多用戶多輸入多輸出之上行正交分頻多工低軌衛星系統之子載波配置

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姓名

王偉綸(Wei-Lun Wang) 查詢紙本館藏

畢業系所

通訊工程學系

論文名稱

多用戶多輸入多輸出之上行正交分頻多工低軌衛星系統之子載波配置
(Subcarrier Allocation Schemes for Uplink Massive MIMO-OFDM LEO Satellite Systems)

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至系統瀏覽論文 (2030-1-16以後開放)

摘要(中)

隨著射頻元件及天線技術的突破，基於Massive MIMO和OFDM的低軌道衛星（LEO）通訊在B5G大頻寬時代中日益普及。這項技術因能夠支持高資料傳輸率並顯著降低硬體成本，而受到廣泛關注，被視為未來網絡中極具潛力的技術之一。本論文針對多用戶（MU）Massive MIMO OFDM LEO衛星通訊系統，目標是最大化上行鏈路的總系統傳輸速率，同時在滿足每個用戶的服務質量（QoS）要求，如位元錯誤率和資料傳輸速率的情況下進行優化。
在子載波配置方面，我們採用拉格朗日優化方法來有效地分配子載波，以提升頻譜效率。此方法考量了各子載波的頻譜利用率及功率分配，確保在總功率限制下達到最佳配置效果。另一方面，針對用戶選擇，我們利用每個使用者在各子通道上的通道矩陣進行奇異值分解（SVD），從眾多候選用戶中優先選取通道條件較佳的用戶進行訊息傳輸。這樣的用戶選擇策略能夠有效提升系統的整體性能和頻譜利用率。
在傳輸過程中，不同頻率上的信號可能會受到不同程度的衰減、多路徑干擾及失真，導致通道的頻率響應有所差異，因此正交分頻多工（OFDM）被廣泛應用以對抗頻率選擇性衰落。此外，拉格朗日方法不僅應用於子載波配置，還用於功率分配，確保在多用戶環境下各用戶的傳輸需求得到滿足。
綜上所述，我們評估了所提出的子載波配置與用戶選擇方案在不同參數下的性能表現，並將其與其他基準方案進行比較。模擬結果顯示，所提出的子載波配置及基於SVD的用戶選擇方案在提升系統總傳輸速率及頻譜效率方面表現優異，並且使用迭代法來調整原資料流配置的結果，可增加其總頻譜效率。

摘要(英)

With advancements in radio frequency (RF) components and antenna technologies, Low Earth Orbit (LEO) satellite communications based on Massive Multiple-Input Multiple-Output (Massive MIMO) and Orthogonal Frequency Division Multiplexing (OFDM) are becoming increasingly prevalent in the B5G era characterized by vast bandwidths. This technology has garnered significant attention due to its ability to support high data transmission rates while substantially reducing hardware costs, positioning it as one of the most promising technologies for future networks. This paper focuses on designing subcarrier allocation and user selection schemes for multi-user (MU) Massive MIMO OFDM LEO satellite communication systems with the objective of maximizing the total uplink system transmission rate. The optimization is performed while ensuring that each user’s Quality of Service (QoS) requirements, such as bit error rate and data transmission rate, are met.
For subcarrier allocation, we employ a Lagrangian optimization method to efficiently distribute subcarriers, thereby enhancing spectral efficiency. This approach considers the spectral utilization and power allocation of each subcarrier to achieve optimal configuration within the total power constraints. In terms of user selection, we utilize Singular Value Decomposition (SVD) on each user′s channel matrix across subchannels, prioritizing users with superior channel conditions from a pool of candidates for data transmission. This user selection strategy effectively improves the overall system performance and spectral utilization.
During the transmission process, signals at different frequencies may experience varying degrees of attenuation, multipath interference, and distortion, leading to differences in channel frequency responses. To counteract frequency-selective fading, Orthogonal Frequency Division Multiplexing (OFDM) is widely applied. Additionally, the Lagrangian method is not only used for subcarrier allocation but also for power distribution, ensuring that the transmission needs of all users are satisfied in a multi-user environment.
In summary, we evaluate the performance of the proposed subcarrier allocation and user selection schemes under various parameters and compare them with other benchmark schemes. Simulation results demonstrate that the proposed subcarrier allocation and SVD-based user selection schemes achieve highly competitive performance, surpassing traditional fully digital designs in terms of total transmission rate and spectral efficiency. Furthermore, iterative adjustments to the original data stream configurations enhance the overall spectral efficiency.

關鍵字(中)

★ 子載波配置

關鍵字(英)

★ subcarrier allocation

論文目次

論文摘要 iv
Abstract vi
致謝 viii
Contents ix
List of Figures x
List of Tables xi
Chapter 1. Introduction 1
1.1. LEO Satellite Communicate Systems 1
1.2. Massive MIMO System 3
1.3. OFDM 6
1.4. Resource allocation algorithm 8
1.5. Contributions 10
1.6. Organization 12
1.7. Abbreviations 12
1.8. Notations 14
Chapter 2. System Model 15
2.1. Signal model 15
2.2. Channel Model 17
2.3. Hybrid Beamforming precoding and combining Algorithm 22
2.4. Problem Formulation 28
Chapter 3. Proposed Resource Allocation 29
3.1. User Selection Scheme 29
3.2. Subcarrier allocation and Power allocation 32
Chapter 4. Simulation Results 39
Chapter 5. Conclusion 44
References 44

參考文獻

[1] M. Handley, "Delay is Not an Option," presented at the Proceedings of the 17th ACM Workshop on Hot Topics in Networks, 2018.
[2] J. Chu and X. Chen, ‘‘Robust design for integrated satellite–terrestrial Internet of Things,’’ IEEE Internet Things J., vol. 8, no. 11, pp. 9072–9083, Jun. 2021.
[3] N. Gupta and S. Bitragunta, ‘‘Green satellite communication link design, optimization, and performance analysis,’’ in Proc. IEEE 7th Uttar Pradesh Sect. Int. Conf. Electr., Electron. Comput. Eng. (UPCON), Nov. 2020, pp. 1–5.
[4] C. Y. Wong, R. S. Cheng, K. B. Letaief, and R. D. Murch, “Multicarrier OFDM with adaptive subcarrier, bit, and power allocation,” IEEE J. Select. Areas Commun., vol. 17, no. 10, pp. 1747-1758, Oct. 1999.
[5] Chen, Y.-F., Chen, J.-W.: ‘A fast subcarrier, bit, and power allocation algorithm for multiuser OFDM-based systems’, IEEE Trans. Veh. Tech., 2008, 57, (2), pp. 873–881
[6] W. Rhee and J. M. Cioffi, “Increasing in capacity of multiuser OFDM system using dynamic subchannel allocation,” in Proc. IEEE Veh. Technol. Conf., Tokyo, Japan, vol. 2, May 2000, pp. 1085-1089
[7] J. Jang and K. B. Lee, “Transmit power adaptation for multiuser OFDM systems,” IEEE J. Select. Areas Commun., vol. 21, no. 2, pp. 171-178, Feb. 2003.
[8] W.-C. Pao and Y.-F. Chen, “Reduced complexity subcarrier allocation schemes for DFT-precoded OFDMA uplink systems,” IEEE Trans. Wireless Commun., vol. 9, no. 9, pp. 2701–2706, Sep. 2010.
[9] J. Jiang and D. Kong, “Joint User Scheduling and MU-MIMO Hybrid Beamforming Algorithm for mmWave FDMA Massive MIMO System”, International Journal of Antennas and Propagation, Article ID 4341068, 2016.
[10] T. E. Bogale, L. B. Le, and A. Haghighat, “User scheduling for massive MIMO OFDMA systems with hybrid analog-digital beamforming,” in Proc. IEEE Int. Conf. Commun., London, U.K., 2015, pp. 1757–1762.
[11] I. Ahmed, H. Khammari, and A. Shahid, “Resource allocation for transmit hybrid beamforming in decoupled millimeter wave multiuserMIMO downlink,” IEEE Access, vol. 5, pp. 170–182, 2017.
[12] V.N. Ha, D.H.N. Nguyen and J.-F. Frigon, "Subchannel allocation and hybrid precoding in mmWave ofdma systems", IEEE Trans. Wireless Commun., 2017.
[13] M. Benmimoune, E. Driouch, W. Ajib and D. Massicotte, "Joint transmit antenna selection and user scheduling for massive MIMO systems", Proc. IEEE Wireless Commun. Netw. Conf., pp. 381-386, Mar. 2015.
[14] A. Forenza, D. Love and R. W. Heath, "Simplified spatial correlation models for clustered MIMO channels with different array configurations", IEEE Trans. Veh. Technol., vol. 56, no. 4, pp. 1924-1934, 2007.
[15] K.-X. Li, J. Wang, X. Gao, X.-G. Xia, and B. Ottersten, "Massive MIMO Transmission for LEO Satellite Communications," IEEE Journal on Selected Areas in Communications, vol. 38, no. 8, pp. 1851-1865, 2020, doi: 10.1109/jsac.2020.3000803.
[16] E. T. Michailidis, P. Theofilakos, and A. G. Kanatas, "Three-Dimensional Modeling and Simulation of MIMO Mobile-to-Mobile via Stratospheric Relay Fading Channels," IEEE Transactions on Vehicular Technology, vol. 62, no. 5, pp. 2014-2030, 2013, doi: 10.1109/tvt.2012.2236691.
[17] F. Sohrabi and W. Yu, “Hybrid analog and digital beamforming for mmWave OFDM large-scale antenna arrays,” IEEE J. Sel. Areas Commun., vol. 35, no. 7, pp. 1432–1443, Jul. 2017
[18] Z. Pi, "Optimal transmitter beamforming with per-antenna power constraints", IEEE Int. Conf. Commun. (ICC), pp. 3779-3784, 2012.
[19] C. Kim, T. Kim, and J. Y. Seol, "Multi-beam transmission diversity with hybrid beamforming for MIMO-OFDM systems," in 2013 IEEE Globecom Workshops (GC Wkshps), 9-13 Dec. 2013 2013, pp. 61-65, doi: 10.1109/GLOCOMW.2013.6824962.
[20] K.-X. Li, X. Gao, and X.-G. Xia, "Channel Estimation for LEO Satellite Massive MIMO OFDM Communications," IEEE Transactions on Wireless Communications, vol. 22, no. 11, pp. 7537-7550, 2023, doi: 10.1109/twc.2023.3252895.
[21] C. Balanis, Antenna Theory, Wiley, 1997.
[22] Z. Pi, "Optimal transmitter beamforming with per-antenna power constraints", IEEE Int. Conf. Commun. (ICC), pp. 3779-3784, 2012.
[23] L. Zhou and Y. Ohashi, “Hybrid precoding with data stream adaptation for high throughput mmWave MIMO systems,” in proc. IEEE Wireless Commun. and Networking Conf., Doha, 2016, pp. 1-6.
[24] E. G. Larsson, O. Edfors, F. Tufvesson, and T. L. Marzetta, "Massive MIMO for next generation wireless systems," IEEE Communications Magazine, vol. 52, no. 2, pp. 186-195, 2014, doi: 10.1109/MCOM.2014.6736761.
[25] Z. Liu, J. Guo, D. Zhang, Y. Su, and T. Sato, "An Accurate Channel Prediction Method for Massive MIMO-Based LEO Communications," presented at the 2022 International Wireless Communications and Mobile Computing (IWCMC), 2022.
[26] Y.-F. C. a. L.-L. Lo, "<Subcarrier Allocation Scheme for Downlink Multiuser MIMO-OFDM mmWave Systems.pdf>," 2020.
[27] Z. Xiang, X. Gao, K.-X. Li, and X.-G. Xia, "Massive MIMO Downlink Transmission for Multiple LEO Satellite Communication," IEEE Transactions on Communications, vol. 72, no. 6, pp. 3352-3364, 2024, doi: 10.1109/tcomm.2024.3367726.
[28] I. Leyva-Mayorga et al., "LEO Small-Satellite Constellations for 5G and Beyond-5G Communications," IEEE Access, vol. 8, pp. 184955-184964, 2020, doi: 10.1109/ACCESS.2020.3029620.

指導教授

陳永芳(Yung-Fang Chen)

審核日期

2025-1-20

推文