A Method for Detecting the Parameters of a Digital Modulation for Cognitive Radio

8/8/2019 A Method for Detecting the Parameters of a Digital Modulation for Cognitive Radio

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A method for detecting the parameters of a digital modulation

for a cognitive radio receiver

Plugariu Ovid

Abstract:” Cognitive Radio have become a key research area in the communications field over the past fewyears as a powerful application addapted to the latest technological demands. Cognitive radios can work collaborativ

o make observations, share knowledge of the environment, and relate lessons from experience. In this project I hav

tudied how you can determine the modulation format using the computational method called recurrence plot analys

I Cognitive Radio have become a key research area in the communications field over the past few years as

powerful application addapted to the latest technological demands. Radio cognitive represents an evolved concept

based on the earlier software defined radios (SDR), software being the tool which gives them their enormous advant– “continous adaptability” [3].

The radio cognitive systems enable the use of artificial intelligence (AI) on flexible communications platform

uch as software defined radios (SDR) to enable on-board, real-time optimization of frequency, time, power, and oth

parameters. They can readapt to existing signal conditions through spectrum sensing and assignment of adequateesources. Here is what experts say about cognitive radios : “ Making radios so smart that they can autonomously

discover how, when and where to use radio spectrum to obtain information services without having previously

programmed to do so”.

Their key characteristics are:1. Sense the radio environment;2. Make decisions;

3. Learn from experience to improve future decision making.To obtain advantages given by the reconfigurability there has to be an efficient management of the entire

decision-making process. Cognitive radios achieve more efficient spectrum utilization by opportunistically findingmpty frequency bands. This new paradigm relies on the fact that a significant portion of the spectrum allocated toincesed services show little usage over time. This is a great advantage because the size of a frequency band is

physically determining the capacity of a radio network [3].

Cognitive radios can work collaboratively to make observations, share knowledge of the environment, and

elate lessons from experience. They can readapt their communications strategy with “friendly users” according to theceived feedback, they can switch radio bands or modulation/ demodulation protocols. This gives them great

adaptability for military applications because in this specific area friendly signals should be securely transmitted and

eceived whereas hostile signals must be located, identified and jammed.

II Automatic modulation classification (AMC) is an important component that improves the overallperformance of the cognitive radio.



AMC is often a difficult task to perform when there is no apriori information about the signal, such as signal

position, power carrier frequency and timing parameters.There are many efforts oriented to improving AMC methods because communications protocols are frequen

performed in non-cooperative environments. Moreover, in addition to multiple- propagation there are other problem

o be solved, like the frequency-selectivity and time-varying nature of a channel, especially when no prior knowledgof the incoming signal is available.

The design of a modulation classifier involves two steps:

Signal preprocessing. This stage includes the following tasks to be solved:

- noise reduction;

- estimative carrier ;- symbol period;

- signal power .

2 Proper selection of the classification algorithm. There are two ways to do this:

A. Likehood function based classification (LB)

B. Feature based classification: several features are employed/analysed and a decision is being made.

IEEE has developped a special standard for cognitive radios (IEEE 802.11k) which includes the following

haracteristics:1. The utilization of a certain frequency band;2. Noise histogram (estimated for a noise-only situation).

3. The hidden node ratio (existence of hidden receivers)

4. Time histogram in which the environment is loaded or free.- The non-802.11k energy levels are measured

- The non 802-11k interferences are measured

The usage of Cognitive Radio technology has an overall emprovement of the spectrum management of 15% [4

III Recurrence Plot Analysis

A signal is a time series which can be real or complex, with noise and modulations more or less difficult to

ecognize and analyse. One possible analysis can be performed using a time-frequency approach.

Recurrence plot analysis : helps visualizing the recurrences in a two dimensional plot, hence it provides

nformation about the dynamics of the system highlighting the presence of certain patterns.

This method is appropriate for computation purposes because it can analyse high-dimensional dynamical-systems

Recurrence diagram

. We find the DM(distance matrix) DM є MM,M(R) DMi,j=

x t

t f

X(f

(1



I. The Recurrence Matrix

RM MM,M(R) RMi,j=H( )

H[n]=the Heaviside function (1)

H[n]= H[n]=

=the Dirac impuls

This recurrence matrix has the capacity to resume in a compact form the essential characteristics of a analysed

ignal; being also known as “the signal’s fingerprint”.

When we reconstruct an RP we must choose an specific threshold . In an TRP (threshold RP) the pixel laying

i,j) is black if the distance falls within a specific corridor or white otherwise

Structures in RP’s

• Homogenous RP’s= typical stationary systems (relaxation times are short)

• Periodic and quasi-periodic RP’s=oscillatory systems whose oscillations are not easily recognizable

• Drift RP’s= slowly varying parameters

• Abrupt RP’s= dynamics of the system are extreme

Small scale structures

• Single, isolated recurrence points= occurs if states are rare, if they persist

• A diagonal line= occurs if a segment of the trajectory runs almost in parallel to another

segment for a time;

• A vertical/horizontal line= marks a time interval in which a state does not change or

changes very slowly;

• Bowed lines= non constant slope

Properties and qualities of RP’s

The principal arguments for the utilization of RP for the characterization of signals:- It’s not based on signal’s energy based characterization/ energetic measurements decision;- It doesen’t need a huge number of samples;

- Visualising the signal’s trajectory in state space allow us to reveal some auto-similarities which are difficult

observe with a second order correlation;

- The recurrence plot representation is more flexible than a frequencial approach;- Utility for recognition and detection of deterministic signal with noise

- RPA transforms a signal (one dimension) into a picture 2D:a. we cand analyse the signal as a picture

b. it’s a redundant picture

c. the picture depends on the characteristics of the signald. moreover identifying could be useful to detection, characterization or

classification



Modulation must allow data transmission between transmitter and receiver without ambiguity. It’s a way of

protecting the signal from noise and adapting it to the transmission line. I have analysed two signals using the RPA

first one is a sine wave (with and without noise) and the other is an FSK4 signal(with and without noise).

SINE ASK4

FSK4 WITH RECURRENCE PROCESSING NOISE FSK4 WITH RECURRENCE NOISE AND SYSTEM NOI



FSK8 FSK8 WITH NOISE

FSK2 WITH RECURRENCE PROCESSING NOISE REMOVED

FSK 4 WITH RECURRENCE PROCESSING NOISE REMOVE



After this filtering I obtain an image in which we have the frequencies of the initial signal in every distinct

quare on the LOS(line of synchronization). The distance between two parallel lines in each square represents the

period of that specific sine wave. This is how we determine the period parameter of the signal.

Recurrence plots and their quantification are useful tools for studying and classifying dynamical processesand their transitions even if only short non-stationary time series are available. The plots can be processed using pho

econstruction techniques for noise reduction and for computed analysis. The modulation format can be recognized

using pattern-search in the recurrence matrix. Recurrence plot analysis is a modern computational method which isused successfully in areas where the quantity of the information that has to be analised is large and complex. Such a

area is the automatic modulation classification for a cognitive radio system, because it is very easy for a computer to

apidly interpret the information from a recurrence analysis comparing it with pre-defined patterns and to take adecision over the analysis result.

This method can be successfully used in complex system analysis like cognitive radios and it is also used i

modern physics, complex phemenomenon analysis, natotechnology, bioengineering, cardiology etc.

Bibliography:1. www.recurrence–plot.tk

2. “ A Survey of Automatic Modulation Classification Techniques:

Classical Approaches and New Trends”Octavia A. Dobre, Ali Abdi, Yeheskel Bar-Ness and Wei Su

3. “Cognitive radio architecture” Joseph Mitola



4. http://www.networkworld.com/news/tech/2004/0329techupdate.html

802.11k reference

Plots have been constructed with “CROSS RECURRENCE PLOT TOOLBOX 5.15” for Matlab developped

Marwan&Kurts. Filtering algorithms are developed by the author.

* Student in the “ Military Technical Academy”

http://www.networkworld.com/news/tech/2004/0329techupdate.html

http://www.networkworld.com/news/tech/2004/0329techupdate.html

A Method for Detecting the Parameters of a Digital Modulation for Cognitive Radio

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