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姓名	張漢魁(Han-Kui Chang)  查詢紙本館藏	畢業系所	通訊工程學系
論文名稱	(Channel Quantization, Power Control and Link Scheduling Strategies for Relaying and Energy Harvesting Communications)
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摘要(中)	隨著高資料量傳輸服務與多樣性應用的快速發展，高傳輸速率、低延遲、更佳的能量效率及更穩定的鏈結能力為下一代的無線通訊系統所具備的。合作式通訊網路與能量獵取技術在其中扮演了重要角色。首先，在第一部分的研究中，於中繼系統研究量化處理的傳送端到中繼端通道資訊對於符元錯誤率影響的效能分析。合作式通訊網路因中繼系統的協助獲取空間多樣性資源；然而，為了獲取完整的空間多樣性資源，接收端必需獲取完整的通道狀態資訊，包含：傳送端到接收端、傳送端到中繼端及中繼端到接收端的通道資訊。因傳送端到中繼端的通道資訊無法於接收端直接獲得，在考慮分解通道估計於中繼系統，為了減少傳送端到中繼端通道資訊向後傳給接收端所衍生的頻寬與複雜度問題，我們研究通道資訊的量化處理對於系統效能的影響與可應用的量化方法。研究內容主要建立放大中繼系統於量化通道訊息下的接收訊雜比訊號模型，分析與推導此訊雜比訊號模型的機率密度分布函數上限與下限，進而得到符元錯誤率的理論分析結果，並以實驗模擬驗證有效性。此外，我們提出等機率分割結合最小均方誤差準則的非遞迴量化方法以減少量化誤差對於系統效能的影響；更進一步，將其結果結合Lloyd-Max方法以遞迴方式所得到的結果，只要3個位元數的訊息量以表示傳送端到中繼端的通道資訊，系統訊雜比仍可維持在優異的效能。在第二部分的研究中，於太陽能供電的雙向通訊系統中分析採集能量及通道衰落的變化對於系統效能的影響。於供電的雙向通訊系統中我們考慮分時傳輸的架構，因能量獵取所導致能量供給模型的不定性，分時傳輸架構的資源分配需要重新被設計。基於馬可夫決策過程的設計方法，我們提出統計型的資源分配方法以降低系統的平均中斷機率。此提出的方法可依據雙向通訊系統中兩個節點之間太陽能採集的狀態、通道變化的狀態及電池儲存狀態，適當地能源分配與鏈結選擇已達到最佳的系統效能。此外，我們分析電池儲存狀態與馬可夫決策結果之間的關係。經電腦模擬結果可證實馬可夫決策過程的設計方法可讓分時的雙向通訊系統達到最小的平均中斷機率。
摘要(英)	As demands for increased data rate services and diverse applications grow tremendously, higher data rates, reduced latencies, better energy efficiency, and reliable connectivity are required in wireless communications in the future. Cooperative communication and energy harvesting techniques play the key role to achieve these requirements. In the first part, cooperative communication assisted by relays is regarded as an effective means of achieving spatial diversity. The achievable diversity gain relies on the channel state information (CSI) that can be acquired at the destination because spatial diversity combining unavoidably requires the disintegrated channel information. To reduce signaling overhead, quantization and compression are applied prior to the delivery of the source-to-relay (SR) CSI to the destination. In this dissertation, an amplify-and-forward (AF) relay network while considering the impact of the quantization of the SR CSI is studied. Several quantization methods relying on the statistics of the SR CSI are investigated. The probability density functions (PDFs) of the upper and lower bounds of the signal-to-noise ratio (SNR) and the corresponding symbol-error rate (SER) achieved at the destination are derived analytically. Monte Carlo simulations are performed to compare the simulation results with the analytical results of the SER bounds and to verify the effectiveness of the proposed channel quantizer. A combined method of determining the quantization interval based on equal-probability partitioning (EPP) and determining the representative quanta based on the minimum-mean-square-error (MMSE) criterion is proposed and is verified to have lowest quantization error among the non-iterative quantization methods compared here. It is shown via the simulations that when using an iterative procedure based on the Lloyd-Max process, $3$ bits per dimension are sufficient to achieve negligible SNR degradation. In the Second part, a solar-powered bidirectional communication system is studied for a pair of energy harvesting (EH) nodes that intend to communicate with each other over wireless fading channels. The conventional time-division duplex (TDD) transmission is revisited by proposing a stochastic resource scheduling scheme to minimize an average rate outage probability based on a Markov decision process (MDP) design framework. Different from the conventional TDD transmission, the proposed scheme can adjust the link direction and energy expenditure for data transmissions between the two EH nodes, in response to the dynamics of the solar EH, channel fading and battery storage conditions. A downstairs threshold structure is theoretically proved under a special optimal on-off policy, in which two-dimensional thresholds pinpoint the interplay between the transmission actions and the available energy in the batteries of the two nodes. Also the optimal on-off policy at asymptotically high signal-to-noise ratios (SNRs) is revealed. The outage performance of the proposed stochastic resource scheduling scheme is validated by extensive computer simulations, and it shows that the proposed optimal MDP policy can achieve significant performance gains over the combinations of other compared schemes, including round-robin and battery state-oriented link scheduling schemes, and greedy and conservative energy scheduling schemes.
關鍵字(中)	★ 中繼系統 ★ 通道估測 ★ 量化 ★ 能量採集 ★ 太陽能 ★ 資源分配	關鍵字(英)
論文目次	Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . i Acknowledgement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vi List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viii List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix 1 Introduction 1 1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Overview and Issue: Cooperative Communication . . . . . . . . . . . . . . 3 1.2.1 Multiple Input Multiple Output Systems . . . . . . . . . . . . . . . 3 1.2.2 Two-Hop Relay Networks . . . . . . . . . . . . . . . . . . . . . . . 4 1.2.3 Relaying Protocols . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.2.4 Channel Estimation Methods for AF relaying . . . . . . . . . . . . 5 1.3 Overview and Issue: Energy Harvesting . . . . . . . . . . . . . . . . . . . . 7 1.3.1 Energy Sources of EH . . . . . . . . . . . . . . . . . . . . . . . . . 7 1.3.2 Energy Models of Solar Source . . . . . . . . . . . . . . . . . . . . . 11 1.3.3 Data-Driven Solar Stochastic Model [48] . . . . . . . . . . . . . . . 12 1.3.4 EH Bidirectional Time Division Duplex . . . . . . . . . . . . . . . . 13 1.4 Organization of the Dissertation . . . . . . . . . . . . . . . . . . . . . . . . 15 2 Impact of Imperfect Source-to-Relay CSI in Amplify-and-Forward Relay Networks 16 2.1 Related Works and Organization of This Work . . . . . . . . . . . . . . . . 16 2.2 Preliminary: Signal Model and Channel Quantization . . . . . . . . . . . . 18 2.2.1 Signal Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 2.2.2 Channel Quantization . . . . . . . . . . . . . . . . . . . . . . . . . 20 2.3 Signal-to-Noise Ratio and Symbol Error Rate Derivations . . . . . . . . . . 21 2.3.1 CSI-Assisted Relay Gain Determination . . . . . . . . . . . . . . . 22 2.3.2 Semi-blind Relay Gain Determination . . . . . . . . . . . . . . . . . 25 2.4 Quantization Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 2.4.1 Possible Choices for I . . . . . . . . . . . . . . . . . . . . . . . . . 27 2.4.2 Possible Choices for M . . . . . . . . . . . . . . . . . . . . . . . . . 29 2.5 Simulations and Numerical Results . . . . . . . . . . . . . . . . . . . . . . 33 2.5.1 Low-CQE Regime . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 2.5.2 Non-iterative Quantization . . . . . . . . . . . . . . . . . . . . . . . 34 2.5.3 Iterative Quantization . . . . . . . . . . . . . . . . . . . . . . . . . 37 2.6 Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 2.7 Appendix 2.A: Derivation of (2.30) . . . . . . . . . . . . . . . . . . . . . . 45 3 On Stochastic Link and Energy Scheduling for Energy Harvesting Bidi- rectional Communications 47 3.1 Related Works and Organization of This Work . . . . . . . . . . . . . . . . 47 3.2 Solar-Powered Bidirectional Communications . . . . . . . . . . . . . . . . . 51 3.3 Markov Decision Process with Stochastic Models . . . . . . . . . . . . . . . 53 3.3.1 MDP System Sates . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 3.3.2 Link and Energy Scheduling Actions . . . . . . . . . . . . . . . . . 54 3.3.3 MDP State Transition . . . . . . . . . . . . . . . . . . . . . . . . . 54 3.3.4 Cost Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 3.3.5 Optimization of Resource Scheduling Policy . . . . . . . . . . . . . 59 3.4 Analysis Of Optimal On-O Link and Energy Scheduling Policy . . . . . . 60 3.4.1 Monotonic and Bounded Expected Total Discounted Rewards for Optimal On-O Policy . . . . . . . . . . . . . . . . . . . . . . . . . 61 3.4.2 Downstairs Threshold Structural Properties of Optimal On-O Policy 63 3.4.3 Optimal On-O Policy at Asymptotically High SNRs . . . . . . . . 66 3.4.4 Performance Analysis of Overall Outage Probability . . . . . . . . . 68 3.5 Numerical Results and Discussions . . . . . . . . . . . . . . . . . . . . . . 69 3.5.1 Heuristic Link and Energy Scheduling Policies . . . . . . . . . . . . 69 3.5.2 Simulation Settings . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 3.5.3 Simulation results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 3.5.4 Implementation Complexity and Overhead . . . . . . . . . . . . . . 78 3.6 Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80 3.7 Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82 3.7.1 Appendix 3.A: Proof of Lemma 3.4.1 . . . . . . . . . . . . . . . . . 82 3.7.2 Appendix 3.B: Proof of Corollary 1 . . . . . . . . . . . . . . . . . . 84 3.7.3 Appendix 3.C: Proof of Lemma 3.4.2 . . . . . . . . . . . . . . . . . 86 3.7.4 Appendix 3.D: Proof of Theorem 3.4.2 . . . . . . . . . . . . . . . . 88 3.7.5 Appendix 3.E: Proof of Theorem 3.4.3 . . . . . . . . . . . . . . . . 90 3.7.6 Appendix 3.F: Proof of Theorem 3.4.4 . . . . . . . . . . . . . . . . 90 3.7.7 Appendix 3.G: Proof of Theorem 3.4.5 . . . . . . . . . . . . . . . . 91 4 Conclusions 92 Bibliography 94 5 Accepted/Submitted Papers 106
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指導教授	林嘉慶 古孟霖(Jia-Chin Lin Meng-Lin Ku)	審核日期	2020-8-24
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