Resource Allocation in OFDM Based Wireless Relay Networks ...

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1.2 Overview of OFDM his idea of the parallel transmissions over multiple channels [11]. He developed a principle for transmitting messages simultaneously through orthogonal channels that is free of inter-channel interference (ICI) and the ISI. In OFDM systems, the available bandwidth is divided into K sub-channels (sub-carriers) and data is transmitted in parallel on these sub-carriers. Different modulation schemes can be used for different sub-carriers. To combat ISI caused by channel time spread, a cyclic prefix (CP) that duplicated last portion of an OFDM block is inserted in the front of OFDM block [12]. As long as the length of CP is greater than the impulse response of the channel, perfect orthogonality among sub-channels is achieved and the effect of ISI caused by the arrival of different OFDM symbols with different delays is completely eliminated. A block structure of CP based OFDM symbol is shown in Fig. 1.2. To transmit data via OFDM symbol, a serial to parallel conversion of bit stream is performed in the OFDM transmitter. The transmitter transforms this parallel bit stream into the time domain using an Inverse Discrete Fourier Transform (IDFT). In practical systems, the Inverse Fast Fourier Transform (IFFT) algorithm is used due Figure 1.3: OFDM transceiver: block diagram 5
1.3 Basics of the Optimization Theory to its much higher processing efficiency. Finally, CP is inserted before actual data transmission. The receiver section performs the reverse operation of the transmitter. It removes CP and then transforms the signal back to frequency domain using fast Fourier transform (FFT). The block diagram of a typical OFDM transceiver is shown in Fig. 1.3. 1.3 Basics of the Optimization Theory The design of wireless systems often involve the optimization of an objective subject to certain resource constraints. An optimization problem with arbitrary equality and inequality constraint can be written as [13] min x f o (x) (1.1) subject to f i (x) ≤ 0, 1 ≤ i ≤ m, h i (x) = 0, 1 ≤ i ≤ p, where x is the optimization variable, f o is the objective function, f 1 , ......, f m are the m inequality constraint functions, and h 1 , ......, h p are the p equality constraint functions. The optimization problems could be grouped into different classes depending on the nature of the objective function and the constraint functions. The problem is called linear programming if the objective and all constraint functions are linear. The linear programming problems can be solved effectively using the well known Dantzig’s simplex method, the criss-cross algorithm, and the recently developed interior point methods [13]. The problem is called non-linear optimization if the objective and/or constraint functions are non-linear and non-convex [14]. The convex optimization theory gives an easy solution if the problem is, or can be transformed into, convex problem. optimization problem if the objective function f o A problem is called a convex and the inequality constraint functions f 1 , ......, f m are convex and the equality constraint functions h 1 , ......, h m are affine. Convex optimization problems can be optimally solved either in closed form 6
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: 1.2 Overview of OFDM Figure 1.2: Th
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 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
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3.6 Simulation Results and SNR B m,
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3.6 Simulation Results 3 2.5 2 Uppe
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3.7 Summary 1.8 1.6 1.4 JntOpt SubO
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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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