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Resource Allocation for Delay Constrained Wireless Communications PDF

200 Pages·2010·1.28 MB·English
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UNIVERSITY COLLEGE LONDON Resource Allocation for Delay Constrained Wireless Communications by Jia Chen A thesis submitted to Department of Electronic and Electrical Engineering in partial fulfillment for the degree of Doctor of Philosophy Email: j.chen @ee.ucl.ac.uk { } April 2010 Declaration of Authorship I, Jia Chen, confirm that the work presented in this thesis is my own. Where information has been derived from other sources, I confirm that this has been indicated in the thesis. Signed: Date: Abstract The ultimate goal of future generation wireless communications is to provideubiquitousseamlessconnectionsbetweenmobileterminalssuch as mobile phones and computers so that users can enjoy high-quality services at anytime anywhere without wires. The feature to provide a wide range of delay constrained applications with diverse quality of service (QoS) requirements, such as delay and data rate requirements, will require QoS-driven wireless resource allocation mechanisms to ef- ficiently allocate wireless resources, such as transmission power, time slots and spectrum, for accommodating heterogeneous mobile data. In addition, multiple-input-multiple-output (MIMO) antenna technique, which uses multiple antennas at the transmitter and receiver, can im- prove the transmission data rate significantly and is of particular in- terests for future high speed wireless communications. In the thesis, we develop smart energy efficient scheduling algorithms for delay constrained communications for single user and multi-user single-input-single-output (SISO) and MIMO transmission systems. Specifically, the algorithms are designed to minimize the total trans- mission power while satisfying individual user’s QoS constraints, such as rate, delay and rate or delay violation. Statistical channel informa- tion (SCI) and instantaneous channel state information (CSI) at the transmitter side are considered respectively, and the proposed design can be applied for either uplink or downlink. We propose to jointly deal with scheduling of the users that access to the channel for each 4 frame time (or available spectrum) and how much power is allocated when accessing to the channel. In addition, the algorithms are applied with modifications for uplink scheduling in IEEE 802.16 Worldwide Interoperability for Microwave Access (WiMAX). The success of the proposed research will significantly improve the ways to design wireless resource allocation for delay constrained communications. Contents Declaration of Authorship . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Acknowledgement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 Abbreviation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 1 Introduction 21 1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 1.1.1 Wireless Communications . . . . . . . . . . . . . . . . . . . 22 1.1.2 Wireless Resource Allocation . . . . . . . . . . . . . . . . . 24 1.1.3 Delay Constrained Communications . . . . . . . . . . . . . . 27 1.2 Contributions and List of Publications . . . . . . . . . . . . . . . . 32 1.3 Thesis Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 2 Wireless Channel Capacity 38 2.1 SISO Channel and its Capacity . . . . . . . . . . . . . . . . . . . . 38 6 2.1.1 SISO Channel Model . . . . . . . . . . . . . . . . . . . . . . 38 2.1.2 Channel Capacity with No Delay Constraint . . . . . . . . . 39 2.1.3 Channel Capacity with Delay Constraint . . . . . . . . . . . 42 2.2 MIMO Channel and its Capacity . . . . . . . . . . . . . . . . . . . 46 2.2.1 Channel Model . . . . . . . . . . . . . . . . . . . . . . . . . 46 2.2.2 Channel Capacity with No Delay Constraint . . . . . . . . . 47 2.2.3 Channel Capacity with Delay Constraint . . . . . . . . . . . 48 2.3 EC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 2.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 3 Mathematical Preliminaries 54 3.1 Convex Optimization Theory . . . . . . . . . . . . . . . . . . . . . 54 3.1.1 Convex Optimization Problem . . . . . . . . . . . . . . . . . 54 3.1.2 Convex Functions . . . . . . . . . . . . . . . . . . . . . . . . 55 3.1.2.1 Basic Properties . . . . . . . . . . . . . . . . . . . 55 3.1.2.2 Operations that Preserve Convexity . . . . . . . . 56 3.1.3 Solutions to Convex Optimization . . . . . . . . . . . . . . . 57 3.1.3.1 Lagrangian Duality Function and KKT Condition . 57 3.1.4 Jensen’s Inequality . . . . . . . . . . . . . . . . . . . . . . . 58 3.2 Nonlinear Optimization Theory . . . . . . . . . . . . . . . . . . . . 59 3.3 DP and Optimal Control Theory . . . . . . . . . . . . . . . . . . . 60 7 3.3.1 Basic Problem . . . . . . . . . . . . . . . . . . . . . . . . . . 60 3.3.2 Solving DP Problems . . . . . . . . . . . . . . . . . . . . . . 61 3.3.3 Problem Reformulations . . . . . . . . . . . . . . . . . . . . 61 4 Wireless Resource Allocation in Single User Systems with Trans- mitter SCI 63 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 4.2 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 4.3 Minimum Power for a Given Outage Probability . . . . . . . . . . . 67 4.4 Derivation of ρ and σ2 . . . . . . . . . . . . . . . . . . . . . . . . . 69 r r 4.5 Finding the Minimum Power Numerically . . . . . . . . . . . . . . . 73 4.6 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . 74 4.6.1 Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74 4.6.2 Benchmarks . . . . . . . . . . . . . . . . . . . . . . . . . . . 74 4.6.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75 4.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77 5 Wireless Resource Allocation in Single User Systems with Trans- mitter CSI 82 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83 5.2 System Model and Problem Formulation . . . . . . . . . . . . . . . 83 5.3 The Optimal Power Allocation . . . . . . . . . . . . . . . . . . . . . 85 8 5.3.1 The DP Algorithm for (5.4) . . . . . . . . . . . . . . . . . . 85 5.3.2 Complexity Analysis . . . . . . . . . . . . . . . . . . . . . . 89 5.4 The Proposed Suboptimal Algorithm . . . . . . . . . . . . . . . . . 90 5.4.1 Hyper DP with Forward Decision Per Block . . . . . . . . . 90 5.4.2 Complexity Analysis . . . . . . . . . . . . . . . . . . . . . . 93 5.4.3 Asymptotic-Optimality . . . . . . . . . . . . . . . . . . . . . 94 5.5 Performance Bounds . . . . . . . . . . . . . . . . . . . . . . . . . . 95 5.5.1 Lower Bound . . . . . . . . . . . . . . . . . . . . . . . . . . 95 5.5.2 A Simpler Closed-Form Method . . . . . . . . . . . . . . . . 95 5.5.3 Optimal Allocation with Acausal CSI . . . . . . . . . . . . . 97 5.6 Extension to MIMO BF Channels . . . . . . . . . . . . . . . . . . . 97 5.6.1 The Power-Rate Relationship . . . . . . . . . . . . . . . . . 97 5.6.2 Complexity of the DP Solutions . . . . . . . . . . . . . . . . 99 5.7 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 5.8 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102 6 Wireless Resource Allocation in Multi-user Systems with Trans- mitter SCI 107 6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108 6.2 System Model and Problem Formulation . . . . . . . . . . . . . . . 109 6.3 MPE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112 9 6.4 Multi-user Time-Sharing from Convex Optimization . . . . . . . . . 114 6.5 The Proposed Algorithm . . . . . . . . . . . . . . . . . . . . . . . . 118 6.6 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . 119 6.6.1 Simulation Setup and Benchmarks . . . . . . . . . . . . . . 119 6.6.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121 6.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123 7 Wireless Resource Allocation in Multi-user Systems with Trans- mitter CSI 128 7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129 7.2 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129 7.3 Single User Power Allocation . . . . . . . . . . . . . . . . . . . . . . 131 7.4 Multi-user Time-Sharing . . . . . . . . . . . . . . . . . . . . . . . . 133 7.5 The Proposed Method . . . . . . . . . . . . . . . . . . . . . . . . . 135 7.6 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . 135 7.6.1 Benchmarks . . . . . . . . . . . . . . . . . . . . . . . . . . . 136 7.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138 8 Wireless Resource Allocation inMulti-userSystems withECCon- straints 143 8.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144 8.2 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145 10 8.2.1 Single User MIMO Systems and EC . . . . . . . . . . . . . . 145 8.2.2 Multi-user MIMO Systems . . . . . . . . . . . . . . . . . . . 147 8.3 The Optimal Power Allocation for single user MIMO Systems . . . 149 8.4 Multi-user MIMO-TDMA with EC Constraints . . . . . . . . . . . 150 8.4.1 The Optimal DP Solution . . . . . . . . . . . . . . . . . . . 150 8.4.2 A Suboptimal Convex Optimization Approach . . . . . . . . 151 8.5 Multi-user MIMO-FDMA with EC Constraints . . . . . . . . . . . . 154 8.5.1 The Optimal DP Solution . . . . . . . . . . . . . . . . . . . 154 8.5.2 A Suboptimal Convex Optimization Approach . . . . . . . . 154 8.6 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . 156 8.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157 9 Scheduling in WiMAX 159 9.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159 9.1.1 A Brief Review of the WiMAX Physical Layer . . . . . . . . 161 9.2 Uplink Scheduling for Single User System . . . . . . . . . . . . . . . 164 9.2.1 Uplink Throughput . . . . . . . . . . . . . . . . . . . . . . . 164 9.2.2 Problem Formulation and Solutions . . . . . . . . . . . . . . 165 9.3 Uplink Scheduling for Multi-user System . . . . . . . . . . . . . . . 168 9.3.1 Problem Formulation and Solutions . . . . . . . . . . . . . . 168 9.3.2 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . 169 9.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 174

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For delay constrained communications, QoS is traditionally considered as the guar-antee of a fixed data rate under a decoding delay constraint, or a specified queueing
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