請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/101389完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 吳安宇 | zh_TW |
| dc.contributor.advisor | An-Yeu Wu | en |
| dc.contributor.author | 陳祈瑋 | zh_TW |
| dc.contributor.author | Chi-Wei Chen | en |
| dc.date.accessioned | 2026-01-27T16:27:18Z | - |
| dc.date.available | 2026-01-28 | - |
| dc.date.copyright | 2026-01-27 | - |
| dc.date.issued | 2026 | - |
| dc.date.submitted | 2026-01-13 | - |
| dc.identifier.citation | [1] Z. Zhou, N. Ge, Z. Wang, and L. Hanzo, “Joint transmit precoding and reconfigurable intelligent surface phase adjustment: A decomposition-aided channel estimation approach,” IEEE Trans. Commun., vol. 69, no. 2, pp. 1228–1243, Feb. 2021.
[2] B. Ning, Z. Chen, W. Chen, and J. Fang, “Beamforming optimization for intelligent reflecting surface assisted MIMO: A sum-path-gain maximization approach,” IEEE Wirel. Commun. Lett., vol. 9, no. 7, pp. 1105–1109, Jul. 2020. [3] S. Zhang and R. Zhang, “Capacity characterization for intelligent reflecting surface aided MIMO communication,” IEEE J. Sel. Areas Commun., vol. 38, no. 8, pp. 1823–1838, Aug. 2020. [4] Z. Li, M. Hua, Q. Wang, and Q. Song, “Weighted sum-rate maximization for multi-IRS aided cooperative transmission,” IEEE Wirel. Commun. Lett., vol. 9, no. 10, pp. 1620–1624, Oct. 2020. [5] Q. Wu, S. Zhang, B. Zheng, C. You, and R. Zhang, “Intelligent reflecting surface-aided wireless communications: A tutorial,” IEEE Trans. Commun., vol. 69, no. 5, pp. 3313–3351, May 2021. [6] “NTT DOCOMO and metawave announce successful demonstration of 28Ghz-band 5G using world＇s first meta-structure technology,” https://www.businesswire.com/news/home/20181204005253/en/NTT-DOCOMO-and-Metawave-Announce-Successful-Demonstration-of-28GHz-Band-5G-Using-Worlds-First-Meta-Structure-Technology. [7] C.-W. Chen, W.-C. Tsai, L.-S. Tsai, and A.-Y. Wu, “Relay-assisted carrier aggregation (RACA) uplink system for enhancing data rate of extended reality (XR),” IEEE Trans. Veh. Technol., pp. 1–14, 2024. [8] Q. Wu and R. Zhang, “Intelligent reflecting surface enhanced wireless network via joint active and passive beamforming,” IEEE Trans. Wireless Commun., vol. 18, no. 11, pp. 5394–5409, Nov. 2019. [9] X. Yu, D. Xu, and R. Schober, “MISO wireless communication systems via intelligent reflecting surfaces,” in 2019 IEEE/CIC International Conference on Communications in China (ICCC), Aug. 2019, pp. 735–740. [10] H. Guo, Y.-C. Liang, J. Chen, and E. G. Larsson, “Weighted sum-rate maximization for intelligent reflecting surface enhanced wireless networks,” in 2019 IEEE Global Communications Conference (GLOBECOM), Dec. 2019, pp. 1–6. [11] C. Huang, A. Zappone, G. C. Alexandropoulos, M. Debbah, and C. Yuen, “Reconfigurable intelligent surfaces for energy efficiency in wireless communication,” IEEE Trans. Wirel. Commun., vol. 18, no. 8, pp. 4157–4170, Aug. 2019. [12] Z. Zhang, L. Dai, X. Chen, C. Liu, F. Yang, R. Schober, and H. V. Poor, “Active RIS vs. passive RIS: Which will prevail in 6G?” IEEE Transactions on Communications, vol. 71, no. 3, pp. 1707–1725, Mar. 2023. [13] D. Xu, X. Yu, D. W. Kwan Ng, and R. Schober, “Resource allocation for active IRS-assisted multiuser communication systems,” in 2021 55th Asilomar Conference on Signals, Systems, and Computers, Oct. 2021, pp. 113–119. [14] R. Long, Y.-C. Liang, Y. Pei, and E. G. Larsson, “Active reconfigurable intelligent surface-aided wireless communications,” IEEE Trans. Wireless Commun., vol. 20, no. 8, pp. 4962–4975, Aug. 2021. [15] M. Gapeyenko, V. Petrov, S. Paris, A. Marcano, and K. I. Pedersen, “Standardization of extended reality (XR) over 5G and 5G-advanced 3GPP new radio,” IEEE Network, vol. 37, no. 4, pp. 22–28, Jul. 2023. [16] P. Hande et al., “Extended reality over 5G—standards evolution,” IEEE J. Sel. Areas Commun., vol. 41, no. 6, pp. 1757–1771, Jun. 2023. [17] X. Lin, “An overview of 5G advanced evolution in 3GPP release 18,” IEEE Commun. Stand., vol. 6, no. 3, pp. 77–83, Sep. 2022. [18] 3GPP TR 36.814 v. 1.2.1, “Further advancements for E-UTRA: Physical layer aspects,” Tech. Spec. Group Radio Access Network, Rel. 9, Jun. 2009. [19] L. Sanguinetti, A. A. D’Amico, and Y. Rong, “A tutorial on the optimization of amplify-and-forward MIMO relay systems,” IEEE J. Sel. Areas Commun., vol. 30, no. 8, pp. 1331–1346, Sep. 2012. [20] C. Song, K.-J. Lee, and I. Lee, “Designs of MIMO amplify-and-forward wireless relaying networks: Practical challenges and solutions based on MSE decomposition,” IEEE Access, vol. 5, pp. 9223–9234, 2017. [21] Z. Yalçın and Y. Yapıcı, “Multiuser precoding for sum-rate maximization in relay-aided mmWave communications,” IEEE Trans. Veh. Technol., vol. 69, no. 6, pp. 6808–6812, Jun. 2020. [22] L. Jiang and H. Jafarkhani, “mmWave amplify-and-forward MIMO relay networks with hybrid precoding/combining design,” IEEE Trans. Wirel. Commun., vol. 19, no. 2, pp. 1333–1346, Feb. 2020. [23] G. T. de Araújo and A. L. F. de Almeida, “PARAFAC-based channel estimation for intelligent reflective surface assisted MIMO system,” in 2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM), Jun. 2020, pp. 1–5. [24] K. Ardah, S. Gherekhloo, A. L. F. de Almeida, and M. Haardt, “Double-RIS versus single-RIS aided systems: Tensor-based MIMO channel estimation and design perspectives,” in ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), May 2022, pp. 5183–5187. [25] S. S. Christensen, R. Agarwal, E. De Carvalho, and J. M. Cioffi, “Weighted sum-rate maximization using weighted MMSE for MIMO-BC beamforming design,” IEEE Trans. Wirel. Commun., vol. 7, no. 12, pp. 4792–4799, Dec. 2008. [26] Zheng, C. You, and R. Zhang, “Double-IRS assisted multi-user MIMO: Cooperative passive beamforming design,” IEEE Trans. Wirel. Commun., vol. 20, no. 7, pp. 4513–4526, Jul. 2021. [27] You, B. Zheng, and R. Zhang, “Wireless communication via double IRS: Channel estimation and passive beamforming designs,” IEEE Wirel. Commun. Lett., vol. 10, no. 2, pp. 431–435, Feb. 2021. [28] P. Wang, J. Fang, X. Yuan, Z. Chen, and H. Li, “Intelligent reflecting surface-assisted millimeter wave communications: Joint active and passive precoding design,” IEEE Trans. Veh. Technol., vol. 69, no. 12, pp. 14960–14973, Dec. 2020. [29] M. Breiling, D. W. K. Ng, C. Rohde, F. Burkhardt, and R. Schober, “Resource allocation for outdoor-to-indoor multicarrier transmission with shared UE-side distributed antenna systems,” in 2015 IEEE 81st Vehicular Technology Conference (VTC Spring), May 2015, pp. 1–7. [30] W. K. Ng, M. Breiling, C. Rohde, F. Burkhardt, and R. Schober, “Energy-efficient 5G outdoor-to-indoor communication: SUDAS over licensed and unlicensed spectrum,” IEEE Trans. Wirel. Commun., vol. 15, no. 5, pp. 3170–3186, May 2016. [31] L.-S. Tsai, S.-L. Shih, P.-K. Liao, and C.-K. Wen, “MIMO evolution toward 6G: End-user-centric collaborative MIMO,” IEEE Commun. Mag., vol. 62, no. 7, pp. 104–110, Jul. 2024. [32] C.-K. Wen, Y.-C. Chan, T.-H. Huang, H.-J. Zeng, F.-K. Wang, L.-S. Tsai, and P.-K. Liao, “End-user-centric collaborative MIMO: Performance analysis and proof of concept,” arXiv preprint arXiv:2409.15109, 2024. [33] El Gamal and Y.-H. Kim, Network information theory. Cambridge University Press, 2011. [34] W. Dinkelbach, “On nonlinear fractional programming,” Management Science, vol. 13, no. 7, pp. 492–498, Mar. 1967. [35] S. P. Boyd and L. Vandenberghe, Convex optimization. Cambridge University Press, 2004. [36] Pradhan, A. Li, L. Song, B. Vucetic, and Y. Li, “Hybrid precoding design for reconfigurable intelligent surface aided mmwave communication systems,” IEEE Wirel. Commun. Lett., vol. 9, no. 7, pp. 1041–1045, Jul. 2020. [37] C.-Z. Zhan, Y.-L. Chen, and A.-Y. Wu, “Iterative superlinear-convergence SVD beamforming algorithm and VLSI architecture for MIMO-OFDM systems,” IEEE Trans. Signal Process., vol. 60, no. 6, pp. 3264–3277, Jun. 2012. [38] Y. Zhang, X. Dong, and Z. Zhang, “Machine learning-based hybrid precoding with low-resolution analog phase shifters,” IEEE Commun. Lett., vol. 25, no. 1, pp. 186–190, Jan. 2021. [39] G. Yuan, X. Zhang, W. Wang, and Y. Yang, “Carrier aggregation for LTE-advanced mobile communication systems,” IEEE Commun. Mag., vol. 48, no. 2, pp. 88–93, Feb. 2010. [40] Z. Wei et al., “Carrier aggregation enabled integrated sensing and communication signal design and processing,” IEEE Trans. Veh. Technol., vol. 73, no. 3, pp. 3580–3596, Mar. 2024. [41] H. Liu, Z. Wei, J. Piao, H. Wu, X. Li, and Z. Feng, “Carrier aggregation enabled MIMO-OFDM integrated sensing and communication,” IEEE Trans. Wirel. Commun., pp. 1–1, 2025. [42] M. G. Kibria et al., “Carrier aggregation in satellite communications: Impact and performance study,” IEEE Open J. Commun. Soc., vol. 1, pp. 1390–1402, Sep. 2020. [43] H. Al-Hraishawi et al., “Scheduling design and performance analysis of carrier aggregation in satellite communication systems,” IEEE Trans. Veh. Technol., vol. 70, no. 8, pp. 7845–7857, Aug. 2021. [44] López–Pérez et al., “Energy efficiency of multi-carrier massive MIMO networks: Massive MIMO meets carrier aggregation,” in IEEE Global Communications Conference (GLOBECOM), Dec. 2021, pp. 01–07. [45] Zappone, D. López-Pérez, A. De Domenico, N. Piovesan, and H. Bao, “Rate, power, and energy efficiency trade-offs in massive MIMO systems with carrier aggregation,” IEEE Trans. Green Commun. Netw., vol. 7, no. 3, pp. 1342–1355, Sep. 2023. [46] Z. Shen, A. Papasakellariou, J. Montojo, D. Gerstenberger, and F. Xu, “Overview of 3GPP LTE-advanced carrier aggregation for 4G wireless communications,” IEEE Commun. Mag., vol. 50, no. 2, pp. 122–130, Feb. 2012. [47] Y. Niu, Y. Li, D. Jin, L. Su, and A. V. Vasilakos, “A survey of millimeter wave communications (mmWave) for 5G: Opportunities and challenges,” Wireless Netw., vol. 21, pp. 2657–2676, Apr. 2015. [48] Q. Wu and R. Zhang, “Intelligent reflecting surface enhanced wireless network via joint active and passive beamforming,” IEEE Trans. Wireless Commun., vol. 18, no. 11, pp. 5394–5409, Nov. 2019. [49] C.-W. Chen, W.-C. Tsai, S.-S. Wong, C.-F. Teng, and A.-Y. Wu, “WMMSE-based alternating optimization for low-complexity multi-IRS MIMO communication,” IEEE Trans. Veh. Technol., vol. 71, no. 10, pp. 11234–11239, Oct. 2022. [50] Z. Zhang, L. Lv, Q. Wu, H. Deng, and J. Chen, “Robust and secure communications in intelligent reflecting surface assisted NOMA networks,” IEEE Commun. Lett., vol. 25, no. 3, pp. 739–743, Mar. 2021. [51] G. Yang, X. Xu, and Y.-C. Liang, “Intelligent reflecting surface assisted non-orthogonal multiple access,” in 2020 IEEE Wireless Communications and Networking Conference (WCNC), May 2020, pp. 1–6. [52] Z. Lin, H. Niu, K. An, Y. Wang, G. Zheng, S. Chatzinotas, and Y. Hu, “Refracting RIS-aided hybrid satellite-terrestrial relay networks: Joint beamforming design and optimization,” IEEE Trans. Aerosp. Electron. Syst., vol. 58, no. 4, pp. 3717–3724, Aug. 2022. [53] K. An, Y. Sun, Z. Lin, Y. Zhu, W. Ni, N. Al-Dhahir, K.-K. Wong, and D. Niyato, “Exploiting multi-layer refracting RIS-assisted receiver for HAP-SWIPT networks,” IEEE Trans. Wireless Commun., pp. 1–1, 2024. [54] M. Breiling, D. Ng, C. Rohde, F. Burkhardt, R. Schober, and T. Heyn, “UE-side virtual MIMO using MM-wave for 5G,” Fraunhofer IIS, White Paper, Aug. 2014. [55] Salah, C.-H. Li, M. Al-Imari, J. Nemeth, and W. Wu, “Dynamic cross-carrier enhancement for 5G and B5G,” in 2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring), Apr. 2021, pp. 1–6. [56] P. Paymard et al., “Optimizing mixed capacity of extended reality and mobile broadband services in 5G-advanced networks,” IEEE Access, vol. 11, pp. 113324–113338, 2023. [57] 3GPP TR 38.900 V. 15.0.0, “Study on channel model for frequency spectrum above 6 GHz,” Tech. Spec. Group Radio Access Network, Rel. 15, Jul. 2018. [58] Z. Wang, Y. Cai, A. Liu, J. Wang, and G. Yue, “Two-timescale uplink channel estimation for dual-hop MIMO relay multi-user systems,” IEEE Trans. Veh. Technol., vol. 70, no. 5, pp. 4724–4739, May 2021. [59] Q. Shi, M. Razaviyayn, Z.-Q. Luo, and C. He, “An iteratively weighted MMSE approach to distributed sum-utility maximization for a MIMO interfering broadcast channel,” IEEE Trans. Signal Process., vol. 59, no. 9, pp. 4331–4340, Sep. 2011. [60] Palomar, J. Cioffi, and M. Lagunas, “Joint Tx-Rx beamforming design for multi-carrier MIMO channels: A unified framework for convex optimization,” IEEE Trans. Signal Process., vol. 51, no. 9, pp. 2381–2401, Sep. 2003. [61] Y. Rong, X. Tang, and Y. Hua, “A unified framework for optimizing linear nonregenerative multicarrier MIMO relay communication systems,” IEEE Trans. Signal Process., vol. 57, no. 12, pp. 4837–4851, Dec. 2009. [62] W. Zhang, U. Mitra, and M. Chiang, “Optimization of amplify-and-forward multicarrier two-hop transmission,” IEEE Trans. Commun., vol. 59, no. 5, pp. 1434–1445, May 2011. [63] Hammerstrom and A. Wittneben, “Power allocation schemes for amplify-and-forward MIMO-OFDM relay links,” IEEE Trans. Wirel. Commun., vol. 6, no. 8, pp. 2798–2802, Aug. 2007. [64] J. Zhang, Y. Zhang, C. Zhong, and Z. Zhang, “Robust design for intelligent reflecting surfaces assisted MISO systems,” IEEE Commun. Lett., vol. 24, no. 10, pp. 2353–2357, Oct. 2020. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/101389 | - |
| dc.description.abstract | 為了滿足第五代（Fifth Generation, 5G）無線通訊系統中的高性能要求，並突破單一裝置在硬體與能量消耗方面的限制，協同式通訊（Cooperative Communication）技術中的低成本智能反射面板（Intelligent Reflecting Surface, IRS）以及放大傳輸中繼站（Amplify-and-Forward Relay, AF Relay）近年來逐漸受到關注。由於各類協同裝置的效能與成本相異，本論文分成兩部分深入探討智能反射面板與放大傳輸中繼站在各應用場景中的適用性，並提出對應之低複雜度演算法以最佳化系統配置，提升整體傳輸效能。
首先，智能反射面透過控制反射元件的相位來達成波束成形（Beamforming）的指向性優勢。為了聯合優化反射係數與預編碼以達到頻譜效率最大化，我們提出了一種基於最小均方誤差（Minimum Mean Square Error, MMSE）的低複雜度與低延遲的交替優化（Alternating Optimization, AO）演算法。此外，我們的MMSE-AO演算法可以根據估計的子通道進行更新，以便在實際應用中使用。為了增強由於雙重衰減效應而導致的智能反射面有限覆蓋範圍，我們將MMSE-AO擴展到室內外情境中的多智能反射面系統。 另一方面，為了解決便攜裝置在天線數量與功率上的限制，同時符合延展實境（Extended reality, XR）應用的高傳輸需求，本論文提出中繼輔助載波聚合（Relay-assisted Carrier Aggregation, RACA）系統，將訊號傳輸於高頻段並利用附近的裝置作為中繼站，從而將訊號轉換至低頻段後傳輸，提高系統的覆蓋範圍與效能。為最大化延展實境裝置的上行傳輸速率，我們提出基於加權最小均方誤差（Weighted MMSE, WMMSE）與基於奇異值分解（Singular Value Decomposition, SVD）之演算法於集中式與分散式傳輸協議，相較於傳統載波聚合系統能提升32%的傳輸速率。最後，針對傳輸速率增長隨著能耗增加逐漸趨緩的問題，我們提出基於Dinkelbach轉換之加權最小均方誤差（Dinkelbach’s-transformed WMMSE, DWMMSE）框架，專注於最大化能源效率的分式規劃（Fractional Programming, FP）問題而非單純提升傳輸速率，以利輕量裝置於延展實境應用中的長時間使用。 | zh_TW |
| dc.description.abstract | To meet the high-performance requirements of Fifth Generation (5G) wireless communication systems and overcome the limitations of hardware and energy consumption in individual devices, cooperative communication technologies such as low-cost intelligent reflecting surfaces (IRS) and amplify-and-forward relays (AF relay) have gained increasing attention in recent years. Due to the varying performance and costs of different cooperative devices, this dissertation is divided into two parts to explore the applicability of IRS and AF relay in various application scenarios and propose corresponding low-complexity algorithms to optimize system configurations and enhance overall performance.
First, intelligent reflecting surfaces (IRS) achieve beamforming advantages by controlling the phase of reflecting elements to reconfigure wireless transmission environments. To jointly optimize the reflecting coefficients and precoder for maximum spectral efficiency, we propose a low-complexity and low-latency alternating optimization (AO) algorithm based on the minimum mean square error (MMSE) objective. In addition, our MMSE-AO can be updated based on estimated subchannels for practical usage. Furthermore, to enhance the limited coverage of IRS caused by the double-fading effect, we extend our MMSE-AO to multi-IRS systems in indoor and outdoor scenarios. On the other hand, to address the limitations of antenna number and power in portable devices while meeting the high transmission requirements of Extended Reality (XR) applications, we introduce a relay-assisted carrier aggregation (RACA) system, where signals are transmitted in the high-frequency band and relayed by nearby devices to convert the signals to the low-frequency band, thereby improving system coverage and performance. To maximize the uplink transmission rate of XR devices, we propose algorithms based on weighted minimum mean square error (WMMSE) and singular value decomposition (SVD) for centralized and distributed transmission protocols, which improve transmission rates by 32% compared to traditional carrier aggregation schemes. Finally, to address the issue where transmission rate growth gradually slows with increased energy consumption, we propose a Dinkelbach’s-transformed WMMSE (DWMMSE) framework, focusing on maximizing energy efficiency through fractional programming (FP) rather than simply increasing transmission rate, to support prolonged use of lightweight devices in XR applications. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2026-01-27T16:27:18Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2026-01-27T16:27:18Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | Verification Letter from the Oral Examination Committee . . . . . . . . . . . . . . . . . . . . i
Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii 摘要 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix List of Figures. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii List of Tables. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xv Notations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xvii Chapter 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.1 Challenges of 5G Technologies in Achieving 6G KPIs . . . . . . . . . . 1 1.2 Background of Cooperative Communication Systems . . . . . . . . . . 3 1.2.1 Intelligent Reflecting Surface (IRS)-assisted System . . . . . . . . . 3 1.2.2 Relay-assisted Carrier Aggregation (RACA) System . . . . . . . . . 5 1.2.3 Comparisons of Various Cooperative Devices . . . . . . . . . . . . 8 1.3 Challenges for Intelligent Reflecting Surface-assisted Systems . . . . . 10 1.3.1 Challenges of High-Complexity Configurations with Imperfect CSI 10 1.3.2 Research Contributions . . . . . . . . . . . . . . . . . . . . . . . . 11 1.3.3 Challenges of Double-Fading Effect for a Single IRS . . . . . . . . 12 1.3.4 Research Contributions . . . . . . . . . . . . . . . . . . . . . . . . 12 1.4 Challenges for Relay-assisted Carrier Aggregation Systems . . . . . . . 13 1.4.1 Challenges of Intractable Solutions for Maximizing Rates . . . . . . 13 1.4.2 Research Contributions . . . . . . . . . . . . . . . . . . . . . . . . 14 1.4.3 Challenges of Power-Constrained Devices and Imperfect CSI . . . . 15 1.4.4 Research Contributions . . . . . . . . . . . . . . . . . . . . . . . . 16 1.5 Dissertation Organization . . . . . . . . . . . . . . . . . . . . . . . . . 18 Chapter 2 Low-Complexity MMSE-based Alternating Optimization for Single-IRS MIMO Communication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 2.1 System Model and Problem Formulation . . . . . . . . . . . . . . . . . 19 2.1.1 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 2.1.2 Channel Model and Channel Estimation Methods . . . . . . . . . . 21 2.1.3 Problem Formulation . . . . . . . . . . . . . . . . . . . . . . . . . 23 2.2 Review of Related Works . . . . . . . . . . . . . . . . . . . . . . . . . 24 2.2.1 FPI for Lower-Bounded Objective [1] . . . . . . . . . . . . . . . . 24 2.2.2 ADMM for Sum-Path-Gain Maximization (SPGM) Objective [2] . . 25 2.2.3 AO for Channel Total Power (CTP) Objective [3] . . . . . . . . . . 26 2.2.4 AO for Spectral Efficiency (SE) Objective [3] . . . . . . . . . . . . 27 2.2.5 MM for Weighted Minimum Mean Square Error (WMMSE) [4] . . 28 2.2.6 Challenges of Prior Works . . . . . . . . . . . . . . . . . . . . . . 30 2.3 Proposed MMSE-AO for Single-IRS . . . . . . . . . . . . . . . . . . . 31 2.3.1 Optimization Design . . . . . . . . . . . . . . . . . . . . . . . . . 32 2.3.2 Interpretation from SE Objective for MMSE-AO . . . . . . . . . . 35 2.3.3 Analysis of Computational Complexity . . . . . . . . . . . . . . . . 36 2.3.4 Performance Evaluations . . . . . . . . . . . . . . . . . . . . . . . 37 2.4 Initialization-Enhanced MMSE-AO based on Estimated Subchannels . . 41 2.4.1 Modified MMSE-AO Update based on Estimated Subchannels . . . 41 2.4.2 Different Initializations for Enhanced Convergence . . . . . . . . . 42 2.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 Chapter 3 Extension of MMSE-AO for Indoor and Outdoor Multi-IRS MIMO Scenarios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 3.1 Scenario 1: Multi-IRS with Inter-IRS Channels . . . . . . . . . . . . . 48 3.2 Scenario 2: Multi-IRS without Inter-IRS Channels . . . . . . . . . . . 49 3.3 Performance Evaluations . . . . . . . . . . . . . . . . . . . . . . . . . 50 3.3.1 Indoor Scenario 1 with Inter-IRS Channels . . . . . . . . . . . . . . 50 3.3.2 Outdoor Scenario 2 with Inter-IRS Channels . . . . . . . . . . . . . 52 3.3.3 Resource Allocation for Reflecting Elements in double IRSs . . . . 54 3.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 Chapter 4 Relay-Assisted Carrier Aggregation (RACA) Uplink System for Enhancing Data Rate of Extended Reality (XR) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 4.1 Review of Related Works . . . . . . . . . . . . . . . . . . . . . . . . . 57 4.1.1 Carrier Aggregation (CA) Systems . . . . . . . . . . . . . . . . . . 57 4.1.2 Relay-assisted (RA) and Intelligent Reflecting Surfaces (IRS)-assisted Cooperative Systems . . . . . . . . . . . . . . . . . . . . . . . . . 58 4.1.3 Relay-Assisted Carrier Aggregation (RACA)-Based Priors . . . . . 59 4.2 System Model and Problem Formulation . . . . . . . . . . . . . . . . . 60 4.2.1 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 4.2.2 Channel Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 4.2.3 Problem Formulation . . . . . . . . . . . . . . . . . . . . . . . . . 64 4.3 Transmission Protocols . . . . . . . . . . . . . . . . . . . . . . . . . . 65 4.4 Centralized Systems and WMMSE-based Solutions . . . . . . . . . . . 67 4.4.1 WMMSE-based Problem Transformation . . . . . . . . . . . . . . 67 4.4.2 Iterative AO-based Optimization . . . . . . . . . . . . . . . . . . . 68 4.5 Distributed Systems and SVD-WF Solutions . . . . . . . . . . . . . . . 71 4.5.1 SVD Optimization of the Direct Link . . . . . . . . . . . . . . . . . 72 4.5.2 SVD Optimization of the Relay Link . . . . . . . . . . . . . . . . . 74 4.5.3 Analysis of Computational Complexity . . . . . . . . . . . . . . . . 76 4.6 Performance Evaluations . . . . . . . . . . . . . . . . . . . . . . . . . 77 4.6.1 Simulation Settings . . . . . . . . . . . . . . . . . . . . . . . . . . 77 4.6.2 Benchmarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78 4.6.3 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . 80 4.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85 Chapter 5 Robust Dinkelbach’s-transformed WMMSE Framework for Energy-Efficient RACA Systems in Power-Constrained XR Applications . . . . . . . . . . . . . . 87 5.1 System Model and Problem Formulation . . . . . . . . . . . . . . . . . 87 5.1.1 CSI Uncertainty Model . . . . . . . . . . . . . . . . . . . . . . . . 87 5.1.2 Spectral Efficiency (SE) under Imperfect CSI . . . . . . . . . . . . 88 5.1.3 Power Consumption Model . . . . . . . . . . . . . . . . . . . . . . 89 5.1.4 Problem Formulation for EE Maximization . . . . . . . . . . . . . 89 5.2 Proposed AO Algorithms for EE Maximization and MSE Minimization 90 5.2.1 Dinkelbach’s Method for Fractional Programming . . . . . . . . . . 90 5.2.2 Penalty-based WMMSE Problem Transform . . . . . . . . . . . . . 91 5.2.3 AO for EE Maximization . . . . . . . . . . . . . . . . . . . . . . . 92 5.2.4 AO for MSE Minimization . . . . . . . . . . . . . . . . . . . . . . 94 5.3 Performance Evaluations . . . . . . . . . . . . . . . . . . . . . . . . . 96 5.3.1 Simulation Setttings . . . . . . . . . . . . . . . . . . . . . . . . . . 96 5.3.2 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . 96 5.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98 Chapter 6 Conclusions and Future Works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 6.1 Design Achievements . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 6.2 Future Works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103 Appendix A — Proof of Theorems and Lemmas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113 A.1 Proof of Theorem 4.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . 113 A.2 Proof of Theorem 5.2 . . . . . . . . . . . . . . . . . . . . . . . . . . . 114 | - |
| dc.language.iso | en | - |
| dc.subject | 協同式通訊 | - |
| dc.subject | 智能反射面板 | - |
| dc.subject | 中繼輔助載波聚合 | - |
| dc.subject | 加權最小均方誤差 | - |
| dc.subject | 交替優化 | - |
| dc.subject | cooperative communication | - |
| dc.subject | intelligent reflecting surface | - |
| dc.subject | relay-asisted car rier aggregation | - |
| dc.subject | weighted minimum mean square error | - |
| dc.subject | alternating optimization | - |
| dc.title | 適用於前瞻協同式多天線通訊系統之高效最佳化演算法 | zh_TW |
| dc.title | Efficient Optimization Algorithms for Advanced Cooperative MIMO Communication Systems | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 114-1 | - |
| dc.description.degree | 博士 | - |
| dc.contributor.oralexamcommittee | 闕志達;蔡佩芸;吳仁銘;黃穎聰;謝欣霖 | zh_TW |
| dc.contributor.oralexamcommittee | Tzi-Dar Chiueh;Pei-Yun Tsai;Jen-Ming Wu;Yin-Tsung Hwang;Shin-Lin Shieh | en |
| dc.subject.keyword | 協同式通訊,智能反射面板中繼輔助載波聚合加權最小均方誤差交替優化 | zh_TW |
| dc.subject.keyword | cooperative communication,intelligent reflecting surfacerelay-asisted car rier aggregationweighted minimum mean square erroralternating optimization | en |
| dc.relation.page | 114 | - |
| dc.identifier.doi | 10.6342/NTU202504690 | - |
| dc.rights.note | 同意授權(限校園內公開) | - |
| dc.date.accepted | 2026-01-14 | - |
| dc.contributor.author-college | 電機資訊學院 | - |
| dc.contributor.author-dept | 電子工程學研究所 | - |
| dc.date.embargo-lift | 2026-01-28 | - |
| 顯示於系所單位： | 電子工程學研究所 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| ntu-114-1.pdf 授權僅限NTU校內IP使用（校園外請利用VPN校外連線服務） | 12.04 MB | Adobe PDF |
系統中的文件，除了特別指名其著作權條款之外，均受到著作權保護，並且保留所有的權利。