Fairness and resource allocation in device-to-device wireless regional area network

in Device-to-Device Wireless

Regional Area Network

Device-to-Device Wireless Regional

Area Network

PROEFSCHRIFT

ter verkrijging van de graad van doctor

aan de Technische Universiteit Delft,

op gezag van de Rector Magnificus prof. ir. K.C.A.M. Luyben,

voorzitter van het College voor Promoties,

in het openbaar te verdedigen

op maandag 6 oktober 2014 om 10.00 uur

door

Huaizhou SHI

Copromoter: Dr. R. R. Venkatesha Prasad

Samenstelling promotiecommissie:

Rector Magnificus, voorzitter

Prof. dr. ir. I.G.M.M. Niemegeers, Technische Universiteit Delft, promoter

Dr. R. R. Venkatesha Prasad, Technische Universiteit Delft, co-promoter

Prof. dr. Vijay K. Bhargava, The University of British Columbia

Prof. dr. ir. N. H. G. Baken, Technische Universiteit Delft

Prof. dr. ir. S.M. Heemstra de Groot, Technische Universiteit Eindhoven

Prof. dr. Luis Mu˜noz, Universidad de Cantabria

Prof. dr. ir. C. H. Slump, Universiteit Twente

ISBN number: 978-94-6259-320-6

Copyright c 2014 by H.SHI

All rights reserved. No part of the material protected by this copyright notice may be reproduced or utilized in any form or by any means, electronic or mechanical, including photocopying, recording or by any information storage and retrieval system, without the prior permission of the author.

With the rapid development in wireless devices, applications and networks, radio frequencies have become scarce resources. Therefore, it is critical to make efficient use of the radio frequencies. A promising way to solve this problem is the use of cognitive radio (CR). In CR, radio frequencies are allocated to licensed users, also called primary users (PUs) and these frequencies can be reused by unlicensed users, also called secondary users (SUs) without affecting the PUs. This way, the spectrum utilization can be increased significantly. Cognitive radio networks (CRNs) are the networks using CR technology, based on which the IEEE 802.22 working group has developed three standards and there are several other on-going projects. IEEE 802.22 aims at wireless regional area networks (WRANs) and broadband services in rural areas. TV channels are employed due to their underutilization in rural areas and good performance for long distance communication. A cellular architecture is adopted with large cells (up to a radius of 100 km). Each cell has a base station (BS) and multiple customer premises equipment (CPE). IEEE 802.22 is a significant milestone in CRN standardization.

However, WRANs have severely limited capacity due to their single operating channel, point-to-multipoint topology and the requirement for regular quiet periods (QPs) to do spectrum sensing. Moreover, the spectrum sharing amongst CPEs is not addressed by IEEE 802.22, and this influences the channel utilization directly. This thesis explores ways to enhance the network capacity by spectrum allocation. Another critical issue is also identified and addressed regarding resource allocation problems, which is the fairness issue. Many resources can be found in wireless networks, e.g., bandwidth, frequencies and energy, and their use influences the performance of the network significantly. For example, unfair spectrum allocation

it is difficult to answer questions such as what is fairness, how to measure it and how to achieve it. Hence, before addressing the WRAN capacity constraints, the fairness issues applicable to wireless networks in general are addressed in this thesis.

This has resulted in two main research topics in this thesis. The first

part deals with fairness in wireless networks. Within this part, definitions

and different perspectives of fairness are discussed. To achieve fairness, an

Observe-Plan-Do-Check-Act (OPDCA) based fairness process is proposed. Then, the existing fairness indices in resource allocations are examined. From this analysis we conclude that these indices are not sufficient and advanced indices are needed in the domain of wireless networks. Hence, based on the OPDCA framework, a general model of fairness indices is proposed and analyzed.

The network capacity limitation of WRANs is addressed in the second part

of this thesis. Device-to-device (D2D) communication is a promising method

to increase the capacity of the cellular architecture, leading to the concept of device-to-device WRAN (D2DWRAN). There are three main ideas in D2DWRANs: D2D communication, channel reuse and the use of multiple operating channels. D2D communication enables intra-cell CPE-to-CPE communication and makes channel reuse possible. For links that are far from each other, the same channel can be used simultaneously, if there is transmission power control. With channel reuse, the utilization of radio frequencies can be increased significantly. One step further, to maximize the use of available channels, multiple operating channels are envisaged in D2DWRANs both in the downstream (BS to CPE) and upstream (CPE to BS) directions. These three ideas require new protocols and strategies. Hence, based on the existing standards of IEEE 802.22, relevant issues and proposed solutions in this thesis are: the D2DWRAN OFDMA system, the channel management of multiple channels, the spectrum sharing problem, the self-coexistence amongst cells and the QP scheduling. These proposals provide the basis for designing D2DWRANs that have a significantly higher capacity compared to the existing IEEE 802.22 network proposals, which is also confirmed by simulation results.

De snelle ontwikkeling van draadloze apparaten, toepassingen en netwerken heeft ertoe geleid dat radio frequenties overbelast en bovendien schaars zijn. Het is daarom van groot belang dat deze frequenties efficient gebruikt worden. Cognitieve radio (CR) is een techniek die hier uitkomst kan bieden. Bij CR worden niet in gebruik zijnde frequenties, die in principe voor licentiehouders, de primary users (PUs), gereserveerd zijn, tijdelijk gebruikt door secundary users (SUs), die niet over een licentie beschikken. De voorwaarde is dat de PUs hier niets van merken. Dit zorgt voor een betere benutting van het radio spectrum. Cognitieve Radio Netwerken (CRNs) zijn draadloze netwerken die gebruik maken van CR technologie. De IEEE 802.22 werkgroep heeft drie standaarden ontwikkeld voor CRNs, en er zijn verschillende lopende onderzoeksprojecten op dit terrein. IEEE 802.22 richt zich op draadloze breedband diensten in landelijke gebieden, de Wireless Regional Networks (WRANs). Hierbij word gebruik gemaakt van TV kanalen die in landelijke gebieden niet druk bezet zijn en bovendien geschikt zijn voor communicatie over langere afstanden. De netwerkarchitectuur is cellulair , met cellen die afstanden tot 100 km kunnen overbruggen.

Er zijn drie factoren die de data transfer capaciteit van WRANs ernstig beperken: (1) er wordt gebruik gemaakt van n enkel radiokanaal, (2) de point-to-multipoint topology en (3) de noodzaak om regelmatig quiet periods (QP) in acht te nemen voor spectrum sensing ten behoeve van cognitieve radio. Bovendien voorziet IEEE 802.22 niet in het onderling delen van spectrum door CPEs, wat directe invloed heeft op de channel utilization. In dit proefschrift gaan we op zoek naar methoden om de capaciteit van WRANs te verhogen d.m.v. spectrum toewijzing. Een belangrijk aspect wat het toewijzen van resources betreft is de fairness waarmee

de gebruikte energie. Een unfaire toewijzing van deze resources kan leiden tot poblemen, zo kan een unfaire toewijzing van spectrum aan een CPE ertoe leiden dat de mogelijkheden om toegang te krijgen tot het radio kanaal uitgeput raken, wat resulteert in een inefficient gebruik van het beschikbare spectrum. De betekenis van fairness hangt af van de context. Daarom is het lastig te bepalen wat fairness exact is, hoe het gemeten kan worden en hoe het kan verzekerd worden. Daarom wordt in dit proefschrift eerst het concept fairness in draadloze netwerken bestudeerd, zonder dat dit reeds toegespitst wordt op WRANs.

Dit heeft ertoe geleid dat er twee hoofdonderwerpen zijn in dit proefschrift. Het eertse onderwerp is fairness in draadloze netwerken. In dit deel worden definities van fairness bestudeerd die met verschillende perspectieven overeenstemmen. Om fairness te bereiken wordt een Observe-Plan-Do-Check-Act (OPDCA) benadering

uitgewerkt. Vervolgens worden diverse fairness indices voor het toewijzen van

resources bekeken. We concluderen dat de bestaande indices niet voldoen en er behoefte is aan een algemeen model voor de ontwikkeling van fairnes indices. Dit model wordt uitgewerkt en geanalyseerd.

Het tweede deel van het proefschrift richt zich op methoden om de inherente capaciteitsbeperkingen van de huidige WRANs op te heffen. Een belangrijke meth-ode is het gebruik van directe device-to-device (D2D) communicatie, resulterend in een device-to-device WRAN (D2DWRAN) . Het omvat drie elementen: (1) D2D, de directe communicatie tussen CPEs waar dit mogelijk is, (2) het hergebruik van radiokanalen in dezelfde cel, en (3) het gebruik van meerdere radiokanalen in parallel. D2D maakt het hergebruik van radiokanalen mogelijk. Gelijktijdige verbindingen tussen CPEs die geografisch ver van elkaar verwijderd zijn mogelijk op voorwaarde dat het zendvermogen aanpasbaar is. Meerdere radiokanalen worden zowel in de upstream (Base Station naar CPE) als de downstream (CPE naar Base Station) gebruikt. Het inzetten van de drie technieken vergt nieuwe protocollen en strategien. Vandaar dat we een aantal technieken en uitbreidingen van de IEEE 802,22 voorstellen om dit voor mekaar te krijgen. Deze vormen de basis voor het ontwerpen van D2DWRANs die een significant hogere capaciteit hebben dan de IEEE802.22 netwerken. Dit wordt bevestigd door onze simulatiestudies.

Summary i

Samenvatting iii

1 Introduction 1

1.1 Background . . . 4

1.1.1 Fairness in Wireless Networks . . . 4

1.1.2 Overview of IEEE 802.22 . . . 5

1.2 Motivations . . . 7

1.2.1 Fairness in Wireless Networks . . . 7

1.2.2 IEEE 802.22 . . . 8 1.3 Contributions . . . 9 1.4 Thesis Outline . . . 10

Part I

Fairness

13

2.2 What Is Fairness? – Definition . . . 16

2.2.1 Definition . . . 16

2.2.2 Classification . . . 17

2.2.3 Fairness, Resource Allocation and Utility . . . 18

2.3 How to Measure Fairness? – Fairness Indices . . . 23

2.3.1 Aspects of Fairness Indices . . . 23

2.3.2 Assumptions and Requirements . . . 24

2.3.3 Comparison of Existing Fairness Indices . . . 26

2.3.4 Open Issues . . . 26

2.4 How to Achieve Fairness? – Fairness Process . . . 27

2.5 Summary . . . 29 v

3.2 Fairness Indices . . . 33

3.2.1 System Model . . . 33

3.2.2 Coherence with Existing Fairness Indices . . . 36

3.3 An Example . . . 36

3.3.1 Formulations . . . 37

3.3.2 Validation in Round Robin Allocation . . . 39

3.3.3 Discussions . . . 43

3.4 Simulations and Results . . . 44

3.4.1 OFDMA Burst Allocation . . . 44

3.4.2 Simulation Setup . . . 47 3.4.3 Results . . . 51 3.5 Summary . . . 60

Part II

D2DWRAN

61

4.2.2 Cognitive Radio Capability . . . 66

4.2.3 Limitations . . . 66

4.3 D2DWRAN Basics . . . 67

4.3.1 Main Ideas . . . 67

4.3.2 Use Cases . . . 73

4.3.3 Advantages and Requirements . . . 78

4.4 Summary . . . 80

5 OFDMA and Resource Allocation in D2DWRANs 81 5.1 Introduction . . . 81

5.2 Related Works . . . 83

5.2.1 The OFDMA System of WRANs . . . 83

5.2.2 Resource Allocation in OFDMA . . . 83

5.2.3 OFDMA with D2D Technology . . . 85

5.3 OFDMA in D2DWRANs . . . 86

5.4 The Burst Allocation Problem in D2DWRANs . . . 88

5.4.1 Problem Formulation . . . 89

5.4.2 Problem Analysis . . . 91

5.5 The Interference Map . . . 94 vi

5.6 QoS Based Intra-cell Burst Allocation . . . 97

5.6.1 First Tier - Different QoS Levels . . . 97

5.6.2 Second Tier - Within the Same QoS Level . . . 99

5.7 Simulations and Results . . . 99

5.7.1 Simulation Setup . . . 100

5.7.2 Results . . . 101

5.7.3 Discussions . . . 106

5.8 Summary . . . 106

6 Fairness and Energy Efficiency in D2DWRANs 109 6.1 Introduction . . . 109

6.2 Greedy versus Optimal Solutions . . . 109

6.3 Fair and Energy-efficient Spectrum Sharing . . . 112

6.4 Simulations and Results . . . 114

6.5 Summary . . . 116

7 Multiple Operating Channel in D2DPWRANs 119 7.1 Introduction . . . 119

7.2 Multiple Channel Management . . . 119

7.2.1 Channel Management in WRANs . . . 120

7.2.2 Channel Management in D2DWRANs . . . 122

7.3 OFDMA with Multiple Operating Channels in D2DWRANs . . . 125

7.3.1 The DS-subframe . . . 125

7.3.2 The US-subframe . . . 125

7.4 Channel Switching . . . 126

7.5 Simulations and Results . . . 128

7.6 Summary . . . 131

8 Self-coexistence and Quiet Period Scheduling in D2DWRANs 133 8.1 Introduction . . . 133

8.2 Related Work . . . 134

8.2.1 The Self-coexistence Problem . . . 134

8.2.2 Quiet Period Scheduling . . . 137

8.3 Self-coexistence in D2DWRANs . . . 140

8.3.1 The Work Flow and Principles . . . 140

8.3.2 CRDS . . . 143

8.3.3 DCSS-1 . . . 144

8.3.4 DCSS-2 . . . 147

8.3.5 Asymmetric Channel Sets . . . 150 vii

8.6 Simulations and Results . . . 155

8.6.1 Different Number of Cells . . . 158

8.6.2 Different Number of Channels . . . 158

8.7 Summary . . . 159

9 Conclusions and Future Work 161 9.1 Revisiting the Issues . . . 161

9.2 Concluding Remarks . . . 162

9.3 Future Work . . . 165

A Existing Fairness Indices 169 A.1 Jain’s Index . . . 169

A.2 Entropy . . . 170

A.3 Max-min . . . 172

A.4 Proportional Fairness . . . 173

A.5 Tian Lan’s Model . . . 175

A.6 Envy Based Fairness Model . . . 177

Bibliography 181 Abbreviations 191 Publications 195 Acknowledgement 199 Curriculum Vitae 201 viii

1

Introduction

Applications of wireless networking technologies are experiencing a tremendous

growth. New techniques, protocols, devices and applications have constantly

been introduced to the users, creating opportunities for new ways of interacting and increased productivity in the professional sphere. The number of wireless devices has been growing exponentially. Over 6 billion mobile phone subscriptions (excluding other wireless devices such as Wi-Fi devices) were reported at the end of 2011 according to the International Telecommunication Union (ITU) report

-Measuring the Information Society 20121_{. According to the same report, around}

85.7% of the world’s population have their own mobile phone subscriptions, and Fig. 1.1 shows the Information and Communication Technology (ICT) development from 2001 to 2011 with respect to wireless devices and networks.

Recently, the booming of the Internet of Things (IoTs) and Cyber Physical Systems (CPS) is offering opportunities for context-aware intelligent services. With IoTs and CPS, a massive number of devices can be networked to offer new services [1]. These developments, however, have also introduced new challenges with respect to the management of spectrum, which is not an abundant resource.

The management of radio frequencies in different countries and regions, each with their own regulators, is governed by treaties under the umbrella of the ITU. For example, the Federal Communications Commission (FCC) and National Telecommunications and Information Administration (NTIA) in the United States manage the use of spectrum by non-government and government users, respectively. In Europe, the European Conference of Postal and Telecommunications Adminis-tration is in charge of the radio frequency allocation. The spectrum is allocated

1_{The full report can be found at http://www.itu.int/dms}

-pub/itu-d/opb/ind/D-IND-ICTOI-2012-SUM-PDF-E.pdf.

0 10 20 30 40 50 60 70 80 90 100

Mobile-cellular telephone subscrip!ons

Percentage of individuals using the Internet Fixed-telephone subscrip!ons

Ac!ve mobile-broadband subscrip!ons Fixed (wired)-broadband subscrip!ons Households with Internet access

P e r 1 0 0 i n h ab it an ts 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 85.7 34.1 32.5 17.3 15.7 8.5

Figure 1.1 – The global ICT development between 2001 and 2011 from Measuring the Information Society 2012, ITU.

for different services and technologies by these regulators. The allocated spectrum is for exclusive use by a certain technology or service and these bands are called licensed bands and the rest are unlicensed bands. The users that are authorized to use these frequencies are called the licensed users or primary users (PUs).

An imbalance with respect to the spectrum availability can be easily seen in this centrally controlled spectrum management. On the one hand, most of the frequencies are already allocated, which means that not many unlicensed bands are left for new technologies. For example the industrial, scientific and medical (ISM) bands are unlicensed and have to be shared by many wireless standards and applications, e.g., IEEE 802.11 (Wi-Fi), IEEE 802.15.1 (Bluetooth), and IEEE 802.15.4 (Zigbee). The coexistence of these technologies and devices is already turning into a critical issue [2]. On the other hand, the licensed bands can be expensive, e.g., the Dutch 4G mobile spectrum auction raised a total of 3.8 billion Euros according to the Dutch spectrum agency Agentschap Telecom report in December of 2012 [3]. However, many licensed bands have a very low utilization. A recent study on 20 MHz to 6 GHz by Vinod Kone, et al. [4] shows that on average 54% of the spectrum is never used and about 26% is not frequently used. Another study indicates that only an average 5% of the total capacity of the licensed bands is used [5]. More spectrum utilization results can be found, for instance, on the website of the Shared Spectrum Company [6].

F req u e n cy b an d s Time PU PU PU PU PU PU PU Hopping of a SU WS WS WS WS WS

Figure 1.2 – An example of frequency reuse in CR.

Cognitive radio (CR) and cognitive radio networks (CRNs) are providing a solution to resolve or at least ease this imbalance [7]1_{. The basic idea of the CR}

concept, in this thesis, is to let unlicensed users (also called secondary users or SUs) occupy licensed bands when the PUs are not using them. The unused licensed bands in time and frequency domains are called white spaces (WSs); the SUs try to use WSs without causing any interference to the PUs. Therefore, when a PU appears, SUs should stop using that particular WS immediately. An example of WSs and a SU using these WSs is shown in Fig. 1.2. The SU in Fig. 1.2 hops from WS to WS in order to get access without causing any interference to the PUs.

In the last decade, there has been a large interest in CR technology. It

has encouraged many standardization groups, such as Ecma, IEEE 802.22, IEEE 802.11af, IEEE 802.16h, IEEE 802.19.1 and IEEE Dynamic Spectrum Access Networks (DySPAN) 1900.6 [8].

IEEE 802.22 is the first world-wide wireless network standard about CR technology. It is applicable to many scenarios, especially to provide broadband services in rural areas. The main drawback of an IEEE 802.22 network is its limited network capacity. In this thesis, we seek to enhance the network capacity via advanced spectrum management techniques and resource allocation strategies. While developing strategies, we found that fairness is an important issue that needs to be addressed first in order to use the spectrum more efficiently and increase the network capacity. Fairness is a prevalent issue in almost all resource allocation scenarios when two or more entities have to share the resource. Even though it is difficult to define fairness precisely, its goal is to treat all individuals equally

1_{Cognitive, as Mitola envisaged it, has a much more ambitious scope. It is about intelligent}

radios that can detect communication needs of applications that are context aware and find the best way of serving the communications needs. Hence, the original CR paradigm has a broader vision.

with respect to some criteria, for example, the priority of a request. Unfairness in resource allocation will lead to starvation and reduction in Quality of Service (QoS) or performance experienced by particular users. For instance, unfairness of medium access opportunities and in energy consumption can be experienced at the edge of IEEE 802.22 networks, the details of which will be discussed later in this thesis (Chapter 6). Since we found that a comprehensive framework was lacking for fairness in wireless networks, we proceeded to develop one. This framework can be of use to researchers working on wireless network resource allocation problems in general. Hence in the first part of this thesis (Part I), we discuss fairness in wireless networks in general. In Part II, we address critical issues concerning IEEE 802.22 networks and use the results of Part I.

An overview of the thesis is provided in the remainder of this chapter, starting with a discussion of fairness in wireless networks and some background information on IEEE 802.22, followed by the motivation for our research and a summary of our contributions.

1.1

Background

1.1.1

Fairness in Wireless Networks

Internet

Figure 1.3 – An ad hoc wireless network scenario consisting five wireless nodes and six wireless links where the objective of the nodes is to access the Internet services through the gateway Node C.

scenario of an ad hoc network as shown in Fig. 1.3. Nodes A, B, C, D and E are devices that communicate with each other over wireless links L1to L5. Node C acts

as a gateway to the Internet over L6. Many fairness issues can be explored even

in this simple scenario. For instance, nodes should get a fair chance to access the Internet, bandwidth should be fairly shared, QoS requirements of the nodes should be fairly satisfied and energy consumption should be fair. These issues indicate the significance and the diversity of fairness issues in wireless networks. There is no single method or solution that can address all the constraints listed above. Although this is a very simple example, we can already see multiple interpretations of the notion of fairness. We will try to capture the notion of fairness for wireless networks in general.

Based on this simple scenario, we can pose three essential questions: Q1 What is fairness?

Q2 How to measure fairness? Q3 How to achieve fairness?

Q1 is about the definition of fairness. It is rather difficult to arrive at a consensus to provide a universally accepted definition of fairness because of the multitude of fairness issues. Q2 is about how to measure the fairness in a system. Q3 implies how to avoid unfairness and what to do when unfairness happens.

1.1.2

Overview of IEEE 802.22

In 2004, the FCC gave permission to use TV channels in both very high frequency (VHF) and ultra-high frequency (UHF) bands for fixed broadband services [9]. Based on this ruling, the IEEE 802.22 standard was developed for wireless regional area networks (WRANs). The differences between IEEE 802.22 and other wireless standards are illustrated in Fig. 1.4 [10]. So far three official standards have been published by the IEEE 802.22 working group. They are IEEE 802.22-2011, IEEE 802.22.1-2010 and IEEE 802.22.2-2012 [11]. There are still two ongoing standardization projects, viz., IEEE 802.22a and IEEE 802.22b (refer to Fig. 1.5).

IEEE 802.22 operates on TV channels from 2 to 69 (54 MHz to 862 MHz) with a bandwidth of 6, 7 or 8 MHz depending on the country [10]. The typical radius of a WRAN cell is 32 km but it can go up to 100 km or even more [10]. WRANs are formed in a point-to-multi-point (P2M) fashion with one base station

(BS) and multiple customer premises equipment (CPE) in a cell. The BS of

an IEEE 802.22 cell manages the channel allocation amongst CPEs and aims at coexistence with the PUs and the neighboring IEEE 802.22 cells. The BS also schedules Quiet Periods (QPs) for spectrum sensing to ensure that no harmful interference is caused to the PUs. The CPEs are equipped with two antennas: a directional one for communication with the BS and an omni-directional one for sensing and geo-location. Orthogonal frequency-division multiple access (OFDMA)

Frequency Network type Range Maximum data rate Standards Mul!path absorp!on window / cyclic prefix 802.15 802.11a 802.11b 802.16 802.22 PAN LAN MAN RAN 10m 20-50m 20m 30 km 1-2 km (5 GHz) 1 Mbps 33m 10 Mbps 54 Mbps 54 Mbps 11 Mbps 23, 27, 31 Mbps / BW: 6, 7, 8 MHz 2.4 GHz 2.4 GHz 5 GHz 2.4 GHz < 2.4 GHz 56-862 MHz 37 µs 2.2 µs 0.25 µs 0.8 µs

Figure 1.4 – Comparison of IEEE 802.22 standard to other wireless network standards [10]. PAN: personal area network; LAN: local area network; MAN: metropolitan area network; RAN: regional area network.

IEEE 802.22 Standard- Wireless

Regional Area networks: Cognive

Radio based Access in TVWS:

Published in July 2011

802.22.1- Std for Enhanced

Interference Protecon in

TVWS: Published in Nov. 2010

802.22.2- Std for

Recommended Pracce for

Deployment of 802.22 systems:

Published in Sep. 2012

802.22a - Enhanced

Management Informaon Base

and Management Plane

Procedures: Expected

Compleon in Dec. 2013

802.2b – Enhancements for

Broadband Services and

Monitoring Applicaons:

Started in Mar. 2012

Figure 1.5 – The Projects of IEEE 802.22 working group [12].

is used in IEEE 802.22 to enable multiple CPEs to access the BS simultaneously in a cell. Time-domain duplexing (TDD) is used to split a frame into downstream and upstream subframes. More relevant details of the IEEE 802.22 standards will be discussed in Chapter 4 and further information can be found in [11].

1.2

Motivations

In order to understand and enhance the spectrum management and resource allocation in IEEE 802.22 networks, the fairness issues need to be looked into. In this section, we demonstrate the urgency of research on both fairness issues and IEEE 802.22 networks.

1.2.1

Fairness in Wireless Networks

There are no commonly agreed upon answers in the literature to the three core questions on fairness that we have raised. Even though some work on fairness in wireless networks has been published, it is not systematically organized. A framework and measures (indices) for fairness are lacking. These are required if one wants to undertake a systematic study of fairness in wireless networks.

The most popular fairness indices in wireless networks are Jain’s index, max-min and proportional fairness [13–15]. However, none of them cover all aspects of fairness. These aspects are individual fairness, system fairness, short-term and long-term fairness, and fairness with priorities. Hence, some advanced fairness indices should be developed to study these aspects.

Fairness is usually considered as equalization during allocation in which every individual should be allocated the same amount of resources, for example [13, 15]. However, this is not always the case since each user may have different requests,

latency and priorities. One way to deal with this is to assign weights during

allocation, but, an efficient method to assign weights needs to be addressed [14]. Further, in many wireless network resource allocation studies, fairness is not treated as an independent problem but only as a sub-problem, for example in [16] and [17]. In these studies, fairness is usually considered along with the resource allocation and utility. A general methodology for defining fairness in wireless networks is not straightforward. A broader view of fairness in wireless networks, based on the three core questions (Q1, Q2 and Q3), is discussed in Part I of this thesis.

1.2.2

IEEE 802.22

In IEEE 802.22 networks, the cellular topology and OFDMA enable simple and centralized network and spectrum management by the BSs. However, the network capacity is severely limited for many reasons: (1) All packets need to be routed by the BS, which adds extra delay and traffic. (2) Multiple channels are not supported if vacant channels are non-adjacent [10]. (3) Multi-input-multi-output (MIMO) is not supported by the IEEE 802.22 standard because of the size of the antennas [10]. (4) Neighboring cells have to share the same TV channels. (5) QPs – during which sensing is done – need to be scheduled constantly to protect the PUs [9]. Therefore, increasing the network capacity is one of the major challenges for IEEE 802.22 networks.

Another challenge is the protection of PUs. In IEEE 802.22, two methods are adopted to protect the PUs. One method is to inquire the incumbent database, which is maintained by the spectrum authorities [10]. Spectrum usage information at different geometric locations is stored in the incumbent database. BSs need to access the incumbent database on a regular basis. The accuracy of this database is critical as inaccurate information may lead to a collapse of the whole regional network or may cause severe interference to PUs. The other method is spectrum sensing. By sensing the use of spectrum during the QPs, a WRAN may detect the unexpected appearance of PUs and switch to another channel. Methods to efficiently schedule the QPs and the sensing technology need to be examined.

Furthermore, the self-coexistence between different WRAN cells is a significant

(CBP) [18] has been introduced to enable spectrum sharing amongst neighboring WRAN cells, the performance of the CBP-based method is not yet clear.

There are many other standardization activities concerning the use of television white spaces (TVWSs), e.g., IEEE 802.11af, IEEE 802.22.16h and IEEE DysPAN

1900.6. However, coexistence of WRANs with these networks has not been

addressed yet in the literature. We can foresee that there will be even more wireless technologies using TV WSs in the near future. Coexistence of these different CRNs will be a major constraint for channel utilization and channel reuse.

1.3

Contributions

Our contributions in this thesis are grouped in two parts. In the first part, we study fairness issues in wireless networks. We propose a device-to-device WRAN (D2DWRAN), which is a promising technology to enhance the network capacity of IEEE 802.22 networks, in the second part of this thesis. Our findings, proposals and all other related issues on D2DWRAN are discussed in the second part. We list our contributions as follows:

• Part 1 - Fairness:

1. Fairness issues in general are discussed, viz., definitions, classifications and relation with resource allocation and utilities. A general view of fairness research is developed.

2. Existing fairness indices are analyzed systematically. The merits and drawbacks are listed for these indices, such that researchers can choose the most suitable one, according to the scenario they are dealing with. 3. Fairness research in wireless networks is discussed and assessed. The

remaining open questions are presented.

4. A general fairness model is proposed, which addresses the challenges in the fairness domain. It provides multiple indices to measure the fairness along different dimensions. A procedure is also presented to achieve fair resource allocation.

• Part 2 - D2DWRAN:

1. D2DWRAN is developed based on the IEEE 802.22 standard, to remedy the severely constrained network capacity of IEEE 802.22 WRANs. Use cases, advantages and special requirements of D2DWRAN compared to WRAN are examined.

2. An OFDMA system is designed for D2DWRAN that supports direct CPE to CPE communication, channel reuse and multiple operating channels.

3. The resource allocation problem in a cell based on the OFDMA system

of D2DWRAN is formulated and analyzed. It turns out to be a

computationally hard problem. Related issues, such as building of

interference maps and QoS levels are also discussed. Some algorithms are proposed accordingly to make quick and reasonable allocation decisions. 4. Fairness and energy efficiency are also critical during the spectrum sensing in D2DWRANs, thus, we propose a mechanism that allows BSs to manage the fairness and energy efficiency of CPEs during spectrum sharing.

5. With multiple operating channels in D2DWRANs, the channel

man-agement is different from WRANs. Further, new channel switching

issues appear. Hence, we modify the channel management mechanism in WRANs to enable multiple operating channels. We also propose a new channel switching procedure to ensure that the QoS of CPEs can be guaranteed.

6. The self-coexistence mechanisms designed for IEEE 802.22 standard do not function anymore in D2DWRAN. Therefore, we propose a resource allocation and sharing mechanism based on the concept of a channel sharing community (CSC) at both inter- and intra-cell levels.

7. Backup channel policies are discussed in D2DWRAN to provide seamless

services for SUs when the PUs appear. Four different policies are

proposed and their merits and drawbacks are discussed. The service providers can select one of the policies based on the actual environment. 8. Quiet period (QP) scheduling is a unique problem in IEEE 802.22 networks. After reviewing the literature, a slot based scheduling method is designed for the scenarios with multiple operating channels.

1.4

Thesis Outline

The outline and flow of the thesis are shown in Fig. 1.6. Part I includes Chapter 2 and 3. In Chapter 2, we analyze fairness in general along with the challenges, based on which, a fairness model is proposed in Chapter 3. Part II starts in Chapter 4 with an overview of D2DWRAN including its main ideas, use cases, advantages and disadvantages. In Chapter 5, we first describe our OFDMA design for D2DWRAN, then the resource allocation problem based on this OFDMA system is addressed. We discuss the fairness and energy efficiency in the resource allocation of the OFDMA system in Chapter 6. To achieve seamless services for CPEs as well as protection of PUs, the multiple operating channel management is addressed in Chapter 7. We also pose the self-coexistence problem amongst neighboring cells and the QP

Part I: Fairness

Part II: D2DWARN Chapter 2:

Fairness in Wireless Networks

Chapter 3:

An OPDCA Based Fairness Model

Chapter 4: D2DWRAN: An Overview

Chapter 8: Self-Coexistence and Quiet Period Scheduling in D2DWRAN Chapter 7: Mul!ple Opera!ng Channel Management in D2DWRAN Chapter 6: Fairness and Energy Efficiency in D2DWRAN Chapter 5:

The OFDMA and Resource Alloca!on D2DWRAN

scheduling problem in Chapter 8. Finally, in Chapter 9, we conclude the thesis, present the open research problems that still need to be solved in order to have a comprehensive set of solutions for D2DWRAN and suggest research topics to enhance the work we did.

Fairness

2

Fairness in General

2.1

Introduction

The pervasiveness of wireless technology has created an opportunity to integrate almost everything into the Internet fabric. The Internet of Things (IoTs) and Cyber Physical Systems (CPS) that involve cooperation of massive number of devices are a manifestation thereof. Sharing of spectrum among all these devices is turning into a critical issue, especially sharing in a fair way. The problem is compounded by the fact that the spectrum availability in WRANs varies with time. Fairness can be analyzed from several perspectives, e.g., energy usage, achievement of required quality of service, allocation of spectrum and so on. In this chapter, these viewpoints are focused and existing fairness researches are compared and analyzed with respect to three core questions:

Q1 What is fairness?

Q2 How to measure fairness? Q3 How to achieve fairness?

In this chapter, we try to answer these three questions. We first present a general view of fairness in Section 2.2, with a definition, classification and relationship with resource allocation problems and utility. Fairness indices are tools to measure fairness level and provide guidelines to distribute resources in a fair way. Defining fairness indices is one of the key issues in fairness study. Hence, in Section 2.3, the existing fairness indices are summarized and analyzed. Finally, to answer Q3, we propose an “Observe-Plan-Do-Check-Act” (OPDCA) based process in Section 2.4 to achieve fairness.

2.2

What Is Fairness? – Definition

Fairness is widely referred to equal treatment of individuals, but it is more than that. In order to grasp the concept of fairness, we investigate definitions in the literature, classifications using different aspects and the relationship between fairness, resource allocation and utility.

2.2.1

Definition

In the Oxford English Dictionary, the definition of fairness is “..., equitableness, fair dealing, honesty, impartiality, uprightness,...” [19]. There are some other fairness definitions in the literature, e.g., “an allocation where no person in the economy prefers anyone else’s consumption bundle over his own” [20] and “a fair allocation is free of envy ” [21]. Sawyer et al., define fairness as “equal treatment to equal individuals and reserving preferred treatment for those individuals who are in some sense more deserving [22]”. However, these definitions draw our attention to the ambiguity in identifying equal individuals in the first place. Furthermore, equal treatment is another ambiguous term. While all these definitions are fuzzy, they jointly indicate a sense of impartiality, justice and satisfaction of individuals. Our objective in this section is to relate these values to the domain of wireless networking. In wireless networks, fairness is generally used for resource sharing or allocation. The consequence of an unfair resource allocation between different individuals may lead to resource starvation, resource wastage or redundant allocation. Fairness has been mostly studied in resource allocation based on impartial and justified strategies. Fairness strategies allocate system resources reasonably to individuals of the system in a distributed or centralized fashion. In this chapter, we use the term Individual (or Node)1 _{to refer to an autonomous constituent of the system.}

For instance, in a wireless ad hoc network, nodes are the individuals and network is the system.

It is rather difficult to agree on a single definition of fairness as it is often subjective. When we consider rational individuals, each individual evaluates the share of resources it received and compare it with others in the system from its own point of view. Consequently, the definition of fairness or any effort to define fairness is influenced by the value ascribed to the resources by the designer of the system or by individuals of the system. Most of the fairness research is around ascribing a real value to the resources shared by individuals. However, this is not an easy task since the requirements by individuals are different. Further, the prices paid by those individuals also play a role. Moreover, the universal performance is also important. Thus the system level resource usage should also be considered.

1_{We use the terms node and individual interchangeably, not to affect the natural flow while}

In the following content, we first classify fairness according to different views and then we discuss the relationship between fairness, resource allocation and utility.

2.2.2

Classification

Various aspects need to be considered in fairness definitions. For example, equal opportunities for individuals in resource sharing may not mean equal allocation of resources. On the other hand, a fair allocation may be an outcome of a process where individuals do not have equal opportunities. Therefore, targeted and resultant fairness should be distinguished. Furthermore, there may be temporal changes in the resource allocation in a dynamic system. This suggests that fairness may also have a temporal dimension. Fairness can also be considered from the point of view of system and individuals. Thus, we classify the fairness definitions into targeted and resultant fairness, short- and long-term fairness, and system and individual fairness.

2.2.2.1 Targeted and Resultant Fairness

Based on resource allocation and utility, fairness can be divided into two types: targeted fairness and resultant fairness1_{. Targeted fairness tries to achieve fair}

sharing of resources whereas resultant fairness aims at fair utilization.

2.2.2.2 Short- and Long-term Fairness

Considering the time duration, fairness can be categorized into short-term and long-term fairness [16, 23]. Short-term fairness focuses on resource allocation in a very short time period, e.g., considering one or few resource allocation iteration; measurement of the fairness is done at selected moments. In contrast, long-term fairness measures the resource allocation over a longer time period, e.g., more than hundreds resource allocation iterations or even at the end of life cycle. Short-term fairness has a significant impact on QoS, especially in realtime applications, because of the focus on the current QoS measurements. When the resources are scarce, short-term fairness is very difficult to be guaranteed when many individuals are contending for it. If the short-term fairness is not a system requirement, then long-term fairness is considered.

1_{In [16], these two terms are mentioned as effort and outcome fairness. We use different terms}

2.2.2.3 System and Individual Fairness

Fairness can be considered both at the system and individual levels. The system fairness addresses the overall fairness amongst all individuals in the system and individual fairness indicates whether a certain individual is treated fairly by the system.

The above classification is considered to be pragmatic rather than absolute. We want to emphasize again that fairness can be very subjective and researchers have different opinions about resource allocation for a particular scenario.

2.2.3

Fairness, Resource Allocation and Utility

Fairness is always discussed along with resource allocation and utility. However, in the literature, most researchers do not even distinguish them. These three notions (fairness, resource allocation and utility) are different:

Fairness aims to quantify the quality of equality in treatment of similar individuals of a system.

Resource allocation is about the distribution of resources amongst individuals in a system.

Utility of a resource or multiple resources is related to the satisfaction or perceived value of the resource by an individual or the whole system. Rational individuals aim at maximizing the satisfaction obtained from the resources allocated to them which in turn impacts the performance.

Note that the notion of utility is often equivalent to the concepts of performance and efficiency. For instance, utility is often simply defined as throughput, delay or other some other performance metric [17, 22, 24]. The term “utility”, in this thesis, stands for a metric that expresses to what extent the resource allocation satisfies an individual. It is usually derived from performance indices excluding fairness.

Fairness can be expressed both in allocation and utility as “allocation fairness” and “utility fairness” respectively. However, allocation fairness can be treated as a special case of utility. Since resource allocation influences utility directly, achieving an acceptable utility is the fundamental principle in resource allocation. Another essential guiding principle for allocation is to guarantee a reasonable fairness. Hence, resource allocation is the action while fairness and utility lead to rules that constrain an allocation. We explore the properties of each of them and also their interdependence in detail.

2.2.3.1 Resource Allocation

Resource allocation involves the complete procedure of resource distribution in a system. However, in this chapter, we mainly focus on the allocation strategies which are directly related to fairness. We classify resource allocation as follows.

Possession: The concept of possession reflects the owners of resources. We adopt two possession types of resources: global and individual resources. Global resources belong to systems and are allocated at the system level. By contrast, individual resources are held by individuals and allocated at the individual level. For example, channels in wireless networks are global resources but battery-energy is an individual resource.

Consumability: It indicates whether the resource decreases in next resource

allocation iteration. For instance, energy in batteries is consumable whereas

channels are non-consumable.

Allocability: It considers whether resources can be re-allocated. In wireless networks, bandwidth can be re-allocated to different nodes at the same time. However, some resources are individual in nature such as the battery for a node that can be used only by that node.

Quantity: Single-resource or multi-resource allocations describe the quantity of resource allocation. When only one type of resource is processed in an allocation, it is a single-resource allocation. In contrast, when several types of resources are considered at the same time in an allocation, it is a multi-resource allocation. For example, channel assignment in wireless networks is a single-resource allocation. However, when it is considered jointly with bandwidth, the allocation becomes a multi-resource allocation. Multi-resource allocation usually considers correlated resources at the same time, such as rate, radio and power together, or channels and battery-energy together. The complex relation between these resources and their influence on the network performance makes multi-resource allocation more complex than single-resource allocation.

Management: Allocation can be either controlled centrally or in a distributed fashion. In centralized management, a single control unit makes allocation decisions

usually based on complete information of the system. Whereas in distributed

management, the nodes make allocation decisions themselves, mostly based on local information of the system. Often, distributed management is not as fair

as centralized control because of lack of information. However, there is less

management communication overhead, less computation and shorter information collection time with distributed management.

Scope: Based on the scope of allocation, it is classified as global or local. Global allocation allocates resources at the overall system level, however, the local allocation considers the allocation locally. Global allocations may contain multiple local allocations. For example, channel allocation in a large-area wireless network involves multiple local allocations because channels can be allocated in different local areas if no interference is caused.

2 . F A IR N E S S IN GE N E R A L

Static and Dynamic Individual Set: Resource allocation can also be classified as static individual and dynamic individual allocations by knowing whether the individual set is static or dynamic. For instance, in mobile ad hoc networks (MANETs), wireless nodes join and leave the networks dynamically requiring the resource allocation to consider topology changes whereas in IEEE 802.22 networks, the nodes are relatively static.

2.2.3.2 Utility

In wireless networks, utility is often treated as single or multiple performance aspects of networks or nodes. However, originally in economics, utility is considered as a measure of satisfaction [25]. We adopt this concept of utility in this thesis and describe its role in the domain of wireless networks.

The notion of utility, in this chapter, is defined as the measurement of satis-faction. Satisfaction in wireless networks is indicated via one or more performance metrics. The performances can be classified as node performance and network performance in wireless networks. For example, in Fig. 1.3, the throughput of Node A is the node performance. However, the overall network capacity represents the performance of the whole network. A network can be evaluated from both the perspectives. Therefore, utility can be divided into individual and system utility as shown in Fig. 2.1. Performance metrics used can be merged into a single satisfaction term, which is individual utility. Similarly, the system utility can be obtained by system performance. Utility can also be classified by the functions used. For example, the functions can provide either an enumeration from performance to utility or the order of preference, both of which can describe the satisfaction feature of utilities to either individuals or systems.

2.2.3.3 Relations

The relationship between fairness, resource allocation and utility is shown in Fig. 2.1. Fairness can be measured for both resource allocation and utility, and

on the aspect of either targeted or resultant. The targeted fairness measures

allocation while the resultant fairness measures the utility. Therefore, fairness in resource allocation and utility should be distinguished and measured separately. Furthermore, fairness can also be treated as a type of utility. During the evaluation of utility based on performance metrics, fairness can be considered as one of the

elements leading to utility. On the other hand, in feedback mechanisms, the

historical utility information of the system may provide feedback to the fairness mechanisms and influence them. One of the goals of resource allocation is the fair distribution of resources especially in wireless networks wherein starvation due to lack of resources may lead to a severe fall in utility. Another goal of resource

allocation is to maximize the system and individual utilities. Resource allocation can influence utilities directly because different amounts of resources allocated to individuals may lead to large variations in performance. However, the utility may provide feedback to the resource allocation algorithm in order to achieve higher utility.

Sometimes, it may not be possible to guarantee the individual fairness and the system utility at the same time. For example, in Fig. 1.3 let us assume that network throughput is the system utility. Network throughput can be defined as the data transfer per second through Link L6. During Internet access, we assume that

Node B does not offer routing service for A and C. We also assume that the quality of the links varies with time, e.g., in a snapshot of the network, capacity of L1, L2

and L6 are 1 MBps, 2 MBps and 8 MBps respectively and the others are 1.5 MBps.

We also assume that the Gateway can only statically allocate bandwidth to other Nodes. Bases on these assumptions, a snapshot of the network when the sharing of L6to access the Internet in three cases is shown in Table 2.1. In Case 1, all nodes are

fairly allocated with 20% of the 8 MBps capacity of Link L6 to access the Internet.

In this case, the network throughput of Node A is min{8 × 20%(MBps), 1(MBps)}, which is between the amount of L6’s capacity allocated to Node A (8×20%(MBps))

and the capacity of Link L1 (1(MBps)). Similarly, the throughput of other nodes

can be obtained. Therefore, the network throughput in Case 1 can be calculated as min{8 × 20%, 1} + min{8 × 20%, 2} + 3 × min{8 × 20%, 1.5} = 7.1(MBps).

Table 2.1 – A snapshot of fairness and utility in the network of Fig. 1.3.

Case 1 Case 2 Case 3

Node A (via L1) 20% 5% 35% Node B (via L3) 20% 35% 5% Node C 20% 20% 20% Node D 20% 20% 20% Node E 20% 20% 20% Throughput 7.1 (MBps) 7.3 (MBps) 6.3 (MBps)

In Case 2 and 3, A and B get different shares, and C, D and E get the average share of the link capacity. The throughput is also shown in Table 2.1. In a general sense, Case 1 is fairer than Case 2 and 3, because all nodes get equal capacity. However, network throughput in Case 1 is greater than Case 3 but less than Case 2. Case 3 will not be considered by a decision maker because of lesser gain in terms of both fairness and throughput. Now, the question is which one should be selected – Case 1 or Case 2? Case 1 is fairer but throughput is less than that of Case 2. This problem illustrates that there is a trade-off between fairness and utility. Another

example is the trade-off between utilization of time-slots and fairness in ad hoc networks [26].

2.3

How to Measure Fairness? – Fairness Indices

Fairness indices are tools to measure the fairness level and provide guidelines to distribute resources in a fair way. To answer Q2, we first review different aspects of fairness indices. Based on these aspects, we propose some requirements for fairness indices. We also compare the existing indices in the literature and discuss some open issues.

2.3.1

Aspects of Fairness Indices

Different aspects of fairness indices are shown in Fig. 2.2 and are listed below.

Fairness indices Resource

Level Time

Single resource fairness

System fairness Individual

fairness Mul!-resource fairness Long term fairness Short term fairness Objects U!lity fairness Alloca!on fairness

Figure 2.2 – Aspects of fairness indices.

• Fairness can be measured in resource allocation and utility. Resource

allocation fairness or “effort fairness” reflects the fairness during the process of allocating resources, for example, channel allocation and power allocation in wireless networks. On the contrary, utility fairness or “outcome fairness” shows the fairness in individual and system performance, for example, the fairness of packet delay and throughput.

• System and individual fairness can be differentiated here. The system fairness indicates the fairness level in the scale of a system, but individual fairness indicates whether each individual is treated fairly.

• Short-term and long-term fairness [16,23] should be distinguished. Short-term fairness shows the fairness level over a short duration (normally in one time resource allocation or one time utility measurement) but long-term fairness considers the fairness of allocation over a long duration (either since the beginning or over a measurement window) [16].

• Fairness should also be measured taking priority into account, which indicates that equal distribution may not necessarily lead to fairness. Fairness may depend on other factors, e.g., the number of requests, QoS levels and so on. Therefore, weights of individuals should be considered in fairness indices.

2.3.2

Assumptions and Requirements

To analyze the existing fairness indices in the same framework, we assume that there is one type of resource whose total amount is Cx and there are n individuals

sharing this resource. X = (x1, x2, ..., xn) implies the allocated resources, where

xi is the amount of resource allocated to individuals i = 1, 2, . . . , n. The sum of

the individually allocated resources must be less than or equal to the total amount, which can be written asPn

i=1xi6Cx, where Cxis the total amount of the resource.

We define f (X) : R+

n → R+ as the fairness index based on resource allocation X,

where n is the number of individuals. The basic requirements that a quantitative fairness index must satisfy are:

R1: f (X) should be continuous on X ∈ R+n.

R2: f (X) should be independent of n.

R3: The range of f (X) should be easily mapped on to [0, 1]. R4: Function f (X) should be scalable to multi-resource case. R5: f (X) should be easy to implement.

R6: f (X) should be sensitive enough to the variation of X.

The requirements R1 and R2 imply the generality of fairness function f (X) with different resource allocations and various number of individuals. R3 shows the scalability of f (X) and it gives an intuition and direct impression of fairness achieved. Requirements R4 and R5 make f (X) realistic and implementable. In the sequel, we review several frequently used quantitative indices and identify the set of requirements they must satisfy.

O W T O M E A S U R E F A IR N E S S ? – F A IR N E S S IN D IC E S 25

Table 2.2 – Comparison of the fairness indices.

Models Jain’s

index Entropy Max-min Proportional

Tian

Lan’s Envy-based

Definition Yes No Yes Yes Yes Yes

Measurability Yes Yes No No Yes Yes

Locating unfairness No No No No No Yes

Weight No No Yes Yes No No

Utility No No No Yes No Yes

Control Centralized Centralized Either Centralized Centralized Either

Data Full Full Either Full Full Either

Requirements R1, R2, R3, R5, R6 R1, R2, R5, R6 No No R1, R2, R3, R6 R1, R2, R3, R4 References [13, 27] [28–32] [14, 14, 33–35] [29, 36–38] [30] [39]

2.3.3

Comparison of Existing Fairness Indices

The detailed descriptions and analysis of the existing fairness indices in Appendix A. We compare these indices in Table 2.2 considering various attributes. The first three attributes, definition, measurability and capability are based on the core

questions Q1 , Q2 and Q3, respectively. Weight gives individuals priorities

in allocation. Utility indicates on whether indices consider the trade-off between usage of resources and fairness. Control shows whether the system is distributed

or centrally controlled. Data indicates whether the index requires complete

information of the system. Requirements denote whether the basic requirements, R1 to R6, are satisfied by the indices or not. Table 2.2 shows that none of the existing indices can perform well with respect to all the attributes. Therefore, there is a need for advanced fairness indices.

2.3.4

Open Issues

Though many studies regarding fairness indices have been reported, there are still some open issues. We list the significant ones especially for wireless networks as follows:

• Multiple dynamically varying resources and dynamics of individuals must be considered instead of just a single resource and a fixed number of individuals. • How to identify unfairly treated individuals is not considered by most of the

existing fairness research.

• Weights are the priorities of individuals to get their share of resources. Two more questions come to the fore here: (a) “How to assign weights to individuals, or what factors need to be taken into account while assigning weights to individuals?” and (b) “How to allocate resources according to

individual weights?” The former implies the strategies to distribute the

weights while the latter focuses on strategies for resource allocation based on individual weights. Many investigations have been done on the latter one. However, the weight assignment strategies should also be given due attention. • The relationship between fairness and utility is another crucial issue to be

looked into.

A new model for fairness indices in Chapter 3 to tackle the above mentioned issues. After discussing “how to measure fairness” with fairness indices, we show a method to use these indices (“how to achieve fairness”) in the following content.

2.4

How to Achieve Fairness? – Fairness Process

Many resource allocations are iterative; resources are distributed to individu-als/groups constantly based on either time slots or tasks. This is especially the case in wireless networks because resources are mostly reusable. A fairness process is the procedure of achieving fairness amongst individuals in resource allocations. Because of the iterative property of resource allocation, fairness processes should be iterative too.

“Observe-Plan-Do-Check-Act” (OPDCA) is a five step business model to iteratively control and improve a certain process or a product [40, 41] (Observe is called grasp the current condition in [41]). As an iterative process, fairness process fits in the OPDCA model very well, which is shown in Fig. 2.3. The functions of these five stages reveal the process of achieving fairness, which are listed below.

Observe: all relevant information, e.g., the individuals, resources, requests and the correction suggestions from the Act stage, is collected in this stage. Observe stage also saves the information from previous iterations in case the Plan stage needs to measure long-term fairness or utility.

Plan: the allocation mechanisms make allocation decisions based on the

information provided by the Observe stage. The fairness and utility indices calculate the expected fairness and performance according to the allocation decisions. If an allocation decision is fair enough and the performance is reasonable, then these decisions are sent to the Do stage, otherwise the allocation mechanisms have to make another decision. The fairness measurement and utility indices may pass feedback to the allocation mechanisms while making decisions.

Do: the allocation decisions are carried out in this stage and the actual results of these decisions are sent to the Check stage.

Check: fairness and performance are checked and compared to the goals.

Meanwhile, the data from previous iterations may also be used to find out the trends of performance and fairness. The information is sent to the Act stage eventually.

Act: the indices from the Check stage are examined by action suggestion mechanisms and possible solutions are provided to the Observe stage for the next iteration of allocation. Two types of solutions exist: compensating solution and non-compensating solution. In a compensating solution, if an individual is treated unfairly or the performance is not acceptable, then it gets extra resources in next it-eration to compensate for the loss in this itit-eration. However, in a non-compensating solution, it only gets the resources which are just enough for the fairness and performance requirements in next iteration. In other words, the compensating solution is motivated by long-term consideration but the non-compensating solution only considers current iteration.

2 . F A IR N E S S IN GE N E R A L

Table 2.3 – The functions of the modules in the OPDCA fairness process.

Modules Stages Functions

Information

col-lection Observe

All information about individual, resource, requests and suggestions are collected.

Allocation

mechanisms Plan

The fairness and performance goals are established first. Then the allocations decisions are made to accomplish these goals, after which the decisions are handed to the fairness and utility indices.

Fairness indices Plan,

Check

The fairness indices can measure short-term, long-term, individual,

system, multi-resource or single resource fairness. Two fairness

indices can be seen (FI1 and FI2) as shown in Fig. 2.3. FI1 measures the fairness in resource allocation and FI2 measures the fairness in utility.

Utility indices Plan,

Check

Different performances are measured from both the system side and the individual side. Data from previous iterations may be used if necessary.

Action suggestion mechanisms

Act

Possible solutions for the unexpected results from the Check stage are proposed to the Plan stage for the next iteration. Compensating or non-compensating solution is selected by this module according to the fairness and utility goals.

Allocaon Mechanism Fair enough? Fairness Indices (FI1) Ulity enough? Ulity Funcon Yes NO NO

PLAN

DO

Allocaon

decisions

CHECK

Results

ACT

S

u

g

g

e

s

o

n

s

Measures

OBSERVE

Informaon

Figure 2.3 – The OPDCA based iterative fairness process.

With the OPDCA based iterative fairness process, resource allocations run iteratively, and both the fairness and utility are guaranteed. Functions of the modules in this process are described in Table 2.3.

In this OPDCA model, fairness indices reveal the fairness feature of resource allocation and influence system and individual performance significantly. Hence, we will discuss further on fairness indices in Chapter 3.

2.5

Summary

Fairness is a vital topic in wireless networks since fairness needs to be considered in almost all resource allocation related problems, for example, energy consumption control, power control, topology control, link and flow scheduling, channel assign-ment, rate allocation, congestion control and routing protocols. These issues are not isolated from each other because many of them may co-exist and influence

each other. For instance, fair energy consumption may be influenced by all other fairness issues in a multi-channel ad hoc network. However, the scope of these fairness issues is different. Fair energy consumption is to prolong the network lifetime. Fair power control is to assign power levels fairly to wireless nodes. The main goal of fair topology control is to find the best logical topology in a fair way. Fair link and flow scheduling focus on fairly allocating links to flows. Fair channel assignment allocates channels fairly to nodes. Fair rate allocation and congestion control balance the rates on links without causing any congestion. Fair routing protocols mainly balance the load amongst routers. As fairness is very substantial, we discussed fairness issues pertaining to three questions in this chapter:

What is fairness? Fairness is a vague description of satisfaction without

a common agreed definition. However, some characteristics can still be found, for example, the targeted and resultant, short- and long-term, and system and individual aspects of fairness. Fairness is a separate problem but it is often discussed within the resource allocation problems and utility analysis.

How to measure fairness? Many fairness indices can be found in the literature. However, none of them can measure fairness in all its dimensions, including single and multiple resources, allocation and utility, system and individual, and short- and long-term. Therefore, new advanced fairness indices are needed.

How to achieve fairness? We propose an OPDCA based fairness process to achieve fair resource allocation in wireless networks. This model works iteratively with feedback and takes into account both fairness and utility during resource allocations.

While trying to answer these questions, we hope to give an overview and guidelines for addressing fairness issues. In the next chapter, we will look at fairness indices, in more detail, including a framework for fairness indices, properties of every fairness index, some examples and examinations of our proposals.

3

A General Model of Fairness Indices

3.1

Introduction

In wireless networks, many users/nodes contend for resources, e.g., downstream subchannels, upstream subchannels, time slots and power constraints are all resources of the IEEE 802.22 OFDMA. Allocation of these resources fairly and efficiently is one of the critical challenges in wireless networks. The proposed OPDCA fairness process in Chapter 2 can assist in sharing of resources. This fairness process fits most of the resource allocation problems and provides a platform for further studies on resource allocation and fairness. Fairness indices play a critical role in OPDCA in Plan and Check stages. However, fairness indices in the literature are not analyzed comprehensively to show fairness from different perspectives.

Therefore, a new model for fairness indices is proposed in this chapter. The structure of the model is presented in Section 3.2, which includes weight assignment and a series of indices aiming for different fairness perspectives. This model provides guidelines and principles for the design of fairness indices. Some examples based on this model are provided. These fairness indices are examined with respect to Round Robin allocation. Then, in Section 3.4, the proposed fairness indices are used to evaluate the fairness of an OFDMA resource allocation scheme that is proposed by Liang, et al. in [42].

Remark 1. The notations in this chapter are listed in Table 3.1. Note that we name the set of resource allocation and utility that are measured by fairness indices as “Fairness Elements”.

Table 3.1 – Summary of notations.

Notations Descriptions

t An iteration of fairness process.

τ The current iteration.

τ′ _{The time duration (number of historical iterations)}

that is considered when fairness is measured.

I The set of all individuals.

uji(t) Utility j of individual i.

Uj(t) = {uji(t)|i = 1, ..., |I|} The set of utility j of all individuals.

U_{(t) = {Uj(t)|j = 1, 2, ...} The set of all individual utility.} uS

j(t) The system performance on utility j.

US_{(t) = {u}S

j(t)|j = 1, 2, ...} The set of all system performances.

rki(t) The request for resource k from individual i.

Rk(t) = {rki(t)|i = 1, ..., |I|} The set of requests of resource k from all individuals.

R_{(t) = {Rk(t)|k = 1, ..., |S|} The set of all resource requests from all individuals.} C_{= {ck(t)|k = 1, ..., |S|} ck(t) is the capacity of resource k.}

xki(t) The amount of resource k that is allocated to

individual i.

Xk(t) = {xki(t)|i = 1, ..., |I|} The set of allocation of resource k for all individuals.

X_{(t) = {Xl(t)|l = 1, 2, ...} The set of all resource allocations for all individuals.}

F E The set of fairness elements. It can be either

resources or utility.

FE_{(t) The values of the set of fairness element at t. When}

resource allocation is considered, FE(t) = X(t); When utility is considered, FE(t) = U(t).

wli(t) The weight of individual i on fairness element l.

Wl(t) = {wli(t)|i = 1, ..., |I|} The set of individual weights on fairness element l.

W_{(t) = {Wl(t)|l =}

1, 2, ..., |F E|}

The set of all individual weights.

vli(t) The fairness utility of individual i on fairness element

l.

Vl(t) = {vli(t)|i = 1, ..., |I|} The fairness utility of all individuals on fairness

element l.

V_{(t) = {Vl(t)|l =}

1, 2, ..., |F E|}

The fairness utility of all individuals on all fairness elements.

fli(t) The individual fairness of individual i on fairness

element l.

Fi(t) The overall individual fairness of individual i.

FS

l (t) The system fairness on fairness element l.

FS_{(t) The overall system fairness.}

FJ