Luận văn thạc sĩ xây dựng các thuật toán phân bố tài nguyên vô tuyến cho mạng vô tuyến nhận thức

MINISTRY OF EDUCATION AND TRAINING HANOI UNIVERSITY OF SCIENCE AND TECHNOLOGY HELIO AUGUSTO MUZAMANE DEVELOPMENT OF RADIO RESOURCE ALLOCATION METIIODS FOR COGNITIVE RADIO NETWORKS

MINISTRY OF EDUCATION AND TRAINING

HANOI UNIVERSITY OF SCIENCE AND TECHNOLOGY

HEIJO AUGUSTO MUZAMANE

DEVELOPMENT O¥ RADLO RESOURCE ALLOCATION

METHODS FOR COGNITIVE RADIO NETWORKS

MASTER OF SCIENCE

TIA NOI — 2016

MINISTRY OF EDUCATION AND TRAINING

HANOI UNIVERSITY OF SCIENCE AND TECHNOLOGY

HELIO AUGUSTO MUZAMANE

DEVELOPMENT OF RADIO RESOURCE ALLOCATION METIIODS

FOR COGNITIVE RADIO NETWORKS

MAJOR : TI

COMMUNICATIONS

MASTER THESIS IN SCIENCE

SCIENTIFIC SUPER VISOR

Associate Prof Eng Nguyễn VănĐức

HàNội 2016

List of Figures

Figure 2.1 Schematic block diagram of a digital radio [8] - 6

Figure 2.3 Spectrum holes concepis [9] - - - 9 Tigure 2.4 Infiastructurc-based CR network architecture |B| 11 Figure 3.1 Structure and spectral characteristic of multicarrier transmission system

Figure 3.6 NR.x NF MIMO system |12| soree —

igure 3.7 Modal decomposition when CSI is available at the transmitter side ]12]

26

Figure 3.8 The r virtual STSO charmels obtamed trom the modal decomposition of a

'.MIMO channel [12] HH HH HH0 mg ga, _— _—- Figure 3.9 Water-filling power allocation algorithm 29 Figure 3.10 Cooxistence of PU and SU seeruric 35 Figure 4.1 Co-existance of PL 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-carries se.52 Kigure 4.4 ‘The power profile structure for the adjacent sub-carriers

Figure 4.8 Transmission capacity of the CR user Vs SNR for the 128 sub-carriors 58 igure 4.9 Power Allocation for 64 sub-carriers with 10 chosen adjacent sub-

List of Acronyms

ALOHA | Additive Links On-line Hawaii Arca

BIS Base Station Transceiver System

CR Cogmiive Radio

CSL Channel State Information

DET Direct Fourier Transform

DSP Digital Signal Processors

ToC Federal Communications Commission

FCC Federal Communications Commission

FPGA | Field Programmable Gate Arrays

TFT Taverse Fourier Transform

ISL Inter-Symbol Interference

18M Industrial, Scientific, and Medical

iti

LAN Local Area Network

LEO Low-Barth Orbil

LTE ‘Long Term Evolution

MA Margin Adaplive

‘Mbps ‘Mega-bits per second

MO-MR | Mului-Chanrel Multi-Radio

‘MEO ‘Medium-Harth Orbit

MIMO | Multiple Input Multiple Output

MRC ‘Maximum-Ratio Combing

MISO | Mobile Telephone Switching Office

mW Milli-walis

GEDM | Orthogonal Frequency for Division Multiplexing

OFDMA | Orthogonal Frequency Division for Multiple Access

PSD Power Spectral Density

PSIN _| Public-Switched Telephone Network

PU Primary User

Qos Quality Of Service

SISO Single input Single Output

SNR Signal to Noise Ratio

Acknowledgements

I would like to thank my scientific supervisor, Associate Prof Ing NeuyénVan Puiu, and Phd Nguyéa Tién Hoa for their kindly support during the

course of this thesis

1 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, T could not have had the opportunity to even starL my studies

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 algorithams (or 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

clusmel capacity is maximized keeping the interference introduced lo PU below a

certain threshold and fthenmore the interference is also taken to be the cost

[Imetien Lo minimize keeping the QoS in an acceptable range

Cognitive Radio systems are designed to be able to occupy the portion of the unused frequoncy 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 accoplable operating porformance Starling from (he basic principles, such

as power control and multiple access, coverage moving to the optimization

techniques for resource allocation, including formulation and analysis [1] Water filing algorithm is proposed to solve the problem pf resource allocation as iL

allocates much amount of power to the sub-chamels experiencing relatively high

SNR than others Along with the water filling scheme, a different algorithnn is

proposed to allocate the power for the group of adjacent sub-camicrs as they play a significant role in terms of interference to the PU's bands ‘I'he performance of all these algorittuns is verified using MATLAB simulation malar: comparison with

the other algorithms previously studied by different authors

vii

24 Cognitive Radio Networks

3.22 OFDMA - - 16

3.3 Resource Allocation Using Multi-Antenna Techniquss - 16

331 Mulu-Antenna Technique càoersreroeseeoe TỔ 3.3.2 Beam-ferming Techniques (at the receiver} 1 3.3.3 Bpabial Multiplexing Techniques cccieirririerirroree 19

3.4 Adaptive Resouree Allocation Techniques for OFDMA Based Wireless NGEWOTS, cu ch Hưng Hư Hư Hư Hư H0 mg mg rrdaegree 29

30 3.4.2 Rate Adaptive

3.5 Optimal Resource Allocation Technique for OFDM based Cognitive Radio

NEEWOLS, coi Hư HH ng thue "-

3.5.2 MIMO-OFDM based Downlink Cognitive Radio Network 32 3.5.3 System Modcland Problem Ñtalemenl scoseseoo.38 3.5.4 Chamelgaim cà neennienieerirrinirrrerarrsoa24

443 Problem formulation (Case 1)

4S — Goometrie Progression Technique for power allocation - 30

CHAPTER 1 - Introductian

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 commecting 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 wiroless

communications capabilities (up to and including 4G) are evolving into new forrns that

rely increasingly on local communications over short distances (eg small cells or

milluneter wave lmks) 4G LTE networks now incorporate small cells to mercase 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, Llence 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

lo 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 expevied 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 (hrough cognilion which they will be

endowed) for ctficient operation with lunited centralized control It’s also descnbed the

case where at the base station is employed a MIMO system and the transmitting

officieney (capacity increasing) is expected Activaling all antermas may not be a good

solution for system capacity maximization when a system with a per antenna power

cost is considered [4] Ffficient methods (algorithins) 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

imachine communivations, will allow 5G to support the expected increase in mobile data volume while broadening the range of application domains that mobile

communications can supporl beyond 2020 [S] The future systems expect Lo offer a

great potential for a design of high speed short range wireless communications which fully support high data streaming capacity ‘his oan be achieved by exploiting both spatial and multipath diversily via the use of MIMO OFDM system and proper ending 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 adjavent sub- carriers of an OFDM based CR user;

+ The new algorithm is compared with the algorithm in which a certain group

of adjacent sub-carricrs is mulled for getting less interference to the PU and with of the Scheme A of Bansal referred in the further sections:

+ The adjacent subearricrs are deeply analyzed as hey 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, ie the

Qos

1.2 Thesis organization

The resi of the thesis is organized as follows:

Chapter 2: Presents the overview of Wircless Communicalion 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 Copnitive Radio - Case Study — (Sub-Oplimal Scheme) and the final results

At the end of this thesis the conclusion, some recommendations for firhire work and

tolerances are presented

13 Chapter Conclusion

In this chapter we gave an overview of the actual scenario of witeless 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 SƠ

The structure of this thesis and its development were also presented in this chapter.

CHAPTER 2 - Cognitive Radio Networks

21 Tntrodurtion

Today the wireless communication represents a new way used by people to communicate in long distances over the world Commumication 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 ils advantage of

makmg possible the communication by simply taking out the traditional and well

imown wires Even though devices can communicate without using physical wires, the

wireless communrigalion also has some disadvanlages The wircloss charmel represents one of the big challenges when designing the wireless devices In other hand the

spectrum as 4 natural resource that caw’ be increased, the demand of users make it

becomes a scarce resourec 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 optimives 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 Relerceing lo the development of wircless systems il is of a big importance to

understand how did they became spread in large dimensions as they are in nowadays

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 transmilled information over line-of-sight distances (lator extended by

telescopes) using smoke signals, torch signaling, flashmg mirrors, signal flares, or

semaphore flags An elaborate set of signal combinations was developed to convey

complex messages wilh these rudimentary signals Observalion stalions were built on hilltops and along roads to relay these messages over large distances ‘hese 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 bom 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, iclevision, and wireless networking,

Farly radio sysiems transmitied armilog signals Today most radio sysiems 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, ALOITANET, was developed at the University of Ilawaii in

1971 Packet radio networks have also found commercial application in supporting wide-area wireless data services ‘Ihese 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 markel 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

bo

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

ercaled Afoba net, the forerunner for Ethsrnet 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 Tndustrial,

Scientific, and Medical frequency bands for wireless LAN products

Wired Lithernets today offer data rates of 100 Mbps, and the performance gap between wired and wireless LANs is likely to increase over time without additional

as voice, video and data, At the beginnizy the inilial 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 thet 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 durmg the 50°s and 60’s when

researchers at AT&TBell Laboratorics developed the celhwar concept 17] 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 al, spatially-scparate

locations with minimal interference between the users Using this premise, a cellular system divides a geographical area into adjacent, non-overlapping, “cells” Different chanel sets are assigned to each cell and cells thal are assigned the same channel set arc spaced far cnough apart so that interference between the mobiles in these cclls is small Each cell has a centralized transmitter and receiver (called a base station) that

commuricates with the mobile ums im that cell, both for control purposes and a3 a call

relay All base stations have high-bandwidth connections to a mobile telephone

switching office (MTSO), which is comected to the public-swilched Lelephone network (PSTN) The handoff of mobile units crossing coll boundaries is typically handled by the MISO, although in current systems some of this functionality is

handled by the base stations and/or mobile units

The second generation of celhilar systems are digital In addition te voice

communication, these systems provide email, voice mail, and paging services Unfortunately, the preat market potential for cellular phones led ta a proliferation of shgital cellular standards Today there are three different digital cellular phone

standards in the LS alone, and other standards in Europe and Japan, none of which

aré compatible The fact thal 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 Bll 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

stations are now satellites orbiting the earth, Satellite systems are typically

characterized by the height of the satellite orbit, low-earth orbit (LIOs at roughly 2000

Kin altitude), medium-earth orbit, (MEOsal roughly 9000 Kin 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 thal it takes a great deal of power to reach the satellite, and the propagation delay is typically

too large for delay-constramed applications like voice However, salellites al these

orbits tend to have larger coverage areas, sơ 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 aystems 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

broadeasts rationwide al near-CD quality [7]

23 Software Defined Radio

Digital radio is the usc of digital technology to transmit and/or reecive 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 ‘he design of

a conventional digital radio in Figure 2.1shows a block diagram of a generic digital

of five sections:

tadio, which consis

- The antenna section, which receives (or transinits) information cneoded in radio waves;

- The RF front-end section, which is responsible for transmitting/teceiving radio

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) blacks, 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, equilivation, frequency hopping, codiny/decoding, and correlation, while also implementing the link layer protocol

kuteana

BF Front Bad

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 crables programmability of both DDCMUC and bascband processing blocks ‘hese features make possible that the operation characteristics of the radio,

such a3 coding, modulation type, and frequency band, can be changed at will, simply

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 supporl varions 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 seeu 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 tomporal 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 inorcase is straining the effectiveness of the traditional spectrum policies ‘I'he limited available spectrum and

the inefficiency in the spectrum usage necessitate a new communication paradigm ta

exploit the existing wireless spectrum opportunistically [10] Dynamic spectrum access

is proposed to solve these current spectrum inefficiency problems

2.4.2 Cognitive radio concept

radio 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 wi

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 MC-

MR 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

Sensing: which is the ability to observe and measure the state of the environment, including spectral occupancy Sensing is necessary if the device is to change its

operation based on ils current kuowledge of RF environment,

Learning and adaptability: the ability to analyze sensory input, to recognize patterns, and modify inlornal operational bohavior based on the analysis of an enviroument

The base of CR network architecture is shown in the Figure 2 4and is classified inta two main groups; ‘The first is the called primary nebwork (with the licensed users) and the second called secondary network or CR network (with the unlicensed users)

The primary user has exclusive right to a certain spectrum band and the CR network does not have a license to operate in a certain spectrum band The access of

the band by the CR is allowed only iti an opportunistic marmer

For the primary uctwork, the following, are (he basic components [8]

Primary user: A primary user has a license to operate in a cortain spectrum band This access can only be controlled by the primary base station and should not be affected by the operations of any other CR users Primary users do not need any modification or

additional functions for coexislence with CR base stalions and CR users.

Figure 2.4 Infrastructure-based CR network architecture [8]

Primary base station: A primary base station is a fixed infrastructure network

component that has a spectrum license, such as a BTS ina cellular system In principle,

the primary base station does not have any CR capability for sharing spectrum with CR users

The basic elements of the CR network are defined as follows [8]

CR user: A CR user has no spectrum license Hence, additional functionalities are

required to share the licensed spectrum band In infrastructure-based networks, the CR

users may be able to only sense a certain portion of the spectrum band through local

observations They do not make a decision on spectrum availability and just report

their sensing results to the CR base station,

CR base station: A CR base station is a fixed infrastructure component with CR

capabilities It provides single-hop connection without spectrum access licenses to CR

users within its transmission range and exerts control over them Through this

connection, a CR user can access other networks It also helps in synchronizing the

sensing operations performed by the different CR users The observations and analysis

1

performed by the latter are fed to the central CR base station so that the devision on the

spectrum availability can be made

Spectrum broker: A spectcum broker (or scheduling server) is a central network entity

that plays a role in sharing the spectrum resources among different CR networks It is

nol direelly engaged in spectrum sensing Tt just manages the spectrum allocation

among different networks according to the sensing information collected by each

network,

2.5 Chapter Conclusion

A cognitive radio is a radio that can be programmed and configured

dynamically 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

communications in a given spectrum band at one location This make possible that CR

systems become strong candidates for the new vision of wireless communication where

the inercase of subscribers is one of the main goal.

CHAPTER 3 - Resource Allocatian Techniques for Wireless

Communication Networks

For the better performance of any wireless communication system it is

important to predic techniques that allow such systems Lo maximize ils ellicieney by the resource management In this, basic concepts, optimization tools and other techniques are required The best possible performance of any wireless system can be approximately reached by following innumerous tecluiques as we can see from the development of the wireless systems in which many techniques were implemented to

achieve the wireless desired QoS

3.2 Multiple Access Methods

One of the most important challenges of any wircluss system is to provide

coverage to maximum possible users As is known the much greater the number of

users lhe greater power degradation at the transmitter TI

simple and similar to when

we have a certain amount of water to satisfy a group of people, it is seon that as the number of people get much greater, fewer become the quantity of water to be distributed for each person The main goal is making true that many devices can communicate simultaneously A system which can provide a service with possibility of incorporating multiple accesses is what is needed to solve the problem of multiple accesses,

A channel-access schemte is based on a multiplexing method that allaws several

data streams or signals to share the same communication channel or physical medium

chamnel is called delay spread It’s the difference between the arrival time of the earliest multipath component and the arrival time of the latest multipath component The delay spread affects the TST because if the symbol duration is long enough compared to the delay spread (typically 10 times as big, would be good cnough), one can expect an equivalent ISI-free channel Coherence bandwidth is related to the

inverse of the delay spread The shorter the delay sproad, the larger is the coherence

fa) Basic structure of multicarrier aysiem

(b) Spectral characleristic of mullicarrier system

Figure 3.1 Structure and spectral characteristic of multicarrier transmission

system [12]

To solve the problem caused by the multipath channel, the communication

systems have been developed in such a way that this TST should be reduced The

OFDM technique came mainly with this purpose The simplest form of multicarrier

modulation divides the data stream into multiple sub-streams to be transmitted over

different orthogonal sub-channels centered at different subcarrier frequencies The

14

number of sub-streams is chosen to make the symbol time on each sub-stream much preater than the delay spread of the charmel or, equivalently, to make the sub-stream bandwidth Jess than the channel coherence bandwidih This insures that the sub- streams will not experience significant IST |7| One of the important keys of the OFDM

is the DFT and IFT and shown im the Figure 3.2

Higure 3.2 Structure and spectral characteristic of OFDM transmission scheme

7]

15

32.2 OFDMA

In OFDMA systems, the multiple user signals are separated in the time and/or fiequeney domains Typically, a burst in an OFDMA system will consist of severat OFDM symbols The subcarriers and the OFDM symbol period are the finest allocation units in the frequency and time domain, respectively Hence, multiple users are allovated different slols in the time and frequeney domain, i¢., different groups of subvaniers and/or OFDM symbols are used for transmitting the signals to/from multiple users The following figure illustrates ant example wherein the subcarriers in

an OFDM symbol are represented by arows and the lincs shown at different times represent the different O1'M symbols ‘Three users are considered and it’s also shown how the resources can be allocated by using the different subcarriers and OFDM symbols,

‘OFDM - TDMA? SUFDM - FDMA> OFDM - CDMA>

Figure 3.3 Multiple access techniques used in OFTYM systems [12]

3.3 Resource Allocation Using Multi-Antenna Techniques

3.3.1 Multi-Antenna Techniques

Compared to a conventional single antenna system, the channel capacity of a

multiple antenna system with N> transmit and Np receive antennas can be increased by

the factor of min(W.; NJ, without using additional transmit power or spectral bandwidth [12]

The use of multiple antennas at hoth the transmitter and the receiver can simply

be secu as a tool io further improve the signal-to-noise/interference ratio and/or achieve

additional diversity against fading, compared to the use of only multiple receive

antennas or multiple transmit antennas However, in case of multiple antennas at both

the transmitter and the receiver there is also the possibilily for so-called spatial multiplexing, allowing for more efficient utilization of high signal-to- noise/nterference ratios and significantly higher data rates over the radio interface

[131

3.3.2 Beam-forming Techniques (at the receiver)

The beam-forming at the receiver consists of allocating multi antennas at the

receiver side Therefore the transinitter side is equipped with a single antenna what

makes this system known by SIMO

We consider a SIMO system with a single transmit antenna, N7-1 and multiple antennas at the receiver, Nx> 1 antennas where, cN

oP

The Figure 3.4 illustrates the basic principle of linear combining r,,

received at the Np different antennas, with the received signals being multiplied by complex weight wi wij,before heing added together In vectar notation this linear receive-antenna combining can be expressed as:

31

Figure 3.5 Linear receive antenna combining [13]

Both the equation and the above figure represent the linear receive antennas combining in general Different specific antenna-combining approaches then differ in the exact choice of weight vector

Assuming that the transmitted signal is only subjected to the non-frequency-

selective fading and noise (white noise), i.c there is no radio-channcl time dispersion,

the signals received at the different antennas in Figure 3.5 can be expressed as:

Where + is the received signal at the different antennas, sis the transmitted

signal, fis the vector which consists of Nz complex channel gains, and the » is the

veclor consisting of the noise impairing the signals received at the different anlermas

To maximize the signal-to-noise ratio after linear combining, the weight vector

w should be selected as:

that is, apply higher weights for stronger received signals

‘The beam-forming at the receiver is the most applicable in telecommunication

systems because it holds the facility knowing the channel state information at the

receiver more that at the transmitter The use of multiple reecive antemas may increase the post-combiner signal-to-noise ratio in proportion to the number of receive antennas

II

3.3.3 Spatial Multiplexing Techniques

‘The wireless communication environment is well known as being very hostile

The signal transmitted over a wireless communication link is susceptible to fading

19

(severe fluctuations in signal level), co-channe) interference, dispersion effects in time and frequency, path loss effect, ete on top of these woes, the limited availability of

bandwidth possess a significant challenge in designing a system (hal provides higher

spectral cfficicney and higher quality of link availability at low cost Multiple antenna

systems are the current trend in many of the wireless technologies that is essential for

their performance Multiple Tnpul, Multiple Output systems improve the spectral efficiency and offers high quality links when compared to traditional Single Input

Single Output systems [14]

3.3.3.1 Useful Matrix Theory and Channel Capacity

Let's consider a point-to-point MIMO channel with M,transmit antennas and M,

pa receive antennas as in the Figure3.6 The matrix ff ec'***has a singular vahie

decomposition (SVL), represented as [121[151

N=LEV" 3.4

Where UcC**đằand ƑcC”*zare unitay matrices, and 3cC**?”?is a

reclangular mulrix, whose diagonal clements are nom-negalive real aumbers and whose off-diagonal elements are zero ‘Ihe diagonal elements of Dare the singular values of

the matrix Ll, denoting them by o,,.03. TƯ > where N,, —min(N,,N,) In fact,

assume that ứ, oy,ằ that is, the diagonal elements of â are the ordered

singular values of the matrix //, the rank of H/ corresponds to the number of non-zero singular values (Le rank(H)<N'q,,) In case of Nj, —N,, SVD in Hquaton 2.4 can be

expressed as

corresponding to the maximum possible nonzero singular values, and X„ cŒ7

now a single matrix

Since N,,, singular vector in U7, are of lengih W,, there always exasl (Ny-Nin)

singular vectors such that | f2 „ |is unitary In case Nyig=Np SVD in

Bqualiơn 3⁄4 can be expressed as

Where i oC%"™"is composed of WV, right-singular vectors Given SVD of

H,, the [ollowing Rigen-decomposition holds

AH” =UEE"U" =QAQ" 3.7

Where Q=U such that @2”2— 7y, and AcC”» is a diagonal matrx with is

diagonal elements given as:

38

As the diagonal elements of in Liquatiom 3.7 are Higen values {a,}% , iquation

2.8 indicates that the squared singular values {7°}"* for H are the Higen values of the Lermitian symmetric matrix HA”, or similarly, of H”H

For a non-Hennilian square matrix 77 €€"°" (or non-symunetric real matrix), the

igen decomposition is expressed as:

rr re

or equivalent

Where {Fcc are the right-side eigenvectors corresponding to Eigen

values in Ay €C™™ In the cquation linear independenes of the cigenvectors is

assumed Comparing Equation 3.7 to Equation 3.9, it can be seen that the eigenvectors

of a non-Ilermitian matrix #7cC™" are not orthogonal, while those of a Ilermitian

matix HH™ are orthonomnal (Le., @'=@*),

Meanwhile, the squared Frobenius norm of the MIMO channel is mterpreted as

a total power gain of the channel, that is,

pre =H) = SS a, | HH“ ậm

Using Equation 3.7, the squared Frobenius norm in Equation 2.9 can also be Topresorited in various ways as follows:

22

In deriving Equation 3.9, we have used the fact that the Frobenious norm of a

matrix does not change by multiplication with a unitary matrix

3.3.3.2 Deterministic MIMO Channel Capacity

Figure 3.6V'K x NT MIMO system |12|

Lel’s consider a MIMO system with Njtransmil and Ngreccive antennas, ax in

the Figure 3.6 It’s considered a narrowband time-invariant wireless chamel

33

vector xe”, which is composed of WV, independent input symbols x, x,

circular symmetric complex Gaussian It can be noted that the noise vector z is referred

to as circular symmetric when ¢’’s has the same distribution as z for any @ ‘The

autocorrelation of transmitted signal vector is defined as

We note that 7r(2,)— Np when the tansmission power for each trausmil arlermia

is assumed to be 1

3.3.3.3 Channel Capacity when CSI is known

The capavily of a deterministic channel is defined as

C= max A(x, y) bits / chanel use 3.15

In which /(x) is the probability density function (PDE) of the transmit signal vectorx, and /(s;»)is the mutual information of random vectors xand y Namely, the channel capacity is the maximum mutual information that can be achieved by varying the PDF of the transmit signel veetor From the fundamental prineiple of the information theory, the mutual information of the bve continuous random vector, x and

» , is given as

IG y= HO)- Ay) 316