LEVERAGING COGNITIVE RADIOS FOR EFFECTIVE WIRELESS

COMMUNICATIONS OVER WATER

by

Li Zhang

A project report submitted in partial fulfillmentof the requirements for the degree

of

Master of Science

in

Computer Science

MONTANA STATE UNIVERSITYBozeman, Montana

March 2010

c©COPYRIGHT

by

Li Zhang

2010

All Rights Reserved

ii

APPROVAL

of a project report submitted by

Li Zhang

This project report has been read by each member of the committee and hasbeen found to be satisfactory regarding content, English usage, format, citations,bibliographic style, and consistency, and is ready for submission to the College ofGraduate Studies.

Dr. Jian (Neil) Tang

Approved for the Department of Computer Science

Dr. John Paxton

Approved for the College of Graduate Studies

Dr. Carl A. Fox

iii

STATEMENT OF PERMISSION TO USE

In presenting this project report in partial fulfillment of the requirements for a

master’s degree at Montana State University, I agree that the Library shall make it

available to borrowers under rules of the Library.

If I have indicated my intention to copyright this project report by including a

copyright notice page, copying is allowable only for scholarly purposes, consistent

with “fair use” as prescribed in the U. S. Copyright Law. Requests for permission for

extended quotation from or reproduction of this project report in whole or in parts

may be granted only by the copyright holder.

Li Zhang

iv

TABLE OF CONTENTS

LIST OF TABLES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v

LIST OF FIGURES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vi

GLOSSARY.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii

ABSTRACT .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix

1. INTRODUCTION .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Transmission Over Water . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1Introduction to Cognitive Radio Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

The physical architecture of cognitive radio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5Cognitive radio network architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6IEEE standards of interest for CR. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

Our Work and Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

2. RELATED WORK.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

Previous Work on Overwater Propagation Modeling . . . . . . . . . . . . . . . . . . . . . . . . . 12Previous Work on Spectrum Allocation and Scheduling. . . . . . . . . . . . . . . . . . . . . . 12

3. SYSTEM MODEL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

Network Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14Propagation Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

4. PROBLEM DEFINITION. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

5. PROPOSED SCHEDULING ALGORITHMS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

Scheduling Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17Optimal and Heuristic Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21Heavy Traffic Case . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

6. NUMERICAL RESULTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

7. CONCLUSIONS. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

REFERENCES CITED .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

v

LIST OF TABLES

Table Page

1. Link Capacity VS. Path Loss Threshold . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

vi

LIST OF FIGURES

Figure Page

1. Overwater path losses on two different frequencies given by the AREPS 2

2. Physical architecture of the cognitive radio [2]. . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

3. The cognitive radio network architecture [26]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

4. Graph models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

5. Scenario 1: n = 5 and H = 10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

6. Scenario 2: n = 15 and H = 35 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

7. Scenario 3: H = 35 and D = 25km .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

8. Scenario 4 (success ratio): n = 15 and H = 35 . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

9. Scenario 4(network throughput): n = 15 and H = 35. . . . . . . . . . . . . . . . . . . . 31

vii

GLOSSARY

Cognitive Radio — A cognitive radio is a radio that can change its transmitter orreceiver parameters based on interaction with the environment in which it op-erates.

802.22 — IEEE 802.22 is a standard for Wireless Regional Area Network (WRAN)using white spaces in the TV frequency spectrum.

802.22 WG — IEEE 802.22 WG is a working group of IEEE 802 LAN/MAN stan-dards committee which is chartered to write the 802.22 standard.

802.11 — IEEE 802.11 is a set of standards for wireless local area network (WLAN)computer communication in the 5 GHz and 2.4 GHz public spectrum bands.

802.15 — IEEE 802.15 is the 15th working group of the IEEE 802 and specializesin Wireless PAN (Personal Area Network) standards.

802.16 — The IEEE WiMAX standard set.

SCC41 — The IEEE Standards Coordinating Committee 41 (SCC41) works on Dy-namic Spectrum Access Networks (DySPAN). The objective of this effort isto develop supporting standards dealing with new technologies and techniquesbeing developed for next generation radio and advanced spectrum management.

primary users — Primary users are the users who have the license to operate in acertain spectrum band.

secondary users — Secondary users have no spectrum license and need additionalfunctionalities to share the licensed spectrum band.

co-channel interference — Any two communication links must not use the samechannel at the same time if at least of them is in the interference range ofthe other.

white space — White spaces refer to frequencies allocated to a broadcasting servicebut not used locally. In the United States, it has gained prominence afterthe FCC ruled that unlicensed devices that can guarantee that they will notinterfere with assigned broadcasts can use the empty white spaces in frequencyspectrum.

AREPS — The Advanced Refractive Effects Prediction System (AREPS) is a so-phisticated propagation modeling tool developed by the Space and Naval War-face Systems Center.

APM — The Advanced Propagation Model (APM) is a hybrid model using thecomplimentary strengths of both ray optics and parabolic equation methods.

viii

A/D converter — An analog-to-digital converter (A/D) is a device which convertscontinuous signals to discrete digital numbers.

ix

ABSTRACT

Wireless communications over water may suffer from serious multipath fadingdue to strong specular reflections from conducting water surfaces. The effect tends tobe temporally variable and frequency selective. Cognitive radios enable dynamic fre-quency selection which can be used to mitigate this problem. In this project, we studyhow to leverage cognitive radios for effective communications in wireless networks overwater. We formally define the related problem as the Overwater Channel Schedul-ing Problem (OCSP) which seeks a channel assignment schedule such that a “good”communication link can be maintained between every Mobile Station (MS) and theBase Station (BS) all the time. We present a general scheduling framework for solv-ing the OCSP. Based on the proposed framework, we present an optimal algorithmand several fast heuristic algorithms. The proposed not only work for the MS-BScommunications but also will work for the MS-MS communications. In addition, wediscuss an extension to the heavy traffic load case and propose two throughput-awarescheduling algorithms. We performed simulation runs based on path loss data pro-vided by the Advanced Refractive Effects Prediction System (AREPS) and presentsimulation results to justify the efficiency of the proposed scheduling algorithms.

1

INTRODUCTION

While extensive research has been carried out examining the effects of terrain and

mobility on wireless communications in point-to-point, point-to-multipoint and mesh

topologies, there have been few instances where the unique effects of propagation over

water and their impact on wireless networking have been reported. In this report,

we study wireless networks over water, such as a wireless network consisting of ships.

U.S. Navy is particularly interested in such networks. Wireless communications over

water may suffer from serious multipath fading due to strong specular reflections from

conducting water surfaces.

Transmission Over Water

Overwater propagation is a special case of the more general ground reflection

problem. The large scale fading characteristics for a link whose transmitting and

receiving nodes are close to the ground are well captured by the two-ray model,

leading to the well known d−4 path loss formula [19], where d is the distance between

transmitting and receiving nodes. In the case where the E-field is in the plane of

incidence (e.g., vertically polarized) and the surface is a strong reflector, such as

a conductor, the exact expression for the received power P is given by Equation (

1.1) [19].

P (r) = PtGtGr

h2t h

2r

d4(1.1)

In this equation, Pt is the transmit power of the transmitter and d is the distance

between the transmitter and receiver. Gt and Gr are the antenna gains of the trans-

mitter and the receiver. ht and hr are the antenna heights of the transmitter and

the receiver respectively. This two-ray effect can lead to deep fades under conditions

when d = k(4hthr)/λ (null conditions), where k is an integer and λ is the wavelength.

According to Equation ( 1.1), the power decays in an oscillatory fashion, with local

minimal approaching −∞dB. Once d is sufficiently large, and the power then falls off

2

asymptotically with the increasing distance. In a practical situation, the reflecting

surface is not a perfect conductor and the surface is not flat, yielding power nulls that

may reach tens of dBs. Water surfaces tend to be flat and conducting, providing a

situation that closely approximates the ideal two-ray model. Ocean water, due to its

salinity, is an excellent conductor, with a conductivity of 5S/m, and fresh water has

a conductivity in the range of 0.005 to 0.5S/m [14].

(a) 1.7GHz (b) 2.4GHz

Figure 1. Overwater path losses on two different frequencies given by the AREPS.

Fig. 1 shows an example of the two-ray effect and overwater path losses predicted

by the Advanced Refractive Effects Prediction System (AREPS) [4]. The AREPS is

a sophisticated propagation modeling tool developed by the Space and Naval Warfare

Systems Center. It can be used for calculating propagation losses for overwater paths,

taking into account surface and atmospheric conditions. The AREPS implements the

Advanced Propagation Model (APM) which is a hybrid model using the complimen-

tary strengths of both ray optics and parabolic equation methods to construct a fast,

but yet very accurate and composite model. Moreover, the AREPS considers range

and bearing-dependent influences from surface features to include terrain elevation,

finite conductivity, and dielectric ground constants, and includes the ability to model

absorption by oxygen and water vapor. Therefore, the AREPS can provide accurate

prediction for overwater path losses. This figure shows the power loss of an path

over ocean water on two different operating frequencies, 2.4GHz and 1.7GHz, as a

function of distance between transmitting and receiving nodes (labeled as “range”),

The heights of both transmitting and receiving antennas are 60m. The power loss

predicted by the AREPS (shown by the solid black line) oscillates about the large

3

scale free space power loss( shown by the red line), with extremes ranging up to 30dB.

Empirical evidence of this effect has also been reported for an overwater LOS path

recently in [6].

Introduction to Cognitive Radio Networks

Current wireless networks are characterized by a static spectrum allocation pol-

icy, where governmental agencies assign wireless spectrum to license holders on a

long-term basis for large geographical regions [26]. However, licensed users do not

necessarily use the spectra uniformly. Recently, because of the increase in spec-

trum demand, this policy faces spectrum scarcity in particular spectrum bands. In

contrast, a large portion of the assigned spectrum is used sporadically, leading to

underutilization of a significant amount of spectrum [28]. Dynamic spectrum access

techniques were proposed to solve these spectrum inefficiency problems. Cognitive

Radio Network is considered one of the most possible and efficient approach among

these dynamic spectrum access techniques.

A cognitive radio (CR) is an intelligent radio capable of accessing the unused

spectrum dynamically on a secondary basis without causing harmful interference to

the licensed users (primary users), which provides a new possible solution to the cur-

rent spectrum scarcity problems in the area of wireless communications system. Lots

of time and money are pouring into a large number of cognitive radio technology

and standards. Technology forecasts predict that CR will be a critical part of many

future radio systems and networks. The use of CR technologies is already being con-

sidered in some regulatory domains, such as the Federal Communications Commission

(FCC) in the United States and the Office of Communications in the United Kingdom

[29] [30]. Some standardization organizations such as the international telecommu-

nications union - radio sector (ITU-R) and the software defined radio (SDR) Forum

are working in this area [31].

The term cognitive radio was first used publicly in an article [32] by Joseph

Mitola where it was defined as: ”The point in which wireless personal digital assistants

4

(PDAs) and the related networks are sufficiently computationally intelligent about

radio resources and related computer-to-computer communications to detect user

communications needs as a function of use context, and to provide radio resources

and wireless services most appropriate to those needs.”

A software-defined radio (SDR) was assumed for this definition where the radios

can be easily reconfigured to operate on different frequencies with different protocols

by software reprogramming. Later the term was reused and reworked to suit different

needs by different authors. For example, the IEEE SCC41’s P1900.1 working group

on Definitions and Terminology defines a CR as follows:

• ”A type of Radio in which communication systems are aware of their environ-

ment and internal state and can make decisions about their radio operating

behavior based on that information and predefined objectives. NOTE: The en-

vironmental information may or may not include location information related

to communication systems.

• A cognitive radio that utilizes radio, adaptive radio, and other technologies

to automatically adjust its behavior or operations to achieve desired objec-

tives [33].”

From this definition, two main characteristics of cognitive radio can be defined

[34]:

• Cognitive capability: Cognitive capability refers to the ability of the radio tech-

nology to capture or sense the information from its radio environment. Through

real-time interaction with the spectrum that are unused at a specific time or

location can be identified. Consequently, the best spectrum can be selected,

shared with other users, and exploited without interference with the licensed

user.

• Reconfigurability: The cognitive capability provides spectrum awareness whereas

reconfigurability enables the radio to be dynamically programmed according to

the radio environment [35]. More specifically, a CR can be programmed to

transmit and receive on a variety of frequencies, and use different access tech-

nologies supported by its hardware design. Through this capability, the best

5

spectrum band and the most appropriate operating parameters can be selected

and reconfigured.

The main objective of the cognitive radio is to obtain the best available spectrum

through cognitive capability and reconfigurability as described before. Since most

of the spectrum has been assigned to licensed users, the most important challenge

is to access the licensed spectrum without interfering with the transmission of other

licensed users. The cognitive radio enables the usage of temporally unused spectrum,

which is referred to as spectrum hole or white space [34]. If this band is used by a

licensed user, the cognitive radio moves to another spectrum hole or stay in the same

band, altering its transmission power level or modulation scheme to avoid interference.

In the over water case, the cognitive radio is used to select a channel with the best

propagation characteristics.

In the following subsections, I will describe the physical architecture of cognitive

radio, the cognitive radio network architecture, IEEE standards of cognitive radio

networks in the following subsections.

The physical architecture of cognitive radio

A general architecture of a cognitive radio transceiver with RF/analog front-end

is shown in Fig. 2. The transceiver and the RF/analog processing unit are the

main components of a cognitive radio. Each component can be reconfigured via a

control bus to adapt to the time-varing RF environment [36]. In the RF front-end,

the received signal is amplified, down converted and A/D converted. In the base-

band processing unit, the signal is modulated/demodulated and encoded/decoded.

The baseband processing unit of a cognitive radio is essentially similar to existing

transceivers. But the novelty of the cognitive radio is the RF front-end [35] which

we will focus on. The novel characteristic of the transceiver is a wideband sensing

capability of the RF front-end. This function is mainly related to RF hardware tech-

nologies such as wideband antenna, power amplifier and adaptive filter. RF hardware

for the cognitive radio should be capable of tuning to any part of a large range of

6

frequency spectrum. Generally, a wideband RF/analog front-end architecture for the

cognitive radio has the following components [35] [36]:

• RF filter: The RF filter selects the desired band by bandpass filtering the

received RF signal.

• Low noise amplifier: The low noise amplifier amplifies the desired signal while

simultaneously minimizing noise component.

• Mixer: In the mixer, the received signal is mixed with locally generated RF

signal and converted to the baseband or the intermediate frequency.

• Voltage-controlled oscillator (VCO): The VCO generates a signal at a specific

frequency for a given voltage to mix with the incoming signal. This procedure

converts the incoming signal to baseband or an intermediate frequency.

• Phase locked loop: The PLL ensures that a signal is locked on a specific fre-

quency and can also be used to generate precise frequencies with fine resolution.

• Channel selection filter: The channel selection filter is used to select the desired

channel and to reject the adjacent channels. The direct conversion receiver uses

a low-pass filter for the channel selection, while the superheterodyne receiver

adopts a bandpass filter.

• Automatic gain control (AGC): The AGC maintains the gain or output power

level of an amplifier constant over a wide range of input signal levels.

In this architecture, a wideband signal is received through the RF front-end,

sampled by the high speed A/D converter, and measurements are performed for the

detection of the licensed user signal. However, there exist some limitations on devel-

oping the cognitive radio front-end. The wideband RF antenna receives signals from

various transmitters operating at different power levels, bandwidths and locations.

As a result, the RF front-end should have the capability to detect a weak signal on a

large dynamic range.

Cognitive radio network architecture

The components of the CR network architecture are showed in Fig. 3.

7

Figure 2. Physical architecture of the cognitive radio [2]..

The components of the CR network architecture, as show in Fig. 3, can be classi-

fied as two groups: the primary network and the CR network. The primary network

is referred to as an existing network, in which the users have been assigned a license

to operate in a certain spectrum band. Due to their priority in spectrum access, the

operations of primary users should not be affected by unlicensed users (secondary

users).

On the other hand, the CR network users do not have the privilege to operate in

a primary users band. CR networks can also be equipped with CR basestations that

provide single-hop connection to CR users. Finally, CR networks may include spec-

trum brokers that play a role in distributing the spectrum resources among different

CR networks [26].

8

Figure 3. The cognitive radio network architecture [26]..

CR users are capable of accessing both the licensed portions of the spectrum

used by the licensed users and the unlicensed portions of the spectrum through wide-

band access technology. Consequently, the operation types for CR networks can be

classified as licensed band operation and unlicensed band operation.

• Licensed band operation: The licensed band is primarily used by the primary

network. Therefore, the CR networks are focused mainly on the detection of

primary users in this case. The channel capacity depends on the interference at

nearby primary users. Furthermore, if primary users show up in the spectrum

band occupied by CR users, CR users should vacate that spectrum band and

move to available spectrum immediately [2] [26] [37].

• Unlicensed band operation: In the absence of primary users, CR users have the

same right to access the spectrum. Hence, sophisticated spectrum sharing meth-

ods are required for CR users to compete for the unlicensed band [2] [26] [37].

9

IEEE standards of interest for CR

Standardization is the key to the success of many technologies. Cognitive radio

is not exception. Currently the IEEE has two well-known standards activities in this

area - SCC41 (formerly known as P1900) [29] and IEEE 802.22 [38]. Standards Co-

ordinating Committee 41 (SCC41) sponsor standards projects in the area of dynamic

spectrum access networks (DySpaN) [27]. The SCC41 activities are co-sponsored by

the IEEE Communications and Electromagnetic Compatibility Societies. SCC41 ad-

dresses techniques and methods of DSA require managing interference, co-ordination

of wireless technologies and include network management and information sharing.

SCC41 considers SDR to be a key enabler for CR/DSA [39]. It concentrates on de-

veloping architectural concepts and specifications for network management between

incompatible wireless networks rather than specific mechanisms that can be added to

the physical or MAC protocol layers. IEEE SCC41 will provide vertical and horizontal

network reconfiguration management methods for inter-operability in infrastructure-

less wireless networks.

The IEEE 802 LAN/MAN Standards committee created the 802.22 working group

on wireless regional area networks (WRAN) in response to the FCC Notice of Pro-

posed Rule Making (NPRM) 04-113 [29] for the use of unlicensed wireless operation

in the analog television bands. IEEE 802.22 defines air interface for use by license-

exempt devices on a non-interfering basis in VHF and UHF bands which are also re-

ferred to as the TV white spaces [40]. IEEE 802.22 working group defines the system

architecture, functionalities of various blocks and their mutual interactions. The pro-

posed protocol reference model separates the system into the cognitive, data/control

and management planes. The data/control and management planes look similar to

other standards within the IEEE The spectrum-sensing function (SSF) and geoloca-

tion function which interface with the RF stage of the device provide information to

the spectrum manager (SM) on the presence of incumbent signals as well as its current

location. The SM function makes decisions on transmission of the information-bearing

signals. The PHY,MAC and converagence layers are essentially the same as in 802.16.

Security sub-layers are added between service access points to provide protection.

10

While SCC41 and IEEE 802.22 are the primary cognitive standards efforts today,

many completed IEEE 802 standards already include CR/DSA like capabilities or re-

lated building blocks. For example, IEEE 802.15 was one of the first standards groups

to address co-existence issues since 802.15 protocols needed to share the same unli-

censed band (2.4GHz) used by IEEE 802.11 [27]. IEEE802.15.2 contains a collection

of collaborative techniques that can be applied to enable the coexistence between

IEEE 802.11 and IEEE 802.15. Some other prior IEEE standards work related to

CR deals with DFS, dynamic channel selection and TPC, i.e. IEEE 802.11h, IEEE

802.16-2004 and IEEE 802.15.4. These features deal with the fact that other systems

may operate in the Unlicensed National Information Infrastructure bands and need

protection.

Our Work and Contributions

Our approach to mitigating this effect is to shift the operating frequency. A null

in power can be avoided by a change in frequency, i.e., using a different channel. If

the trajectory and speed of a Mobile Station (MS) are known in advance, predictive

techniques can be used to estimate the times and durations of deep fades, and a

dynamic channel selection method can be used to switch its radio to an operating

frequency where the null conditions are not met. For example, during a period in

which a channel on 2.4GHz experiences deep fades, another channel on 1.7GHz can

be selected for communications if it may lead to acceptable path losses. It is assumed

that the radio of the MS’s can get access to all the channels.

The emerging cognitive radio technology enables dynamic spectrum access [2].

With a cognitive radio, an MS can dynamically switch its radio to any available

channel whenever it has packets to send. This report briefly summarizes overwater

propagation phenomena and then proposes the use of cognitive radios to mitigate the

pronounced channel fading effects that are experienced in overwater paths, which, to

our best knowledge, has not been well studied before. We formally define the related

11

problem as the Overwater Channel Scheduling Problem (OCSP) which seeks a chan-

nel assignment schedule such that a “good” communication link can be maintained

between each MS and the BS all the time. Our major contributions are summarized

as follows:

1) We present a general scheduling framework which can provide a guideline for

designing scheduling algorithms to solve the OCSP.

2) Based on the proposed framework, we present an optimal algorithm and several

fast heuristic algorithms for the OCSP.

3) We performed simulation runs based on path loss data provided by the AREPS

and present simulation results to justify the efficiency of the proposed algorithms.

The rest of this report is organized as follows. We discuss related work in Chap-

ter 2. The system model and problem definition are described in Chapter 3 and

Chapter 4 respectively. We present the proposed scheduling framework and algo-

rithms in Chapter 5. We present simulation results in Chapter 6 and conclude the

report in Chapter 7.

12

RELATED WORK

Previous Work on Overwater Propagation Modeling

Path loss effects for LOS paths close to the ground were extensively studied

in the early 1970’s with the deployment of point-to-point microwave radio systems.

Ground reflection, atmospheric refraction and ducting effects were reported in [16].

Particularly deep fades, exceeding 20dB relative to free space path loss, were observed

in overwater paths for radio links between the UK and France [5]. Overwater path loss

effects have generally been ignored until recently, as the focus of attention in wireless

system design and applications has been toward cellular systems and wireless LANs.

Empirical evidence of this effect has recently been reported for an overwater LOS

path in [6].

Previous Work on Spectrum Allocation and Scheduling

Spectrum allocation (channel assignment) and scheduling are very important

problems in cognitive radio networks [2], which have been studied by a few recent

works. In a centralized spectrum sharing protocol proposed in [7], spectrum manage-

ment is conducted in a central server, which can obtain a global view of network by

exchanging information with users. In [25], Zheng et al. developed a graph-theoretic

model to characterize spectrum access, based on which, they designed several central-

ized heuristics to find fair spectrum allocation solutions. Distributed methods were

presented in [8, 15, 24]. For example, a distributed spectrum allocation algorithm

based on local bargaining was presented in [8]. In [24], the authors presented optimal

and suboptimal distributed spectrum access strategies under a framework of par-

tially observable Markov decision process. In [15], the authors proposed the Dynamic

Open Spectrum Sharing (DOSS) MAC protocol, which provides real-time dynamic

spectrum allocation and high spectrum utilization. The authors of [23] introduced the

13

concept of time-spectrum block and proposed algorithms to allocate such blocks to

meet certain performance goals. In [22], Tang et al. studied joint spectrum allocation

and scheduling problems in cognitive radio networks. They presented a graph model

to characterize the interference impact. Based on that model, optimal and heuristic

algorithms were presented to find maximum throughput and fair solutions.

Channel assignment has also been studied for traditional wireless networks with

multiple homogeneous channels. In [17] and [18], the authors proposed one of the

first 802.11-based multi-radio mesh network architectures and developed several cen-

tralized and distributed heuristic algorithms for channel assignment and routing.

In [21], Tang et al. proposed an interference-aware channel assignment algorithm.

A constant-bound approximation algorithm was presented in [3] to jointly compute

channel assignment, routing and scheduling solutions for fair rate allocation. A simi-

lar problem was studied in [13]. The authors derived upper bounds on the achievable

throughput using a fast primal-dual algorithm and presented two channel assignment

algorithms.

In this report, we study channel assignment and scheduling in the context of

cognitive radio networks and overwater communications. Our problem is different

from those studied in the related works, as the main factor is the channel quality

rather than primary or secondary interference.

14

SYSTEM MODEL

In this chapter, we will describe the network model and the propagation model.

Network Model

We consider a wireless network over water, consisting of a Base Station (BS) v0

and n − 1 MSs {v1, v2, · · · , vn−1}, each of which is equipped with a cognitive radio.

The BS could be a radio station installed on the shore or carried by a ship. An MS

could be a ship, or any object flying over water such as an airplane or a balloon. The

available spectrum is divided into H non-overlapping channels. A cognitive radio can

be tuned to an available channel to deliver its packets. A radio used by a ship or an

airplane can usually transmit packets over a long distance with the help of a powerful

amplifier. For example, the SeaLancet radio developed for the U.S. Navy has an

amplifier that can increase the transmit power to 10W, which gives a transmission

range of approximately 80km [20]. Hence, each MS can directly communicate with the

BS. It is also assumed that each radio transmits at the fixed power level. Therefore,

in such a network, there are n − 1 MS-BS links and every MS-BS link needs to be

assigned a different channel for communications at any time to prevent co-channel

interference.

Propagation Model

According to the two-ray propagation model given in Equation ( 1.1), this two-ray

fading effect can lead to deep fades under conditions when θ∆ = kπ, where k is an

integer. Note that θ∆ is a function of the antenna heights of transmitting and receiving

nodes, the distance between them and the operating frequency. These conditions can

result from movements of the nodes (e.g., changes in distance). We explicitly calculate

path losses using the AREPS. Fig. 1 illustrates a calculation of path losses obtained

with AREPS for three different operating frequencies, 4.9GHz, 2.4GHz and 1.7GHz.

15

The orange horizontal line in the figure indicates a threshold of 138dB, corresponding

to a path loss that would limit the radio link capacity to a certain acceptable level.

Note that link capacity is related to path loss and other parameters such as transmit

power, antenna gains, channel bandwidth, and so on. Once the values of the other

parameters are fixed, link capacity becomes a function of path loss. In other words,

given a link capacity threshold, we can obtain the corresponding path loss threshold.

We will describe how we set the values of the other parameters in the simulation and

explain how we derived the corresponding path loss threshold according to a given

capacity threshold in Chapter 6. Fig. 1 shows, as a function of distance between

transmitting and receiving nodes, that there are intervals where the path loss exceeds

this threshold for a particular operating frequency. Intuitively, a channel assignment

and scheduling method could be used to switch the radio to a different “good” channel

whenever this happens.

16

PROBLEM DEFINITION

In this chapter, we formally define the scheduling problems to study.Each MS is as-

sumed to know its moving trajectory and speed in the next T seconds. Therefore, the

distance between the BS and an MS at any time can be computed in advance. The BS

gathers such information periodically from each MS. We define a channel assignment

schedule for each MS vi as Ai = {(τ i1, h

i1), · · · , (τ i

j−1, hij−1), (τ

ij , h

ij), · · · , (T, hi

mi)},

which specifies the channel (hij) assigned to vi in each time interval (τ i

j−1, τij). Cor-

respondingly, a channel assignment schedule for the network is given by = {Ai : i ∈

{1, 2, · · · , n− 1}}. Given a cognitive radio network over water with a BS, n− 1 MSs

and H channels, and a capacity threshold of C, we study the following optimization

problem.

Definition 1 (OCSP): The Overwater Channel Scheduling Problem (OCSP)

seeks a channel assignment schedule for the network such that at any time within

[0, T ], the capacity of every MS-BS link is no smaller than C and no two MSs share

a common channel.

Here, we are basically interested in finding a channel assignment schedule which

can always guarantee a good communication link between each MS and the BS all

the time. It is assumed that each node has a channel demand for a channel. For

simplicity, we assume an MS always keeps using a channel until it becomes unusable,

i.e., the corresponding link capacity drops below the threshold C.

17

PROPOSED SCHEDULING ALGORITHMS

In this section, we present a general framework to solve the OCSP. Based on the

framework, we present an optimal algorithm and several fast heuristic algorithms.

As mentioned before, the distance between the BS and an MS vi at any time

can be pre-computed. According to the path loss values predicted by the AREPS,

we can identify a set T ih of time intervals {(0, thi

1 ), · · · , (thij−1, t

hij ), · · · , (thi

mi−1, T )} for

each MS-channel pair (i, h), where i ∈ {1, 2, · · · , n − 1} and h ∈ {1, 2, · · · , H}.

During each of such intervals, the link capacity that can be supported by channel h

for MS vi is no smaller than the given threshold C. Note that usually these time

intervals are not continuous. For example, suppose that the BS is a stationary node

on the shore, v1 is 16km away from the BS at time 0 and it moves away along a

straight line at a speed of 60km/h. C = 10Mbps and the operating frequency is

2.4GHz. The corresponding path loss threshold is 138dB. According to Fig. 1(b),

we have 11 = {(0, 60), (84, 378), (438, 942), (1098, 2166)}. The unit of time is seconds

throughout the report.

Scheduling Framework

First, we introduce a graph model, time-channel graph, to assist computation.

We construct a time-channel graph, Gi(V i, Ei) for each MS vi. In Gi, each vertex

u corresponds to a pair of time interval and channel (tj−1, tj, h), where (tj−1, tj) ∈ih.

There is a directed edge from vertex u = (tj−1, tj, hj) to vertex u′ = (tj′−1, tj′ , hj′)

if tj′−1 ≤ tj < tj′ . In this graph, each edge e = (u, u′) can be characterized by a

5-tuple (tj−1, tj, hj, tj′ , hj′) since it is assumed that channel h is used until it becomes

unusable, i.e., channel hj is used until tj then channel hj′ is used. The direction

of an edge is consistent with the time progressing direction. Gi also includes two

virtual vertices si and di. There is a directed virtual edge from si to every vertex

corresponding to a time interval whose starting time is 0. Moreover, there is a directed

virtual edge from every vertex corresponding to a time interval whose ending time

18

is T to di. It is easy to see that a time-channel graph is a Directed Acyclic Graph

(DAG).

We say an edge ei in Gi corresponding to (tij−1, tij, h

ij, t

ij′ , h

ij′) conflicts with an

edge ek in Gk (k 6= i) corresponding to (tkl−1, tkl , h

kl , t

kl′ , h

kl′) if there exists a time within

[tij−1, tij′ ]

⋃[tkl−1, t

kl′ ] in which a common channel is shared by both MSs vi and vk. The

conflicting number of an edge ei in Gi (denoted by Nei) is the number of edges in all

the time-channel graphs other than Gi that conflict with ei. The importance of a time-

channel graph Gi lies in the fact that any simple path from si to di corresponds to a

channel assignment schedule. It can be obtained by concatenating the time intervals

corresponding to edges on the path. Similarly, we say a path pi in Gi conflicts with a

path pk in Gk if there exists a time within [0, T ] in which a common channel is shared

by both MSs vi and vk. Note that if an edge on a path conflicts with another edge on

another path, these two paths may not conflict with each other. Therefore, whether

a path pi conflicts with another one pk cannot be simply determined by checking

whether any pair of edges (ei, ek) (where ei ∈ pi and ek ∈ pk) conflict with each other.

Based on time-channel graphs for all the MSs, we can construct a corresponding

conflict graph GP (VP , EP ), which is a layered undirected graph. Each layer i corre-

sponds to an MS vi and there are totally n − 1 layers. In each layer i, each vertex

zi corresponds to a simple path pi from vertex si to vertex di in Gi. There is an

edge between every pair of vertices in layer i. Hence, the subgraph on each layer i

is a complete graph. The number of neighbors of vertex zi on layer i is called the

intra-layer degree of zi. In addition, there is an edge between a vertex zi in layer i

and another vertex zk in layer k 6= i if their corresponding paths (schedules) conflict

with each other. The number of neighbors of vertex zi on layers other than layer i is

called the inter-layer degree of zi, denoted by Dzi .

The proposed scheduling framework is formally presented as Algorithm 1.

Next, we use a simple example to demonstrate how the proposed approach works.

In this example, we have 2 MSs and 3 channels. Suppose that T = 500s. For v1,11 =

{(0, 60), (84, 378), (438, 500), 12 = {(30, 90), (300, 470)} and 1

3 = {(0, 40), (55, 440), (450, 500).

For v2,21 = {(50, 100), (270, 440), 2

2 = {(0, 70), (75, 390), (420, 500)} and 23 = {(60, 280), (305, 470).

19

Algorithm 1 The Scheduling Framework

Step 1 forall i = 1 to n − 1Construct a time-channel graph Gi(V i, Ei);Find a set of paths from si to ti and store themin set P i;

endforall

Step 2 Construct a conflict graph GP (VP , EP ) based on the paths P = {P i : i =1, 2, · · · , n − 1} found in Step 1;

Step 3 if There exists a Maximal Independent Set (MIS) S in GP such that |S| = n−1return the channel assignment schedulecorresponding to S;

elsereturn “There is no feasible solution!”;

endif

(0,60,1) (84,378,1) (438,500,1)

(30,90,2) (300,470,2)

(0,40,3) (55,440,3) (450,500,3)

s1 d1

(0,70,2) (75,390,2) (420,500,2)

(50,100,1) (270,440,1)

(60,280,3) (305,470,3)

s2

d2

G1

G2

(a) A time-channel graph

11p

12p 1

3p

21p

22p 3

3p

Layer 1

Layer 2

0 60 90 378 470500

1 2 1 2 1

0 40 90 470500

3 2 3 2 3

440

0 60 500

1 3 1

440

0 70100 390 440 500

2 1 2 1 2

0 70 280 390 470500

2 3 2 3 2

0 70100 390 470500

2 1 2 3 2

11p

12p

13p

21p

22p

33p

(b) A conflict graph

Figure 4. Graph models.

The corresponding time-channel graph is shown in Fig. 4(a). Suppose an algorithm

designed based on our scheduling framework find 3 paths on each time-channel graph:

p11 = {(0, 60, 1), (30, 90, 2), (84, 378, 1), (300, 470, 2),

(438, 500, 1)}

p12 = {(0, 40, 3), (30, 90, 2), (55, 440, 3), (300, 470, 2),

(450, 500, 3)}

p13 = {(0, 60, 1), (55, 440, 3), (438, 500, 1)}

p21 = {(0, 70, 2), (50, 100, 1), (75, 390, 2), (270, 440, 1),

20

(420, 500, 2)}

p22 = {(0, 70, 2), (60, 280, 3), (75, 390, 2), (305, 470, 3),

(420, 500, 2)}

p23 = {(0, 70, 2), (50, 100, 1), (75, 390, 2), (305, 470, 3),

(420, 500, 2)}

Every path gives a channel assignment schedule. For example, path p11 gives a channel

assignment schedule for MS v1: {(60, 1), (90, 2), (378, 1), (470, 2), (500, 1). All the cor-

responding channel assignment schedules and the conflict graph are shown in Fig. 4(b)

where the black numbers indicate the time and the red numbers are channels. In this

example, we can find an MIS including two vertices corresponding to paths p13 and p2

1

respectively, which gives a feasible channel assignment for this network.

Note that this scheduling framework can be implemented in different ways. For

example, different methods can be used to find paths in Step 1. Similarly, in Step 3,

different algorithms can be used to test if there exists an independent set S in GP

such that |S| = n − 1. This will be discussed in detail in the next section. We have

the following theorem.

Theorem 1: Any channel assignment schedule for the network returned by a

scheduling algorithm designed based on this framework is a feasible schedule.

Proof: As mentioned before, each simple si-di path in a time-channel Gi corre-

sponds to a channel assignment schedule for MS vi. The feasibility of the returned

channel assignment schedule for the network is guaranteed by the ways we construct

time-channel graphs and the conflict graph. Specifically, each Gi only includes those

vertices whose corresponding channels are usable in the corresponding time intervals,

which makes sure that the capacity constraint is satisfied. Moreover, the way we add

virtual vertices si and di, and edges into Gi ensures that each si-di path (channel

assignment schedule) covers every time interval between 0 and T .

In addition, according to the framework, the subgraph on each layer i of the

conflict graph GP is a complete graph. Therefore, an MIS in GP can include no more

than one si-di path in layer i (a channel assignment schedule for MS vi). Moreover, the

returned schedule for the network corresponds to an MIS in GP , which ensures there

21

is no confliction between any pair of individual channel assignment schedules, i.e., at

any time between 0 and T , no two MSs share a common channel. This completes the

proof.

Optimal and Heuristic Algorithms

In this section, we first present an optimal algorithm based on the proposed

framework to solve the OCSP. Then we present several fast heuristic algorithms.

All the proposed algorithms follow the scheduling framework described in Sec-

tion 5. However, they vary in Step 1 and Step 3. To achieve the optimality, the

scheduling algorithm needs to find all possible paths from si to di in Gi for each MS

vi in Step 1 and enumerate all MISs in GP in Step 3. We call this algorithm the all-

paths based scheduling algorithm. It is known that a simple Depth First Search (DFS)

based algorithm can be used to find all possible paths between a pair of vertices in

a directed graph [9]. In addition, several efficient MIS enumeration algorithms (e.g.,

the algorithm in [12]) have been proposed in the literature, which can be applied in

Step 3 to test if there exists an MIS S in GP such that |S| = n − 1. We can slightly

revise such an algorithm by making it stop once an MIS with a cardinality of n − 1

is found.

However, it is well-known that both the number of paths between a pair of vertices

and the number of MISs in a graph could be exponentially large. It may take a very

long time for the optimal scheduling algorithm to solve large cases. Hence, we present

several fast heuristic algorithms.

The first heuristic algorithm is called K-paths based scheduling algorithm, which

is formally presented as follows.

In Step 1, this algorithm simply finds K paths (instead of one path) for every time-

channel Gi, which hopefully would increase the chance of finding a feasible schedule.

Suppose that Gi has N i vertices and M i edges, and N = max{Ni : i ∈ {1, 2, · · · , n−

1}} and M = max{Mi : i ∈ {1, 2, · · · , n − 1}}. Then Step 1 takes O(n(N + M)K)

time. Step 2 can be done within O(Nn2K2) time. In Step 3, a greedy algorithm

is used to test if there exists an MIS whose cardinality is n − 1. The algorithm

22

Algorithm 2 The K-paths based scheduling algorithm

Step 1 forall i = 1 to n − 1Construct a time-channel graph Gi(V i, Ei);Find the first K paths from si to di using theDFS-based path enumeration algorithms andstore them in set P i;

endforall

Step 2 Execute Step 2 in the scheduling framework;

Step 3 forall k = 1 to KS := Ø; G := GP ;S := S + {z1

k}, where z1k is the kth vertex on

layer 1 in GP ;Remove all the vertices in layer 1, and thevertices on the other layers that conflict withz1

k from GP ;forall i = 2 to n − 1

if There are vertices left in layer iS := S + {zi

min}, where zimin is the vertex

with the minimum inter-layer degree in GP ;Remove all the vertices in layer i, and thevertices on the other layers that conflict withzi

min from GP ;else break;endif

endforallif (|S| = n − 1)

return the channel assignment schedulecorresponding to S.

endifendforallreturn “There is no feasible solution!”

tries to construct an MIS covering exactly one vertex in each layer of GP , which

can done within O(nK) time. This trial is repeated for K times to increase the

success ratio. So Step 3 takes O(nK2). The total running time of this algorithm is

O(n(N + M)K + n2NK2).

23

The second heuristic algorithm is called the K-shortest-paths based scheduling

algorithm, which is different from K-paths based scheduling algorithm in Step 1. In-

stead of constructing an un-weighted time-channel graph, we construct a weighted

time-channel graph Gi for each MS vi in which the weight of each edge is set to its

conflicting number, Nei . Then in Step 1, the K-shortest-paths based scheduling al-

gorithm finds K shortest paths for every time-channel graph Gi. The basic design

philosophy is to find paths composed of edges with relatively small conflicting num-

bers in a time-channel graph, which will unlikely conflict with paths found in other

time-channel graphs. There are a number of algorithms which can be used to find

K shortest paths in a graph in the literature. In the simulation, we implemented

a well known algorithm in [10] for this purpose, which has a time complexity of

O(NMK2 log K). Steps 2 and 3 of this algorithm are the same as those of K-paths

based scheduling algorithm. Therefore, the overall time complexity of our K-shortest-

paths based scheduling algorithm is O(nNMK2 log K + n2NK2).

The third heuristic algorithm is called the min-max-K-paths based scheduling

algorithm, whose first step is different from either the K-path based scheduling algo-

rithm or the K-shortest-paths based scheduling algorithm. As the K-shortest-paths

based scheduling algorithm, we construct a weighted time-channel graph Gi for each

MS vi. Then we use a binary search to find the minimum edge conflicting number

Nmin such that there exist K-paths in a subgraph Gi of Gi which is the same as Gi

except that it only includes those edges whose conflicting numbers are no more than

Nmin. In the simulation, we also used the algorithm in [10] to test or find K paths in

a subgraph Gi. In this way, it can ensure that the maximum edge conflicting number

of the K paths found in Step 1 is minimized. The time complexity of this algorithm

is O(nNM log MK2 log K + n2NK2).

Note that usually even if we choose a small value for K, the algorithms can

still give decent performance which is quite close to result obtained by the optimal

algorithm. For example, it was set to 15 in the simulation. Hence, these algorithms

are generally time efficient in practice.

24

Heavy Traffic Case

In this section, we discuss how to extend our solutions to the heavy traffic load

case.

The solutions given by the previous algorithms can ensure that a good communi-

cation link (the capacity threshold is satisfied) can always be maintained for each MS

all the time. However, the throughput has not been carefully addressed. Specifically,

during a certain period, it might be possible that multiple channels are usable. How-

ever, they may experience quite different path losses at a particular time, i.e., some

of them may be able to support relatively high link capacities and the other can only

support relatively low link capacities. If traffic load is light,e.g. less than 10Mbps,

it is good enough to only make sure that every selected channel is usable. However,

for the heavy traffic load case, a more careful decision should be made for channel

assignment to ensure that the channel leading to high throughput is selected among

all usable channels.

Next, we discuss how to extend the scheduling algorithms proposed in the last

section to address the above issue. Similarly, a time-channel graph needs to be con-

structed for each MS to assist computation. As mentioned before, each edge in this

graph e = (u, u′) can be characterized by a 5-tuple (tj−1, tj, hj, tj′ , hj′). We define

a weight function W (·) for each edge in Equation ( 5.1), which gives the maximum

number of bits that can be delivered between this MS and the BS in the period

[tj−1, tj′ ].

W (e) =

∫ tj

tj−1

rhj(t)dt +

∫ tj′

tj

rhj′(t)dt (5.1)

In this equation, rhj(t) and rhj

(t) gives the maximum data rates (capacities) that

can be supported by channel hj and hj′ at t respectively, which can be derived based

on the corresponding path loss values. In the simulation, we placed a number of

sample points on the time axis and obtained the corresponding path loss values using

the AREPS. We then calculated the maximum volume of traffic that can delivered in

each time interval based on those sample points, which provides a good estimation

25

for the weight function. Similarly, we can define a weight function for an si-di path

p in Gi, which gives the maximum number of bits that can be delivered between this

MS and the BS in the period [0, T ]. However, note that the path weight is usually

less than (not equal to) the summation of weighs of edges on that path. By altering

the notation a little bit, we use W (·) to denote the path weight function as well.

We propose two throughput-aware heuristic algorithms, which are described in the

following.

The first heuristic algorithm is called the K-max-throughput-paths based schedul-

ing algorithm, which is similar to the K-shortest-paths based scheduling algorithm.

However, in Step 1, we construct a weighted time-channel graph Gi for each MS vi in

which the weight of each edge ei is set to W (ei) according to Equation ( 5.1). Then

the algorithm finds K longest paths for every time-channel graph Gi. It is well known

that the longest path problem in a general graph is NP-hard. However, every time-

channel graph Gi is a DAG. K longest paths in each Gi can be found by changing the

weight of every edge ei to −W (ei) and then applying the K-shortest-path algorithm

in [10]. In addition, in Step 3, instead of adding a vertex zimin with the minimum

inter-layer degree in layer i of the current conflict graph GP (note the conflict graph

is updated every time when a vertex is added to S) into the set S every time, we add

a vertex zimax such that zi

max = argmaxz∈V iP

W (z)Dz

, where V iP is set of vertices in layer

i of GP and W (z) gives the weight of the path corresponding to z. The time com-

plexity of this algorithm is the same as that of the K-shortest-paths based scheduling

algorithm.

The second heuristic algorithm is called the max-min-throughput-K-paths based

scheduling algorithm, which is similar to the min-max-K-paths based scheduling al-

gorithm. In Step 1, we construct a weighted time-channel graph Gi for each MS vi

in which each edge weight is assigned according to Equation ( 5.1). Then we use a

binary search to find the maximum edge weight Wmax such that there exist K paths

in a subgraph Gi of Gi which is the same as Gi except that it only includes those edges

whose weight is no less than Wmax. We also use the algorithm in [10] to test or find

K shortest paths in terms of the edge conflicting number in Gi. In this way, it can

26

ensure that the minimum edge weight of the K paths found in Step 1 is maximized.

In Step 3, we use the same greedy method for testing as the K-max-throughput-paths

based scheduling algorithm. The time complexity of this algorithm is the same as

that of the min-max-K-paths based scheduling algorithm.

Note that it is likely that the two throughput-aware scheduling algorithms pre-

sented here perform better than the three heuristic algorithms presented in Section 5

in terms of network throughput. However, in terms of the probability of successfully

finding a feasible channel assignment schedule (success ratio), the throughput-aware

algorithms may not be as good as those in Section 5 which aim for finding paths

(schedules) with relatively small conflicting numbers in each time-channel graph Gi.

This tradeoff is verified by simulation results in Chapter 6.

27

NUMERICAL RESULTS

We evaluated the performance of the proposed algorithms via simulation based

on path loss data given by the AREPS.

Table 1. Link Capacity VS. Path Loss Threshold.Modulation Minimum Link Capacity Path Loss

CNR(dB) (Mbps) Threshold (dB)QPSK 1/2 10 10 13816QAM 1/2 14.5 20 133.516QAM 3/4 17.25 30 130.7564QAM 2/3 21.75 40 126.564QAM 3/4 23 45 125

The simulation runs were performed based on scenarios where the BS had a 12dBi

antenna with a height of 60m and each MS had a 2dBi antenna with a height of 60m.

The transmit power was assumed to be 10W. Moreover, the channel bandwidth and

the receiver noise figure were chosen as 10Mhz and 5dB respectively. An implemen-

tation loss of 3dB was also assumed at both the BS and MSs. The threshold Carrier

to Noise Ratio (CNR) values given in the IEEE 802.16 [1] standard for a bit error

rate of 10−6 were used in a link budget calculation with the parameters given above

to establish the maximum allowable path loss (path loss threshold) for a given mod-

ulation index and forward error correction rate. The corresponding link capacity for

each modulation index, as shown in Table 1, was obtained by combining the channel

bandwidth with the maximum supported symbol rate. In all the simulation scenarios,

the link capacity threshold was set to 10Mbps. From this table, we obtained a max-

imum path loss threshold of 138dB. In addition, the method described in Section 5

was used to calculate edge and path weights based on values from Table 1 for the

throughput-aware algorithms and the corresponding scenarios.

In these simulations, a static BS was always placed at (0, 0). In each run, each

MS was assumed to move away from the BS along a direction randomly chosen and

28

at a random constant speed uniformly distributed in [56, 65]km/s (typical speed of

a warship). The schedule duration T was set to 600s for all the scenarios. At the

beginning of each run, each MS was randomly placed in a circular strip specified by a

small circle with a radius of Dkm and a large circle with a radius of D+5km. We call

D the minimum initial distance. All the channels were divided into three groups, each

of which includes approximately the same number of channels. The first, second and

third groups of channels were chosen from the 700MHz, 1.7GHz and 2.4GHz bands

respectively with a step size of 20MHz.

Intuitively, the following parameters play a key role in system performance: the

number of MSs nM = n − 1, the number of channels H, and the minimum initial

distance D. We conducted our performance evaluation by setting those parameters

to different values in different scenarios. Since the objective of the OCSP is to find a

feasible channel assignment schedule for the network, the success ratio was used as a

performance metric. Specifically, we performed 20 simulation runs and counted the

number of times a feasible schedule was successfully found by a proposed algorithm.

For the throughput-aware algorithms (i.e, the K-max-throughput-paths based algo-

rithm and the max-min-throughput-K-paths based algorithm), network throughput

was used as the performance metric, which is the summation of the throughput given

by the channel assignment schedule of each MS. In addition, K was always set to 15.

Scenario 1 was designed to compare the proposed heuristic algorithms against

the optimal algorithm in small cases. In this scenario, nM = 5, H = 10 and D was

changed from 20km to 40km with a step size of 5km. In scenarios 2 and 3, we tested

our algorithms in larger cases. In scenario 2, nM = 15, H = 35 and D was increased

from 20km to 40km. In scenario 3, H = 35, D = 25km and nM was increased from

5 to 25. The corresponding results are presented in Figs. 5 to 7. Scenario 4 has

the same settings as as scenario 2. However, we compared the max-min-K-paths

based algorithm with the throughput-aware algorithms in terms of the success ratio.

Moreover, we randomly picked a trial in which every algorithm can find a feasible

channel assignment schedule and then compared their performance with regards to

network throughput. The corresponding results are presented in Figs. 8 to 9.

29

20 25 30 35 400.3

0.4

0.5

0.6

0.7

0.8

0.9

1

The Minimum Initial Distance (km)

The

Suc

cess

Rat

io

OptimalK−pathsK−shortest−pathsmin−max−K−paths

Figure 5. Scenario 1: n = 5 and H = 10.

20 25 30 35 400.4

0.5

0.6

0.7

0.8

0.9

1

The Minimum Initial Distance (km)

The

Suc

cess

Rat

io

K−pathsK−shortest−pathsmin−max−K−paths

Figure 6. Scenario 2: n = 15 and H = 35.

30

5 10 15 20 250.5

0.6

0.7

0.8

0.9

1

The Number of MSs (nM

)

The

Suc

cess

Rat

io

K−pathsK−shortest−pathsmin−max−K−paths

Figure 7. Scenario 3: H = 35 and D = 25km.

20 25 30 35 400.4

0.5

0.6

0.7

0.8

0.9

1

The Minimum Initial Distance (km)

The

Suc

cess

Rat

io

K−max−thru−pathsmax−min−thru−K−pathsmin−max−K−paths

Figure 8. Scenario 4 (success ratio): n = 15 and H = 35.

31

20 25 30 35 40300

350

400

450

500

550

600

650

The Minimum Initial Distance (km)

Net

wor

k T

hrou

ghpu

t (M

bps) K−max−thru−paths

max−min−thru−K−pathsmin−max−K−paths

Figure 9. Scenario 4(network throughput): n = 15 and H = 35.

We can make the following observations from these results:

1) In terms of the success ratio, the max-min-K-paths based algorithms always

performs best among all the heuristic algorithms. In small size networks, the average

difference between its success ratios and the optimal values is only 5%. On average,

it outperforms the K-shortest-paths based algorithm by 2.7% and the K-paths based

algorithm by 12.7%.

2) From Fig. 6, we can see the success ratio decreases with the minimum initial

distance no matter which algorithm is used. A large minimum initial distance usually

leads to large distances between MSs and the BS throughout the whole simulation

run thus a poor success ratio. We can also see from Fig. 7, the success ratio decreases

with the number of MSs. It is easy to understand this. With the number of channels

fixed, it becomes harder to satisfy every MS’s requirement in a larger network since

no two MSs can share a common channel.

3) As expected, we can see from Fig. 9 the two throughput-aware scheduling

algorithms provide higher network throughput than the max-min-K-paths based al-

gorithm which has been shown to be the best algorithm in terms of the success ratio.

32

However, from Fig. 8, we find out that in terms of the success ratio, the throughput-

aware algorithms are not as good as the max-min-K-paths based algorithm. This is

because the two throughput-aware scheduling algorithms focus more on throughput

than conflicting numbers in the first step, which may lead to finding a path (schedule)

that is likely to conflict with other paths.

33

CONCLUSIONS

In this report, we studied overwater communications in wireless networks with

cognitive radios. We formally defined the related problem as the Overwater Channel

Scheduling Problem (OCSP). We presented a general scheduling framework for solving

the OCSP. Based on the proposed framework, we presented an optimal algorithm and

several fast heuristic algorithms. In addition, we discussed an extension to the heavy

traffic load case and proposed two throughput-aware scheduling algorithms. AREPS-

based simulation results have been shown to justify the efficiency of the proposed

algorithms.

34

REFERENCES CITED

[1] IEEE 802.16 Working Group, Part 16: Air interface for fixed and mobile broad-band wireless access systems amendment 2: Physical and Medium AccessControl layers for combined fixed and mobile operation in licensed bands,IEEE Standard, 2005.

[2] I. F. Akyildiz, W-Y Lee, M. C. Vuran and S. Mohanty, NeXt generation/dynamicspectrum access/cognitive radio wireless networks: a survey, Computer Net-works, Vol. 50, No. 13, 2007, pp. 2127–2159.

[3] M. Alicherry, R. Bhatia and L. Li, Joint channel assignment and routing forthroughput optimization in multi-radio wireless mesh networks, Proceedingsof ACM MobiCom’2005, pp. 58–72.

[4] Space and Naval Warfare Systems Center, Advanced Refractive Effects Predic-tion System (AREPS) (Version 3.6), 2006, http://areps.spawar.navy.mil/

[5] A. E. Baker and P. J. Brice, Rada-wave propagation as a factor in the designof microwave relay links, Post Office Electrical Engineers Journal, Vol. 62,No. 4, 1970.

[6] G. Bernardi, P. Buneman, and M. Marina, Tegola tiered mesh network testbedin rural scotland, Proceedings of ACM WINS-DR’2008, pp. 9–16.

[7] V. Brik, E. Rozner, S. Banarjee and P. Bahl, DSAP: a protocol for coordinatedspectrum access, Proceedings of IEEE DySPAN’2005, pp. 611–614.

[8] L. Cao and H. Zheng, Distributed spectrum allocation via local bargaining, Pro-ceedings of IEEE SECON’2005, pp. 475–486.

[9] T. H. Cormen, C. E. Leiserson, R. L. Rivest and C. Stein, Introduction to Algo-rithms (3rd Edition), The MIT Press, 2009.

[10] W. Hoffman and R. Pavley, A method for the solution of the Nth best pathproblem, Journal of the ACM, Vol. 6, No. 4, 1959, pp. 506–514.

[11] J. Huang, R. A. Berry and M. L. Honig, Spectrum sharing with distributedinterference compensation, Proceedings of IEEE DySPAN’2005, pp. 88–93.

[12] D. Johnson and M. Yannakakis, C. H. Papadimitriou, On generating all maxi-mal independent sets, Information Processing Letters, Vol. 27, No. 3, 1988,pp. 119–123.

35

[13] M. Kodialam and T. Nandagopal, Characterizing the capacity region in multi-radio multi-channel wireless mesh networks, Proceedings of ACM Mobi-Com’2005, pp. 73–87.

[14] Lenntech, Water conductivity, http://www.lenntech.com/water-conductivity.htm

[15] L. Ma, X. Han and C-C. Shen, Dynamic open spectrum sharing MAC protocolfor wireless ad hoc networks, Proceedings of IEEE DySPAN’2005, pp. 203–213.

[16] P. F. Panter, Communication Systems Design: Line-of-Sight and Tropo-scatterSystems, McGraw Hill, 1972.

[17] A. Raniwala, K. Gopalan and T. Chiueh, Centralized channel assignment androuting algorithms for multi-channel wireless mesh networks, ACM MobileComputing and Communications Review (MC2R), Vol. 8, No. 2, 2004, pp. 50–65.

[18] A. Raniwala and T. Chiueh, Architecture and algorithms for an IEEE 802.11-based multi-channel wireless mesh network, Proceedings of IEEE Info-com’2005, pp. 2223–2234 .

[19] T. S. Rappaport, Wireless Communications: Principles and Practice (2nd Edi-tion), Prentice Hall, 2002.

[20] Harris Corporation, SeaLancet RT-1944/U tactical networking radio (TNR),2009, http://www.govcomm.harris.com/solutions/products/ 000122.asp

[21] J. Tang, G. Xue and W. Zhang, Interference-aware topology control and QoSrouting in multi-channel wireless mesh networks, Proceedings of ACM Mobi-Hoc’2005, pp. 68–77.

[22] J. Tang, S. Misra and G. Xue, Joint spectrum allocation and scheduling for fairspectrum sharing in cognitive radio wireless networks, Computer Networks,Vol. 52, No. 11, 2008, pp. 2148-2158.

[23] Y. Yuan, P. Bahl, R. Chandra, T. Moscibroda and Y. Wu, Allocating dynamictime-spectrum blocks in cognitive radio networks, Proceedings of ACM Mo-biHoc’2007, pp. 130-139.

[24] Q. Zhao, L. Tong and A. Swami, Decentralized cognitive MAC for dynamicspectrum access, Proceedings of IEEE DySPAN’2005, pp. 224–232.

36

[25] H. Zheng and C. Peng, Collaboration and fairness in opportunistic spectrumaccess, Proceedings of IEEE ICC’2005, pp. 3132–3136.

[26] I. F. Akyildiz, W. Lee, M. Vuran and S. Mohanty, A Survey on spectrum man-agement in cognitive radio networks, IEEE Communications Magazine, Vol.46, Issue. 4 pp. 40–48.

[27] A. Mody, M. Sherman and R. Martinez, A survey of IEEE standards supportingcognitive radio and dynamic spectrum access, Proceedings of IEEE MilitaryCommunications Conference’2008, pp. 1–7.

[28] FCC, ET Docket No 03-322 Notice of Proposed Rule Making and Order, 2003.

[29] FCC, Notice of Proposed Rulemaking FCC 04-113, 2005.

[30] UK Office of Communications, Choice, competition, innovation: delivering thebenefits of the digital dividend, 2007.

[31] M. Sherman, A. Mody and R. Martinze, IEEE standards for cognitive radiotechnologies, IDGA Software Radio Summit, 2008.

[32] J. Mitola and G.Q. Maguire, Cognitive radios: making software radios morepersonal, IEEE Personal Communications, Vol. 6, pp. 13–18, 1999.

[33] Standard Definitions and Concepts for Dynamic Spectrum Access: TerminologyRelating to Emerging Wireless Networks, System Functionality, and SpectrumManagement, IEEE P1900.1/D4.00, Mar. 2008.

[34] S. Haykin, Cognitive Radio: Brain-Empowered Wireless Communications, IEEEJSAC’2005, Vol.23, No. 2, pp. 201-204, 2005.

[35] F. K. Jondral, Software-Defined Radio–Basic and Evolution to Cognitive Radio,EURASIP J. Wireless Commun. and Networking, 2005.

[36] D. Cabric, S.M. Mishra, R.W. Brodersen, Implementation issues in spectrumsensing for cognitive radios, Proceedings of 38th Asilomar Conference on Sig-nals, Systems and Computers, pp. 772–776, 2004.

[37] C. Li and C. Li, Opportunistic Spectrum Access in Cognitive Radio Networks,IEEE International Joint Conference on Neural Networks’2008, pp. 3412–3415.

37

[38] IEEE 802.22 Working Group on Wireless Regional Area Networks (WRANs),for a cognitive radio-based PHY/MAC/air-interface for use by license-exempt devices in spectrum that is allocated to the TV Broadcast Service:http://grouper.ieee.org/groups/802/22, IEEE Std.802.22, 2005.

[39] R. V. Prasad et al, Cognitive Functionality in Next Generation Wireless Net-works: Standardization Efforts, IEEE Communications Magazine, Vol. 46,Apr. 2008.

[40] X. Lin and Y. Fang, A survey on the available digital TV bands for cognitiveradio operations, Fourth International Conference on Communications andNetworking in China’2009, pp. 1–7.