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HÉLIO AUGUSTO MUZAMANE

MINISTRY OF EDUCATION AND TRAINING
HANOI UNIVERSITY OF SCIENCE AND TECHNOLOGY
---------------------------------------

HÉLIO AUGUSTO MUZAMANE

TELECOMMUNICATIONS

DEVELOPMENT OF RADIO RESOURCE ALLOCATION
METHODS FOR COGNITIVE RADIO NETWORKS

MASTER OF SCIENCE

2014B

HA NOI – 2016


MINISTRY OF EDUCATION AND TRAINING
HANOI UNIVERSITY OF SCIENCE AND TECHNOLOGY
--------------------------------------HÉLIO AUGUSTO MUZAMANE

DEVELOPMENT OF RADIO RESOURCE ALLOCATION METHODS
FOR COGNITIVE RADIO NETWORKS

MAJOR : TELECOMMUNICATIONS

MASTER THESIS IN SCIENCE

SCIENTIFIC SUPERVISOR:
Associate Prof. Eng. NguyễnVănĐức

Hà Nội – 2016


List of Figures
Figure 2.1 Schematic block diagram of a digital radio [8] .........................................6
Figure 2.2 Spectrum utilization [9] .............................................................................8
Figure 2.3 Spectrum holes concepts [9] ......................................................................9
Figure 2.4 Infrastructure-based CR network architecture [8] ...................................11
Figure 3.1 Structure and spectral characteristic of multicarrier transmission system
[12] ............................................................................................................................14
Figure 3.2 Structure and spectral characteristic of OFDM transmission scheme [7]
...................................................................................................................................15
Figure 3.3 Multiple access techniques used in OFDM systems [12] ........................16
Figure 3.4 Linear receive-antenna combining [13] ...................................................17
Figure 3.5 Linear receive antenna combining [13] ...................................................18
Figure 3.6 NR x NT MIMO system [12] ...................................................................23
Figure 3.7 Modal decomposition when CSI is available at the transmitter side [12]
...................................................................................................................................26
Figure 3.8 The r virtual SISO channels obtained from the modal decomposition of a
MIMO channel [12] ..................................................................................................27
Figure 3.9 Water-filling power allocation algorithm ................................................29
Figure 3.10 Coexistence of PU and SU scenario ......................................................35
Figure 4.1 Co-existance of PU and SU at the same geographical space ..................42
Figure 4.2 Power allocation by water filling.............................................................48
Figure 4.3 Water filling configuration for the centered sub-carriers ........................52

Figure 4.4 The power profile structure for the adjacent sub-carriers .......................54
Figure 4.5 Power Allocation for 128 sub-carriers with 10 chosen adjacent subcarriers .......................................................................................................................55
Figure 4.6 Power Allocation for 128 sub-carriers with nulling the adjacent subcarriers .......................................................................................................................56
Figure 4.7 Transmission capacity .............................................................................57

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Figure 4.8 Transmission capacity of the CR user Vs SNR for the 128 sub-carriers 58
Figure 4.9 Power Allocation for 64 sub-carriers with 10 chosen adjacent subcarriers .......................................................................................................................59
Figure 4.10 Transmission capacity of the CR user Vs SNR for the 64 sub-carriers 60
Figure 4.11 Interference Comparison of G.P Algorithm Vs Bansal's Scheme A .....61

ii


List of Acronyms
4G

Fourth Generation

5G

Fifth Generation

ALOHA

Additive Links On-line Hawaii Area

BTS

Base Station Transceiver System

CR

Cognitive Radio

CSI

Channel State Information

DFT

Direct Fourier Transform

DSP

Digital Signal Processors

FCC

Federal Communications Commission

FCC

Federal Communications Commission

FPGA

Field Programmable Gate Arrays

GHz

Giga-Hertz

G-P

Geometric Progression

GPP

General-Purpose Processors

ICI

Inter-Carrier Interference

IF

Intermediate Frequency

IFT

Inverse Fourier Transform

ISI

Inter-Symbol Interference

ISM

Industrial, Scientific, and Medical

iii


Kbps

Kilo-bits per second

Km

Kilo-meters

LAN

Local Area Network

LEO

Low-Earth Orbit

LTE

Long Term Evolution

MA

Margin Adaptive

Mbps

Mega-bits per second

MC-MR

Multi-Channel Multi-Radio

MEO

Medium-Earth Orbit

MIMO

Multiple Input Multiple Output

MRC

Maximum-Ratio Combining

MTSO

Mobile Telephone Switching Office

mW

Milli-watts

OFDM

Orthogonal Frequency for Division Multiplexing

OFDMA

Orthogonal Frequency Division for Multiple Access

PSD

Power Spectral Density

PSTN

Public-Switched Telephone Network

PU

Primary User

QoS

Quality Of Service

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RA

Rate Adaptive

RF

Radio Frequency

SIMO

Single Input Multiple Output

SIMO

Single-Input And Multi-Output

SISO

Single Input Single Output

SNR

Signal to Noise Ratio

SU

Secondary User

TV

Television

UWB

Ultra-Wide Band

ZMCSCG Zero-Mean Circular Symmetric Complex Gaussian

v


Acknowledgements
I would like to thank my scientific supervisor, Associate Prof. Eng.
NguyễnVăn Đức, and Phd Nguyễn Tiến Hòa for their kindly support during the
course of this thesis.
I also would like to thank my lovely parents for their unconditional presence
and my adorable family, who always support me in my whole life. Without their
support, I could not have had the opportunity to even start my studies.

vi


Abstract
In this thesis the radio resource allocation methods are presented, taking to
further analysis the ODFM based Cognitive Radio for wireless communications.
The classical algorithms for power allocation are deeply studied and a new
algorithm applied to the adjacent sub-carriers is proposed in order to develop the
CR performance obtaining a good approximation to the expected results. The
channel capacity is maximized keeping the interference introduced to PU below a
certain threshold and furthermore the interference is also taken to be the cost
function to minimize keeping the QoS in an acceptable range.
Cognitive Radio systems are designed to be able to occupy the portion of the
unused frequency bands and they also must be aware of the interference caused to
or by the possible groups of both adjacent PU’s and SU’s bands. The resource
allocation is formulated as a pack containing many problems to be modeled for the

good or acceptable operating performance. Starting from the basic principles, such
as power control and multiple access, coverage moving to the optimization
techniques for resource allocation, including formulation and analysis [1]. Water
filling algorithm is proposed to solve the problem of resource allocation as it
allocates much amount of power to the sub-channels experiencing relatively high
SNR than others. Along with the water filling scheme, a different algorithm is
proposed to allocate the power for the group of adjacent sub-carriers as they play a
significant role in terms of interference to the PU’s bands. The performance of all
these algorithms is verified using MATLAB simulation making comparison with
the other algorithms previously studied by different authors.

vii


Index
List of Figures ...................................................................................................................i
List of Acronyms............................................................................................................ iii
Acknowledgements .........................................................................................................vi
Abstract ......................................................................................................................... vii
CHAPTER 1 - Introduction ........................................................................................... 11
1.1

Thesis outline .................................................................................................... 12

1.2

Thesis organization ........................................................................................... 13

1.3

Chapter Conclusion .......................................................................................... 13

CHAPTER 2 - Cognitive Radio Networks ...................................................................... 1
2.1

Introduction ........................................................................................................ 1

2.2

Evolution of Wireless Communication Systems ................................................ 1

2.3

Software Defined Radio ..................................................................................... 5

2.4

Cognitive Radio Networks ................................................................................. 7

2.4.1

Spectrum usage ............................................................................................ 7

2.4.2

Cognitive radio concept ............................................................................... 8

2.5

Chapter Conclusion .......................................................................................... 12

CHAPTER 3 - Resource Allocation Techniques for Wireless Communication
Networks ........................................................................................................................ 13
3.1

Introduction ...................................................................................................... 13

3.2

Multiple Access Methods ................................................................................. 13

3.2.1

OFDM ........................................................................................................ 13


3.2.2
3.3

OFDMA ..................................................................................................... 16

Resource Allocation Using Multi-Antenna Techniques .................................. 16

3.3.1

Multi-Antenna Techniques ........................................................................ 16

3.3.2

Beam-forming Techniques (at the receiver) .............................................. 17

3.3.3

Spatial Multiplexing Techniques ............................................................... 19

3.4

Adaptive Resource Allocation Techniques for OFDMA Based Wireless

Networks ..................................................................................................................... 29
3.4.1

Margin Adaptive ........................................................................................ 30

3.4.2

Rate Adaptive ............................................................................................ 30

3.5

Optimal Resource Allocation Technique for OFDM based Cognitive Radio

Networks ..................................................................................................................... 32
3.5.1

Introduction ................................................................................................ 32

3.5.2

MIMO-OFDM based Downlink Cognitive Radio Network...................... 32

3.5.3

System Model and Problem Statement ...................................................... 33

3.5.4

Channel gain .............................................................................................. 34

3.5.5

Optimal Scheme......................................................................................... 37

3.6

Chapter Conclusion .......................................................................................... 40

CHAPTER 4 - Power allocation for an OFDM based Cognitive Radio - Case Study –
(Sub-Optimal Scheme) ................................................................................................... 41
4.1

Introduction ...................................................................................................... 41

4.2

System model ................................................................................................... 41

4.3

Problem formulation (Case 1) .......................................................................... 42

4.4

Problem formulation (Case 2) .......................................................................... 48


4.5

Geometric Progression Technique for power allocation .................................. 50

4.6

Numerical results .............................................................................................. 54

4.7

Chapter Conclusion .......................................................................................... 62

Conclusion and Future work .......................................................................................... 63
Bibliography ................................................................................................................... 64


CHAPTER 1 - Introduction
The number of users or devices that need to get connected with each other
increases more rapidly as never before. Due to the necessity of connecting more people
and things (new vision for communication in future as in 5G technology), different
network configurations are being deployed and many related projects are also in
studying process. The cellular networks that have provided steady progress in wireless
communications capabilities (up to and including 4G) are evolving into new forms that
rely increasingly on local communications over short distances (e.g., small cells or

millimeter wave links). 4G LTE networks now incorporate small cells to increase the
capacity [2].
The emerging cognitive radio networks are needful to respond to the flexibility
in spectrum usage, as they have the ability of managing the spectrum according to the
given conditions even not being predicted for it. Hence they are proposed as one of the
principals technologies applied in thesis.
To achieve the goal of having more people and devices connected is necessary
to combine many technologies. Also because of this demand in connections the
spectrum scarcity arises as a new problem to be faced with. Cognitive Radio networks
are envisioned to be able to opportunistically exploit those spectrum “leftovers,” by
means of knowledge of the environment and cognition capability, to adapt to their
radio parameters accordingly. Spectrum sensing is the technique that will enable
cognitive radio networks to achieve this goal [3]. This is the technology which we
expect that will also bring a significant contribution for the small cells traffic
management as described below.
Actually the OFDM associated to the power allocation methods for the
Cognitive Radio are the expected keys for achieving the main goal of this thesis.


The CR can be adapted to many structures going from the single users in
networks to a large structure environment. For the small cells it is required to have
their capabilities of self-organizing network (through cognition which they will be
endowed) for efficient operation with limited centralized control. It’s also described the
case where at the base station is employed a MIMO system and the transmitting
efficiency (capacity increasing) is expected. Activating all antennas may not be a good
solution for system capacity maximization when a system with a per antenna power
cost is considered [4]. Efficient methods (algorithms) of radio resource allocation are
obviously required whereby could be possible to identify the channel state information
at the receiver and feeding back to the transmitter in order to increase more power
transmission for the suitable group of transmitting antennas thereby improving the

transmission performance which is the expected.
The integration of these new radio concepts, such as massive MIMO, ultra
dense networks, moving networks, and device-to-device, ultra reliable, and massive
machine communications, will allow 5G to support the expected increase in mobile
data volume while broadening the range of application domains that mobile
communications can support beyond 2020. [5]. The future systems expect to offer a
great potential for a design of high speed short range wireless communications which
fully support high data streaming capacity. This can be achieved by exploiting both
spatial and multipath diversity via the use of MIMO OFDM system and proper coding
techniques [8].
1.1

Thesis outline
The highlighted outline of this thesis is as follows:
-

The traditional resource allocation techniques are analyzed and some
methods are developed in order to get better performance of a target wireless
systems;


-

A new algorithm is proposed for the power allocation to the adjacent subcarriers of an OFDM based CR user;

-

The new algorithm is compared with the algorithm in which a certain group
of adjacent sub-carriers is nulled for getting less interference to the PU and
with of the Scheme A of Bansal referred in the further sections;

-

The adjacent subcarriers are deeply analyzed as they are good candidates for
the interference caused by the CR users to the PU;

-

The capacity is maximized to obtain good performance of the wireless
system keeping the interference below certain threshold;

-

The interference is minimized subjected to the capacity requirements, i.e the
QoS.

1.2

Thesis organization
The rest of the thesis is organized as follows:

Chapter 2: Presents the overview of Wireless Communication Systems and the
Cognitive Radio;
Chapter 3: Presents the Resource Allocation Techniques for Wireless Communication
Networks
Chapter 4: Presents the Power allocation for an OFDM based Cognitive Radio - Case
Study – (Sub-Optimal Scheme) and the final results.
At the end of this thesis the conclusion, some recommendations for future work and
references are presented.
1.3

Chapter Conclusion
In this chapter we gave an overview of the actual scenario of wireless

communication systems in where we have seen that the increase of users and the better
quality also needed in the future systems make the Cognitive Radio systems to gain


more acceptance as one the most powerful concept to be applied in near future as is
also planned to implement in 5G.
The structure of this thesis and its development were also presented in this
chapter.


CHAPTER 2 - Cognitive Radio Networks
2.1

Introduction

Today the wireless communication represents a new way used by people to
communicate in long distances over the world. Communication provides the senses for
ships on the high seas, aircraft in flight, and rockets and satellites in space.
Communication through a wireless telephone keeps a car driver in touch with the
office or home miles away [2].
Today the wireless communication has conquered the world by its advantage of
making possible the communication by simply taking out the traditional and well
known wires. Even though devices can communicate without using physical wires, the
wireless communication also has some disadvantages. The wireless channel represents
one of the big challenges when designing the wireless devices. In other hand the
spectrum as a natural resource that can’t be increased, the demand of users make it

becomes a scarce resource. To find solutions to overcome this scenario is of high
interest for the wireless to investigate in this field. Cognitive radio is a new key for a
better use of the spectrum as it’s a form of wireless communication in which a
transceiver can intelligently detect which communication channels are in use and
which are not, and instantly move into vacant channels while avoiding occupied ones.
This optimizes the use of available radio-frequency (RF) spectrum while minimizing
interference to other users.
2.2

Evolution of Wireless Communication Systems

Today the wireless systems represent important part of our daily lives.
Refereeing to the development of wireless systems it is of a big importance to
understand how did they became spread in large dimensions as they are in nowadays

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what will make also possible that they keep developing as facilitate the daily life of
connected people.
The first wireless networks were developed in the pre-industrial age. These
systems transmitted information over line-of-sight distances (later extended by
telescopes) using smoke signals, torch signaling, flashing mirrors, signal flares, or
semaphore flags. An elaborate set of signal combinations was developed to convey
complex messages with these rudimentary signals. Observation stations were built on
hilltops and along roads to relay these messages over large distances. These early
communication networks were replaced first by the telegraph network (invented by
Samuel Morse in 1838) and later by the telephone. In 1895, a few decades after the
telephone was invented, Marconi demonstrated the first radio transmission from the
Isle of Wight to a tugboat 18 miles away, and radio communications was born. Radio

technology advanced rapidly to enable transmissions over larger distances with better
quality, less power, and smaller, cheaper devices, thereby enabling public and private
radio communications, television, and wireless networking.
Early radio systems transmitted analog signals. Today most radio systems
transmit digital signals composed of binary bits, where the bits are obtained directly
from a data signal or by digitizing an analog voice or music signal. A digital radio can
transmit a continuous bit stream or it can group the bits into packets. The first network
based on packet radio, ALOHANET, was developed at the University of Hawaii in
1971. Packet radio networks have also found commercial application in supporting
wide-area wireless data services. These services, first introduced in the early 1990’s,
enable wireless data access (including email, file transfer, and web browsing) at fairly
low speeds, on the order of 20 Kbps. The market for these wide-area wireless data
services is relatively flat, due mainly to their low data rates, high cost, and lack of
“killer applications”. Next-generation cellular services are slated to provide wireless

2


data in addition to voice, which will provide stiff competition to these data-only
services.
As technology continued to develop, in 1970, Professor Norman Abramson
created Aloha net, the forerunner for Ethernet and future wireless signals. His
invention made use of radio signals for easier data transmission through high-speed
packets [6].In 1985 the Federal Communications Commission enabled the commercial
development of wireless LANs by authorizing the public use of the Industrial,
Scientific, and Medical frequency bands for wireless LAN products.
Wired Ethernets today offer data rates of 100 Mbps, and the performance gap
between wired and wireless LANs is likely to increase over time without additional
spectrum allocation [7].
We can’t refer to wireless today without touching the cellular telephone system.

Clearly the cellular telephone system became one of the most successful applications
of wireless system communication. This is because of the billions of subscribers that
the cellular telephones systems are designed to serve and also because of the fact that
the subscribers are mostly attracted by other services incorporated in this systems such
as voice, video and data. At the beginning the initial systems could not provide their
service to many users. These systems used a central transmitter to cover an entire
metropolitan area. This inefficient use of the radio spectrum coupled with the state of
radio technology at that time severely limited the system capacity: thirty years after the
introduction of mobile telephone service the New York system could only support 543
users [7].
A solution to the capacity problem emerged during the 50’s and 60’s when
researchers at AT&TBell Laboratories developed the cellular concept [7]. Cellular
systems exploit the fact that the power of a transmitted signal falls off with distance.
Thus, the same frequency channel can be allocated to users at spatially-separate
3


locations with minimal interference between the users. Using this premise, a cellular
system divides a geographical area into adjacent, non-overlapping, “cells”. Different
channel sets are assigned to each cell and cells that are assigned the same channel set
are spaced far enough apart so that interference between the mobiles in these cells is
small. Each cell has a centralized transmitter and receiver (called a base station) that
communicates with the mobile units in that cell, both for control purposes and as a call
relay. All base stations have high-bandwidth connections to a mobile telephone
switching office (MTSO), which is connected to the public-switched telephone
network (PSTN). The handoff of mobile units crossing cell boundaries is typically
handled by the MTSO, although in current systems some of this functionality is
handled by the base stations and/or mobile units.
The second generation of cellular systems are digital. In addition to voice
communication, these systems provide email, voice mail, and paging services.

Unfortunately, the great market potential for cellular phones led to a proliferation of
digital cellular standards. Today there are three different digital cellular phone
standards in the U.S. alone, and other standards in Europe and Japan, none of which
are compatible. The fact that different cities have different incompatible standards
makes roaming throughout the U.S. using one digital cellular phone impossible. Most
cellular phones today are dual-mode: they incorporate one of the digital standards
along with the old analog standard, since only the analog standard provides universal
coverage throughout the U.S.
Commercial satellite communication systems are now emerging as another
major component of the wireless communications infrastructure. Satellite systems can
provide broadcast services over very wide areas, and are also necessary to fill the
coverage gap between high-density user locations. Satellite mobile communication
systems follow the same basic principle as cellular systems, except that the cell base

4


stations are now satellites orbiting the earth. Satellite systems are typically
characterized by the height of the satellite orbit, low-earth orbit (LEOs at roughly 2000
Km. altitude), medium-earth orbit (MEOsat roughly 9000 Km. altitude), or
geosynchronous orbit (GEOs at roughly 40,000 Km. altitude). The geosynchronous
orbits are seen as stationary from the earth, whereas the satellites with other orbits have
their coverage area change over time. The disadvantage of high altitude orbits is that it
takes a great deal of power to reach the satellite, and the propagation delay is typically
too large for delay-constrained applications like voice. However, satellites at these
orbits tend to have larger coverage areas, so fewer satellites (and dollars) are necessary
to provide wide-area or global coverage.
A natural area for satellite systems is broadcast entertainment. Direct broadcast
satellites operate in the 12 GHz frequency band. These systems offer hundreds of TV
channels and are major competitors to cable. Satellite-delivered digital radio is an

emerging application in the 2.3 GHz frequency band. These systems offer digital audio
broadcasts nationwide at near-CD quality [7]
2.3

Software Defined Radio

Digital radio is the use of digital technology to transmit and/or receive across the
radio spectrum. It’s a digital transmission by radio waves, including digital
broadcasting, and especially to digital audio radio services. This term is also applied to
radio equipment using digital electronics to process analog radio signals. The design of
a conventional digital radio in Figure 2.1shows a block diagram of a generic digital
radio, which consists of five sections:
- The antenna section, which receives (or transmits) information encoded in radio
waves;
- The RF front-end section, which is responsible for transmitting/receiving radio

5


frequency signals from the antenna and converting them to an intermediate frequency
termed by IF;
- The ADC/DAC section, which performs analog-to-digital/digital-to-analog
conversion;
- The digital up-conversion (DUC) and digital down-conversion (DDC) blocks, which
essentially perform modulations of the signal on the transmitting path and
demodulation of the signal on the receiving path;
- The baseband section, which performs operations such as connection setup,
equalization, frequency hopping, coding/decoding, and correlation, while also
implementing the link layer protocol.

Figure 2.1 Schematic block diagram of a digital radio [8]
Software-defined radio refers to technologies wherein these functionalities are
performed by software modules running on FPGAs, DSP, GPP, or a combination of
them. This enables programmability of both DDC/DUC and baseband processing
blocks. These features make possible that the operation characteristics of the radio,
such as coding, modulation type, and frequency band, can be changed at will, simply

6


by loading new software that better fits with the requirements. Also multiple radio
devices using different modulations can be replaced by a single radio device that can
perform the same task.
2.4

Cognitive Radio Networks
The next generation of wireless cellular networks aims to support various

multimedia services with different quality of service (QoS) requirements. Due to the
spectrum scarcity and the limited power budget, an efficient radio resource allocation
is therefore mandatory for the next generation of wireless networks.
2.4.1 Spectrum usage
A large portion of the assigned spectrum is used sporadically as illustrated in
Figure 2.2where can be seen the signal strength distribution over a large portion of the
wireless spectrum. The spectrum usage is concentrated on certain portions of the
spectrum while a significant amount of the spectrum remains unutilized. According to
FCC [9], the temporal and geographical variations in the utilization of the assigned
spectrum range is from 15% to 85%. Although the fixed spectrum assignment policy
generally served well in the past, there is a dramatic increase in the access to the
limited spectrum for mobile services in the recent years. This increase is straining the

effectiveness of the traditional spectrum policies. The limited available spectrum and
the inefficiency in the spectrum usage necessitate a new communication paradigm to
exploit the existing wireless spectrum opportunistically [10]. Dynamic spectrum access
is proposed to solve these current spectrum inefficiency problems.

7


Figure 2.2 Spectrum utilization [9]
2.4.2 Cognitive radio concept
Cognitive radio integrates machine perception software into wireless systemsradio nodes and networks. Radios today are evolving from awareness (e.g., of location)
toward cognition: the self-aware radio autonomously learns helpful new wireless
information access and use behaviors, not just sensing the RF spectrum but also
perceiving and interpreting the user in the user’s environment via computer vision,
speech recognition (speech-to-text), and language understanding [11].
CR is an intelligent radio that can be programmed and configured dynamically.
The transceiver is designed to use the best wireless channels in its vicinity. Such a
radio automatically detects available channels in wireless spectrum, then accordingly
changes its transmission or reception parameters to allow more concurrent wireless

8


communications in a given spectrum band at one location. This process is a form of
dynamic spectrum management.
Mainly the objective of the cognitive radio is to obtain the best available
spectrum through cognitive capability and re-configurability. From Figure 2.3 can be
seen the spectrum holes concept.

Figure 2.3 Spectrum holes concepts [9]

The cognitive radio functionality requires at least the following capabilities:
Flexibility and agility: the ability to change the waveform and other radio operational
parameters on the fly. In contrast, there is a very limited extent that the current MCMR can do this. Full flexibility becomes possible when CRs are built on top of SDRs.
Another important requirement to achieve flexibility which is less discussed is
reconfigurable or wideband antenna technology.

9