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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 吳安宇 | zh_TW |
| dc.contributor.advisor | An-Yeu Wu | en |
| dc.contributor.author | 蔡文喬 | zh_TW |
| dc.contributor.author | Wen-Chiao Tsai | en |
| dc.date.accessioned | 2024-08-05T16:27:27Z | - |
| dc.date.available | 2024-08-06 | - |
| dc.date.copyright | 2024-08-05 | - |
| dc.date.issued | 2024 | - |
| dc.date.submitted | 2024-07-30 | - |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/93542 | - |
| dc.description.abstract | 為了滿足第五代（Fifth Generation, 5G）無線通信系統的性能要求，人們已經開發了各種相關技術，如大規模多輸入多輸出（Massive Multiple-Input Multiple-Output, Massive MIMO）和毫米波（Millimeter-Wave, mmWave）通訊。然而，實現這些技術需要增加能源消耗和硬體成本，這可能不是未來第六代（Sixth Generation, 6G）無線網路的可擴展解決方案。近年來，智慧反射面板（Intelligent Reflecting Surface, IRS）已經成為具發展前景的技術，能夠以低成本高效益的方式增強頻譜效率和能源效率。透過智能地調控低成本的反射元件，IRS可以將入射信號反射到指定的方向，以提高通道容量和可靠性。然而，由於大量低成本的被動反射元件，並不具備信號感知和信號處理能力，因此通道狀態資訊（Channel State Information, CSI）的獲取需要大量的訓練負擔（Training Overhead）。
儘管利用壓縮感知（Compressive Sensing, CS）的技術可以減少訓練負擔，但傳統的CS算法在低訓練負擔下無法達到滿意的通道估測效能。在本論文中，我們利用深度展開（Deep Unfolding）技術來結合壓縮感知和深度學習（Deep Learning, DL）的優點。首先，我們提出了一種新型的超網絡輔助學習近似訊息傳遞（Learned Approximate Message Passing, LAMP）網絡，以資料驅動的方式提高通道估計的準確性。此外，我們提出了一種具有列壓縮的雙階段LAMP網絡（RCTS-LAMP），通過分解通道估測過程，實現了更好的復雜度和準確度之間的平衡。此外，RCTS-LAMP網絡還可以應用於直接通道（Direct Channel）和級聯通道（Cascaded Channel）的聯合估測，而無需額外的網絡。最後，通過從多測量向量（Multiple-Measurement Vector, MMV）角度解決多用戶通道估測問題，我們提出的RCTS-LAMP-MMV網絡可以顯著降低IRS輔助多用戶系統的訓練負擔。 | zh_TW |
| dc.description.abstract | To fulfill the performance requirements of the fifth-generation (5G) wireless communication system, a variety of enabling technologies have been developed, such as massive multiple-input multiple-output (MIMO) and millimeter-wave (mmWave) communication. However, implementing these techniques requires increasing energy consumption and hardware cost, which may not be a scalable solution for the sixth-generation (6G) wireless networks. Recently, intelligent reflecting surface (IRS) has emerged as a promising technique to enhance spectral and energy efficiency cost-effectively. By smartly reconfiguring the low-cost reflective elements, IRS can reflect the incident waves toward the desired directions to improve communication capacity and reliability. However, the acquisition of channel state information (CSI) requires large training overhead due to the large number of low-cost passive reflective elements that do not have the capabilities of sensing and signal processing.
Although the training overhead can be reduced with compressive sensing (CS) techniques, conventional CS algorithms cannot achieve satisfactory estimation performance under low training overhead. In this dissertation, we leverage the deep unfolding technique to combine the advantages of CS and deep learning (DL). First, we present a novel hypernetwork-assisted learned approximate message passing (LAMP) network to improve the channel estimation accuracy in a data-driven manner. Furthermore, we propose a two-stage LAMP network with row compression (RCTS-LAMP), which achieves a better trade-off between complexity and accuracy by decomposing the estimation process. Besides, the RCTS-LAMP network can be applied to the joint estimation of direct and cascaded channels without an extra network. Finally, by solving the multi-user channel estimation problem from the multiple-measurement vector (MMV) perspective, the proposed RCTS-LAMP-MMV network can significantly reduce the training overhead for IRS-aided multi-user systems. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2024-08-05T16:27:27Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2024-08-05T16:27:27Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 誌謝 I
摘要及關鍵詞 II Abstract & Keywords III Table of Contents IV List of Figures VII List of Tables IX Notations X Chapter 1 Introduction 1 1.1 BACKGROUND 1 1.1.1 INTELLIGENT REFLECTING SURFACE-AIDED COMMUNICATION SYSTEMS 1 1.2 CHANNEL ESTIMATION FOR IRS COMMUNICATION SYSTEMS 3 1.2.1 CHALLENGES OF IRS CHANNEL ESTIMATION 3 1.2.2 COMPRESSIVE SENSING FOR IRS CHANNEL ESTIMATION 4 1.2.3 DEEP LEARNING FOR IRS CHANNEL ESTIMATION 7 1.3 MOTIVATION AND DESIGN OBJECTIVE 8 1.3.1 ROBUST AND HARDWARE-FRIEDLY NETWORK DESIGN FOR PRACTICAL DEPLOYMENT 9 1.3.2 HIGH COMPUTATIONAL COMPLEXITY FOR JOINT CS RECOVERY 11 1.3.3 DIRECT CHANNEL ESTIMATION WITHOUT EXTRA NETWORK 11 1.3.4 IMPROVEMENT OF ESTIMATION EFFICIENCY FOR MULTI-USER SYSTEMS 13 1.4 RESEARCH CONTRIBUTIONS 14 1.5 DISSERTATION ORGANIZATION 18 Chapter 2 Review of Related Works 20 2.1 CASCADED CHANNEL ESTIMATION 20 2.1.1 CLASSICAL CHANNEL ESTIMATION SCHEMES 20 2.1.2 CS-BASED METHODS 23 2.2 DEEP UNFOLDING-BASED COMMUNICATION SYSTEM DESIGNS 27 2.3 SUMMARY 29 Chapter 3 Compressive Channel Estimation for IRS-Aided Systems with Hypernetwork-Assisted LAMP Network 30 3.1 SYSTEM MODEL AND PROBLEM FORMULATION 30 3.1.1 SYSTEM MODEL AND CHANNEL MODEL 30 3.1.2 PROBLEM FORMULATION 33 3.2 PROPOSED HYPERNETWORK-ASSISTED RECURRENT LAMP NETWORK 34 3.2.1 LAMP NETWORK 34 3.2.2 HYPERNETWORK ASSISTED DYNAMIC SHRINKAGE PARAMETERS 36 3.2.3 RECURRENT ARCHITECTURE WITH INCREMENTAL WEIGHT SHARING MECHANISM 37 3.3 PERFORMANCE EVALUATION 40 3.3.1 COMPARISON OF ESTIMATION PERFORMANCE FOR PROPOSED NETWORKS 41 3.3.2 COMPARISON OF TRAINING OVERHEAD 43 3.3.3 COMPARISON OF COMPUTATIONAL COMPLEXITY 43 3.4 SUMMARY 44 Chapter 4 Compressive Channel Estimation for IRS-Aided Systems Via Two-Stage LAMP Network with Row Compression 45 4.1 SYSTEM MODEL AND PROBLEM FORMULATION 45 4.1.1 SYSTEM MODEL AND CHANNEL MODEL 45 4.1.2 CHALLENGES OF THE JOINT CS METHOD 48 4.2 PROPOSED TWO-STAGE LAMP NETWORK WITH ROW COMPRESSION 51 4.2.1 PROPOSED TWO-STAGE LAMP NETWORK 52 4.2.2 ROW COMPRESSION BASED ON ROW SPARSITY 54 4.3 PERFORMANCE EVALUATION 56 4.3.1 BENCHMARKS 56 4.3.2 SIMULATION SETTING AND PERFORMANCE METRICS 58 4.3.3 COMPARISON OF CASCADED CHANNEL ESTIMATION PERFORMANCE 59 4.4 SUMMARY 63 Chapter 5 Joint Recovery of Direct and Cascaded Channels via Two-Stage LAMP Network with Row Compression 64 5.1 SYSTEM MODEL AND PROBLEM FORMULATION 64 5.1.1 SYSTEM MODEL AND CHANNEL MODEL 64 5.1.2 PROBLEM FORMULATION 65 5.2 JOINT ESTIMATION OF DIRECT AND CASCADED CHANNELS VIA THE PROPOSED RCTS-LAMP NETWORK 68 5.2.1 PROPOSED THREE-STAGE TRAINING PROCEDURE FOR THE JOINT OPTIMIZATION OF THE DIRECT AND CASCADED CHANNELS 69 5.2.2 WEIGHT-SHARING METHODS FOR THE FIRST LAMP NETWORK 73 5.3 PERFORMANCE EVALUATION 75 5.3.1 SIMULATION SETTING 75 5.3.2 JOINT ESTIMATION PERFORMANCE OF DIRECT AND CASCADED CHANNELS 76 5.4 SUMMARY 79 Chapter 6 Joint Channel Estimation for IRS-Aided Multi-User Communications Based on LAMP-MMV Network 81 6.1 SYSTEM AND CHANNEL MODELS 81 6.2 JOINT CHANNEL ESTIMATION FOR IRS-AIDED MULTI-USER SYSTEMS 84 6.2.1 DIRECT DECOMPOSITION WITH ORTHOGONAL PILOT SEQUENCES 84 6.2.2 PROPOSED RCTS-LAMP-MMV NETWORK 86 6.3 PERFORMANCE EVALUATION 90 6.3.1 COMPARISONS OF ESTIMATION PERFORMANCE AGAINST SNR 91 6.3.2 COMPARISONS OF ESTIMATION PERFORMANCE AGAINST TRAINING OVERHEAD 92 6.4 SUMMARY 94 Chapter 7 Conclusions and Future Works 95 7.1 DESIGN ACHIEVEMENTS 95 7.2 FUTURE WORKS 97 Appendix A Reversing the Order of the RCTS-LAMP Network 100 Appendix B Adaptability of the RCTS-LAMP Network 102 Bibliography 103 | - |
| dc.language.iso | en | - |
| dc.subject | 毫米波 | zh_TW |
| dc.subject | 智慧反射面板 | zh_TW |
| dc.subject | 深度展開 | zh_TW |
| dc.subject | 壓縮感知 | zh_TW |
| dc.subject | 通道估測 | zh_TW |
| dc.subject | channel estimation | en |
| dc.subject | deep unfolding | en |
| dc.subject | millimeter-wave | en |
| dc.subject | Intelligent reflecting surface | en |
| dc.subject | compressive sensing | en |
| dc.title | 基於深度展開之壓縮通道估測用於智慧反射面板輔助之無線通訊 | zh_TW |
| dc.title | Deep Unfolding-Based Compressive Channel Estimation for Intelligent Reflecting Surface-Aided Wireless Communications | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 112-2 | - |
| dc.description.degree | 博士 | - |
| dc.contributor.oralexamcommittee | 謝欣霖;黃穎聰;闕志達;蔡佩芸;沈中安 | zh_TW |
| dc.contributor.oralexamcommittee | Shin-Lin Shieh;Yin-Tsung Hwang;Tzi-Dar Chiueh;Pei-Yun Tsai;Chung-An Shen | en |
| dc.subject.keyword | 智慧反射面板,毫米波,通道估測,壓縮感知,深度展開, | zh_TW |
| dc.subject.keyword | Intelligent reflecting surface,millimeter-wave,channel estimation,compressive sensing,deep unfolding, | en |
| dc.relation.page | 107 | - |
| dc.identifier.doi | 10.6342/NTU202402708 | - |
| dc.rights.note | 同意授權(限校園內公開) | - |
| dc.date.accepted | 2024-08-01 | - |
| dc.contributor.author-college | 電機資訊學院 | - |
| dc.contributor.author-dept | 電子工程學研究所 | - |
| 顯示於系所單位： | 電子工程學研究所 | |
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