdf. [Accessed 12 2018].

[85] ESA, “Technology Readiness Levels Handbook for Speace Applications,” September 2008.

[Online]. Available: https://artes.esa.int/sites/default/files/TRL_Handbook.pdf. [Accessed Dec

2018].



[86] M. Raustia, “Cognitive Radio Network Demonstration at Public CWC GIGA Seminar 2010,” 11

January 2010. [Online]. Available: http://www.cwc.oulu.fi/public-cwc-giga-

2010/1200_Raustia.pdf. [Accessed 18 December 2018].

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