博碩士論文 995403601 詳細資訊




以作者查詢圖書館館藏 以作者查詢臺灣博碩士 以作者查詢全國書目 勘誤回報 、線上人數:43 、訪客IP:34.231.21.105
姓名 新可夫(keshav Singh)  查詢紙本館藏   畢業系所 通訊工程學系
論文名稱 Performance Optimization of Spectrum and Energy Efficiency for Wireless Relay Networks
(Performance Optimization of Spectrum and Energy Efficiency for Wireless Relay Networks)
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摘要(中) Explosive growth in data rate put some serious challenges on wireless system designers and link budget planning. Low transmit power, system coverage and capacity, high data rates, spatial diversity and quality of services (QOS) are the key factors in future wireless communication system that made it attractive. Dual-hop relaying is the promising underlying technique for future wireless communications to address such dilemmas. The objective of this dissertation is to optimize the performance of dual-hop amplify-and-forward (AF) relay networks to enhance the energy and spectral efficiency.
Multiple-input multiple-output (MIMO) relays provide the high throughput of MIMO communication with the coverage extension capability of relay transmission. But one of the main limitations of MIMO relay network is effectively managing the intersymbol interference (ISI) and multiple antenna interference (MAI). In the first part of this dissertation, an equalize-and-forward (EF) relaying schemes are designed to efficiently mitigate the interference generated due to multipath channels, by jointly optimize equalizer weights and power allocation for dual-hop MIMO relay networks with full/partial channel state information (CSI) knowledge. We then extend the design to a more general case in which the direct link between the source and the destination is taken into account. Furthermore, two relay selection algorithms based on the allocated power and the mean square error (MSE) performance are investigated for the two scenarios which attain a performance that is comparable to that of cases with brute-force search or without relay selection.
In second part of this dissertation, we extend relay selection algorithms based on the allocated power to a perturbation-based power allocation and multi-relay selection for further enhancement of performance of MIMO relay networks. In this approach, the relays are partitioned into two groups according to Lagrangian multipliers of power constraints. The power allocation for the relays is perturbed by increasing the power for the potential relay’s group, while decreasing the power of the relays in the other group. An optimization framework is then formulated as a trade-off between the relay selection and the mean square error (MSE) performance degradation.
A pricing-based approach is proposed in third part of this dissertation to achieve energy-efficient power allocation in relay-assisted multiuser networks. We consider a network price to the power consumption as a penalty for the achievable sum rate, and study its impact on the tradeoff between the energy efficiency (EE) and the spectral efficiency (SE). Due to non-convex nature of the original problem, it is It is hard to directly solve it, and thus a concave lower bound on the pricing-based utility is applied to transform the problem into a convex one. Through dual decomposition, a q-price algorithm is proposed for iteratively tightening the lower bound and finding the optimal solution. In addition, an optimal price that enables green power allocation is defined and found from the viewpoint of maximizing EE. Moreover, we also analyze the optimal power allocation strategies of the pricing-based approach in a two-user case under different noise operating regimes, yielding on-off, water-filling, and channel-reversal approaches. We then prolong this idea for two-way relay networks as in fourth part of this dissertation and propose a novel energy-efficient power allocation schemes to improve the EE in multiuser multi-carrier two-way relay networks which are able to not only balance the EE of the two-way links but also ensure the quality-of-service (QoS).
The last part of this dissertation is dedicated to the design of a novel joint source and relay transmit power allocation and energy transfer schemes to maximize the network sum rate within a deadline subject to energy causality and battery storage constraints. The non-convex sum rate optimization problem is transformed into a solvable convex optimization problem using a successive convex approximation for low-complexity (SCALE) algorithm. The effect of node′s battery capacity and energy harvesting profiles on the network sum rate maximization are investigated.
摘要(英) Explosive growth in data rate put some serious challenges on wireless system designers and link budget planning. Low transmit power, system coverage and capacity, high data rates, spatial diversity and quality of services (QOS) are the key factors in future wireless communication system that made it attractive. Dual-hop relaying is the promising underlying technique for future wireless communications to address such dilemmas. The objective of this dissertation is to optimize the performance of dual-hop amplify-and-forward (AF) relay networks to enhance the energy and spectral efficiency.
Multiple-input multiple-output (MIMO) relays provide the high throughput of MIMO communication with the coverage extension capability of relay transmission. But one of the main limitations of MIMO relay network is effectively managing the intersymbol interference (ISI) and multiple antenna interference (MAI). In the first part of this dissertation, an equalize-and-forward (EF) relaying schemes are designed to efficiently mitigate the interference generated due to multipath channels, by jointly optimize equalizer weights and power allocation for dual-hop MIMO relay networks with full/partial channel state information (CSI) knowledge. We then extend the design to a more general case in which the direct link between the source and the destination is taken into account. Furthermore, two relay selection algorithms based on the allocated power and the mean square error (MSE) performance are investigated for the two scenarios which attain a performance that is comparable to that of cases with brute-force search or without relay selection.
In second part of this dissertation, we extend relay selection algorithms based on the allocated power to a perturbation-based power allocation and multi-relay selection for further enhancement of performance of MIMO relay networks. In this approach, the relays are partitioned into two groups according to Lagrangian multipliers of power constraints. The power allocation for the relays is perturbed by increasing the power for the potential relay’s group, while decreasing the power of the relays in the other group. An optimization framework is then formulated as a trade-off between the relay selection and the mean square error (MSE) performance degradation.
A pricing-based approach is proposed in third part of this dissertation to achieve energy-efficient power allocation in relay-assisted multiuser networks. We consider a network price to the power consumption as a penalty for the achievable sum rate, and study its impact on the tradeoff between the energy efficiency (EE) and the spectral efficiency (SE). Due to non-convex nature of the original problem, it is It is hard to directly solve it, and thus a concave lower bound on the pricing-based utility is applied to transform the problem into a convex one. Through dual decomposition, a q-price algorithm is proposed for iteratively tightening the lower bound and finding the optimal solution. In addition, an optimal price that enables green power allocation is defined and found from the viewpoint of maximizing EE. Moreover, we also analyze the optimal power allocation strategies of the pricing-based approach in a two-user case under different noise operating regimes, yielding on-off, water-filling, and channel-reversal approaches. We then prolong this idea for two-way relay networks as in fourth part of this dissertation and propose a novel energy-efficient power allocation schemes to improve the EE in multiuser multi-carrier two-way relay networks which are able to not only balance the EE of the two-way links but also ensure the quality-of-service (QoS).
The last part of this dissertation is dedicated to the design of a novel joint source and relay transmit power allocation and energy transfer schemes to maximize the network sum rate within a deadline subject to energy causality and battery storage constraints. The non-convex sum rate optimization problem is transformed into a solvable convex optimization problem using a successive convex approximation for low-complexity (SCALE) algorithm. The effect of node′s battery capacity and energy harvesting profiles on the network sum rate maximization are investigated.
關鍵字(中) ★ Energy-efficient
★ Relay networks
★ Resource allocations
★ Amplify-and-forward scheme
關鍵字(英)
論文目次 Acknowledgement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . i
Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ii
Declaration of Authorship . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii
Acronyms and Symbols . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v
Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viii
List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiv
List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xv
1 Introduction 1
1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Overview: Cooperative Communication . . . . . . . . . . . . . . . . . . . . 3
1.2.1 Multiple Input Multiple Output Systems . . . . . . . . . . . . . . . 3
1.2.2 Two-Hop Relay Networks . . . . . . . . . . . . . . . . . . . . . . . 3
1.2.3 Relaying Protocols . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.3 Dissertation Outline and Contributions . . . . . . . . . . . . . . . . . . . . 5
2 Joint Power Allocation, Equalization and Relay Selection for MIMO
Relay Networks with Multipath Receptions 8
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2.2 Preliminary: System Model . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.3 Scenario 1: TS Relay Equalizer Design with Full CSI . . . . . . . . . . . . 18
2.4 Scenario 2: TS Relay and Destination Equalizer Design with Backward CSI 22
2.4.1 Suboptimal TS Relay Equalizers . . . . . . . . . . . . . . . . . . . . 22
2.4.2 Suboptimal TS Destination Equalizer . . . . . . . . . . . . . . . . . 23
2.5 Extension to MIMO Relay Networks with Direct Link . . . . . . . . . . . . 25
2.5.1 Destination Equalizer for Direct Link in Scenario 1 . . . . . . . . . 26
2.5.2 Destination Equalizer for Direct Link in Scenario 2 . . . . . . . . . 27
2.6 Computational Complexity . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
2.7 Relay Selection Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
2.8 Performance and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . 30
2.8.1 Simulation Parameters . . . . . . . . . . . . . . . . . . . . . . . . . 30
2.8.2 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
2.9 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
2.10 Appendix 2.A: Proof of Theorem 2.3.1 . . . . . . . . . . . . . . . . . . . . 41
3 Power Allocation and Relay Selection in Relay Networks: A Perturbation-
Based Approach 42
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
3.2 Problem Formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
3.2.1 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
3.2.2 Optimization Problem . . . . . . . . . . . . . . . . . . . . . . . . . 46
3.3 Perturbation-based Power Allocation and Relay Selection . . . . . . . . . . 48
3.4 Performance Evaluation and Discussions . . . . . . . . . . . . . . . . . . . 51
3.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
4 Toward Green Power Allocation in Relay-Assisted Multiuser Networks:
A Pricing-Based Approach 55
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
4.2 Relay-assisted Multiuser Network . . . . . . . . . . . . . . . . . . . . . . . 59
4.2.1 Signal Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
4.2.2 Energy Consumption Model . . . . . . . . . . . . . . . . . . . . . . 63
4.3 Power Allocation and Pricing Solutions . . . . . . . . . . . . . . . . . . . . 64
4.3.1 Pricing-based Power Allocation Problem . . . . . . . . . . . . . . . 64
4.3.2 Optimal q-Price Power Allocation Algorithm . . . . . . . . . . . . . 66
4.3.3 Optimal Price q? for Achieving Energy-Ecient Transmission . . . 70
4.4 Analysis of Optimal Solutions for Two-User Cases . . . . . . . . . . . . . . 73
4.4.1 Interference-Dominated Regime . . . . . . . . . . . . . . . . . . . . 73
4.4.2 Relay Noise-Dominated Regime . . . . . . . . . . . . . . . . . . . . 74
4.4.3 Destination Noise-Dominated Regime . . . . . . . . . . . . . . . . . 75
4.5 Computer Simulations and Performance Discussions . . . . . . . . . . . . . 77
4.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
4.7 Appendix 4.B: Proof of Lemma 4.3.1 . . . . . . . . . . . . . . . . . . . . . 88
4.8 Appendix 4.C: Proof of Theorem 4.3.1 . . . . . . . . . . . . . . . . . . . . 89
4.9 Appendix 4.D: Proof of Theorem 4.3.2 . . . . . . . . . . . . . . . . . . . . 90
4.10 Appendix 4.E: Proof of Theorem 4.3.3 . . . . . . . . . . . . . . . . . . . . 91
4.11 Appendix 4.F: Proof of Theorem 4.3.4 . . . . . . . . . . . . . . . . . . . . 92
4.12 Appendix 4.G: Proof of Theorem 4.4.1 . . . . . . . . . . . . . . . . . . . . 92
4.13 Appendix 4.H: Proof of Theorem 4.4.2 . . . . . . . . . . . . . . . . . . . . 93
5 Joint QoS-Promising and EE-Balancing Power Allocation for Two-Way
Relay Networks 95
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95
5.2 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97
5.2.1 Signal Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97
5.2.2 Power Consumption Model . . . . . . . . . . . . . . . . . . . . . . . 99
5.3 QoS-Promising and EE-Balancing Power Allocation . . . . . . . . . . . . . 100
5.3.1 Problem Formulation . . . . . . . . . . . . . . . . . . . . . . . . . . 100
5.3.2 Power Allocation Algorithm . . . . . . . . . . . . . . . . . . . . . . 102
5.4 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104
5.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105
6 Power Allocation and Transfer in SWIPT Amplify-and-Forward Relay
Networks with Energy Harvesting 107
6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107
6.2 System Model and Problem Formulation . . . . . . . . . . . . . . . . . . . 111
6.2.1 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111
6.2.2 Optimization Problem Formulation . . . . . . . . . . . . . . . . . . 113
6.3 Transformation into Convex Optimization Problem . . . . . . . . . . . . . 115
6.4 Optimal Power Allocation Algorithm and Energy Transfer . . . . . . . . . 117
6.4.1 Subproblem Solution: Update of Ps, Pr and  . . . . . . . . . . . . 118
6.4.2 Solution of the Master Dual Problem: Update of Lagrange Multipliers120
6.5 SPECIAL SCENARIOS . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122
6.5.1 Source Battery Capacity Es;max = 1 . . . . . . . . . . . . . . . . . 123
6.5.2 Relay Battery Capacity Er;max = 1 . . . . . . . . . . . . . . . . . . 125
6.5.3 Source and Relay Battery Capacity Es;max = Er;max = 1 . . . . . . 126
6.6 Numerical Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128
6.7 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135
6.8 Appendix 6.A: Proof of Lemma 6.5.1 . . . . . . . . . . . . . . . . . . . . . 136
6.9 Appendix 6.B: Proof of Theorem 6.5.1 . . . . . . . . . . . . . . . . . . . . 136
6.10 Appendix 6.C: Proof of Theorem 6.5.2 . . . . . . . . . . . . . . . . . . . . 138
6.11 Appendix 6.D: Proof of Theorem 6.5.5 . . . . . . . . . . . . . . . . . . . . 139
7 Conclusions And Future Directions 142
7.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142
7.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144
7.2.1 Energy-ecient . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144
7.2.2 Green Energy Harvesting . . . . . . . . . . . . . . . . . . . . . . . . 145
Bibliography 146
8 Accepted/Submitted Papers 159
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指導教授 林嘉慶、古孟霖(Jia-Chin Lin Meng-Lin Ku) 審核日期 2015-10-7
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