Resource Allocation in OFDM Based Wireless Relay Networks ...

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2.5 Simulation Results 2.5 2 Dual Solution (D(ν,λ)/K) OptSol SubOpt EP−ECarr EP−RndCarr rate (bits/s/Hz) 1.5 1 0.5 0 0 5 10 15 20 SNR (dB) Figure 2.3: Rate versus SNR for M = 10 and N = 1. However, the gap between the SubOpt and OptSol is almost constant, which shows the robustness of the proposed sub-optimal algorithm. Interestingly, the average throughput increase with M but tends to a constant value when M is greater than 10. The threshold indicates the point when the single relay cannot support more multi-user diversity. The performance can be further improved if we apply more relays. This will be demonstrated in the later examples. Now, we look into multiple relay scenario. To compare the results with the recent work [32], JSP-(with matching) shows the performance of the solution presented in Algorithm 3 [32], where sub-carrier assignment and power allocation problem is solved through primal decomposition approach. Further, a sub-carrier pairing policy is also developed in this algorithm. The throughput performance of five different methods versus SNR for a system with M = 10 users and N = 5 are shown in Fig. 2.5. The objective value of the dual problem at the solution points is also displayed in the same figure. We first observe that the duality gap between the dual objective and JntSol is close to zero 37
2.5 Simulation Results 0.9 0.85 0.8 rate (bits/s/Hz) 0.75 0.7 0.65 0.6 OptSol SubOpt EP−ECarr EP−RndCarr 0.55 0.5 0.45 0.4 2 4 6 8 10 12 14 16 18 M Figure 2.4: Throughput versus the number of users for N = 1. at all SNR region, which demonstrates the optimality of the proposed algorithm. This is consistent with the result presented in [35]. We also see that JntSol exhibits the best performance at all SNR values among all different methods. It yields 2.5 dB SNR gain over JSP-(with matching). The reason is that JSP-(with matching) does not exploit diversity from multiple relay stations and only utilizes a suboptimal pairing strategy. Moreover, JntSol achieves large performance gains over two trivial methods, i.e., EP-ECarr and EP-RndCarr methods. In comparison, the rate loss of the proposed SubOpt is only 0.6 dB but it requires less computational complexity. Next we examine the performance of the end-to-end rate versus the number of MUs for N = 5 relay system and the corresponding curves at SNR= 10 dB are shown in Fig. 2.6. Similar to the previous example, we see that JntSol always yields the best performance among different methods. The gain of JntSol over EP-ECarr and EP-RndCarr becomes large when the number of the users increases due to two facts: i) the performance loss without optimization will become significant; ii) no multi-user diversity is exploited. However, the gap between the SubOpt and JntSol is 38
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 and 28: 1.5 Contribution and Organization o
Page 29 and 30: 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: 2.5 Simulation Results • OptSol/J
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
Page 79 and 80: 4.3 Cooperative Non-Orthogonal Tran
Page 87 and 88: 4.4 Optimization under Orthogonal T
Page 89 and 90: 4.4 Optimization under Orthogonal T
Page 91 and 92: 4.5 Simulations Then, the over all
Page 93 and 94: 4.5 Simulations 40 30 20 Sum−ρ S
Page 95 and 96: 4.6 Summary 0.9 0.8 0.7 JPSC Pow EP
Page 97 and 98: Chapter 5 Lifetime Maximization in
Page 99 and 100: 5.1 Introduction Figure 5.2: Linear
Page 101 and 102: 5.2 System Model antenna that canno
Page 103 and 104:
5.4 Lifetime Maximization Scheme 5.
Page 105 and 106:
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
Page 115 and 116:
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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Resource Allocation in OFDM Based Wireless Relay Networks ...

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