Resource Allocation in OFDM Based Wireless Relay Networks ...

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1.5 Contribution and Organization of the Thesis Figure 1.4: Organization of the thesis. two hops are jointly optimized via convex optimization techniques to maximize the end-to-end system throughput. Secondly, a Time Division Multiple Access (TDMA) based orthogonal transmission is considered. The power loading at the source/relay nodes and the sub-carrier matching over the two hops in each time slot are optimized in the developed algorithm. Finally, simulation results are provided to corroborate the proposed studies. The chapter 5 considers the multi-hop transmission scheme where more than one intermediate relaying nodes are deployed for reliable transmission in wireless sensor networks. The relay nodes in the sensor networks are consist of small batteries such that the replacement or recharging of batteries is not possible. Thus, we develop lifetime maximization algorithms where the optimization problem is formulated considering both the initial energy and the power constraint at each node. To reduce the energy utilization for decoding and encoding in DF relays and to minimize the noise enhancement with AF relay transmission, we consider a mixed AF-DF relay transmission. Convergence and the performance of the proposed iterative algorithm is shown by simulations. In chapter 6, we investigate the resource allocation problem in cognitive 13
1.5 Contribution and Organization of the Thesis relay transmission. The cognitive radio (CR) has been proposed to improve spectrum utilization by exploiting the less used spectrum in a dynamically changing environment. In the proposed algorithms, the secondary user’s throughput is maximized through the power allocation (at the secondary source node and the secondary relay node) and the sub-carrier pairing (on two hops of secondary system) subject to various power constraints. Meanwhile, the interference from both the secondary source and the secondary relay node to the primary receiver is kept within an acceptable limit. The numerical examples demonstrate enhanced performance compared to trivial cognitive relay resource allocation algorithms. In chapter 7, we conclude our work by highlighting the major contributions of the thesis. An overview of the thesis organization is shown in Fig. 1.4. 14
Page 1 and 2: Resource Allocation in OFDM Based W
Page 3 and 4: Dedications: To my family
Page 5 and 6: Acknowledgment I am greatly thankfu
Page 7 and 8: Contents 2.3.3 Proposed Low-Complex
Page 9 and 10: Abstract The combination of relay t
Page 11 and 12: List of Tables 3.1 Complexity compa
Page 13 and 14: List of Figures 4.1 System model fo
Page 15 and 16: List of Acronyms IEEE MU RS AWGN OW
Page 17 and 18: 1.1 Overview of Relay Networks wire
Page 19 and 20: 1.2 Overview of OFDM Figure 1.2: Th
Page 21 and 22: 1.3 Basics of the Optimization Theo
Page 23 and 24: 1.3 Basics of the Optimization Theo
Page 25 and 26: 1.4 Resource Allocation Problem is
Page 27: 1.5 Contribution and Organization o
Page 31 and 32: 2.1 Introduction allocated to a nod
Page 33 and 34: 2.1 Introduction The main contribut
Page 35 and 36: 2.2 System Model where h m,k denote
Page 37 and 38: 2.3 Resource Allocation Schemes opt
Page 39 and 40: 2.3 Resource Allocation Schemes ( )
Page 41 and 42: 2.3 Resource Allocation Schemes and
Page 43 and 44: 2.3 Resource Allocation Schemes tha
Page 45 and 46: 2.3 Resource Allocation Schemes 2.3
Page 47 and 48: 2.4 Generalization to Multiple Rela
Page 49 and 50: 2.4 Generalization to Multiple Rela
Page 51 and 52: 2.5 Simulation Results • OptSol/J
Page 53 and 54: 2.5 Simulation Results 0.9 0.85 0.8
Page 55 and 56: 2.6 Summary 2.6 2.4 2.2 2 Rate (bit
Page 57 and 58: 2.6 Summary 2.4 2.2 2 1.8 N=4 N=3 N
Page 59 and 60: 3.1 Introduction A new technology c
Page 61 and 62: 3.1 Introduction • Broadcast Phas
Page 63 and 64: 3.2 System Model MU MU 3 8 A 1 2 7
Page 65 and 66: 3.4 Joint Resource Allocation Schem
Page 67 and 68: 3.4 Joint Resource Allocation Schem
Page 69 and 70: 3.5 Step-Wise Low-Complexity Resour
Page 71 and 72: 3.6 Simulation Results and SNR B m,
Page 73 and 74: 3.6 Simulation Results 3 2.5 2 Uppe
Page 75 and 76: 3.7 Summary 1.8 1.6 1.4 JntOpt SubO
Page 77 and 78: 4.1 Introduction • Non-orthogonal
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4.3 Cooperative Non-Orthogonal Tran
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4.4 Optimization under Orthogonal T
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4.4 Optimization under Orthogonal T
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4.5 Simulations Then, the over all
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4.5 Simulations 40 30 20 Sum−ρ S
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4.6 Summary 0.9 0.8 0.7 JPSC Pow EP
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Chapter 5 Lifetime Maximization in
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5.1 Introduction Figure 5.2: Linear
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5.2 System Model antenna that canno
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5.4 Lifetime Maximization Scheme 5.
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5.4 Lifetime Maximization Scheme wh
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5.5 Simulation Results 150 OPT−SO
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5.6 Summary Lifetime (S) 150 100 50
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Chapter 6 Resource Allocation in Co
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7. Conclusions were considered for
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Bibliography [12] A. Peled, and A.
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Bibliography [36] Z. Luo and S. Zha
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Bibliography [61] D. H. N. Nguyen,
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Bibliography [84] A. Goldsmith, S.
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Bibliography [105] Y. Li, W. Wang,
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Appendix A Derivations of Closed-Fo
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B. Derivations of the Optimal Power
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