多輸入多輸出下基於傳送端功率最小化的使用者選擇低軌衛星研究

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姓名

張致軒(Zhi-Xuan Zhang) 查詢紙本館藏

畢業系所

通訊工程學系

論文名稱

多輸入多輸出下基於傳送端功率最小化的使用者選擇低軌衛星研究
(Research on User Selection for LEO Satellites Based on Transmit Power Minimization in MIMO Systems)

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至系統瀏覽論文 (2026-8-2以後開放)

摘要(中)

在低軌衛星通訊為現在及未來重要的通訊技術中的一部分，這項技術可以應用於海上通訊、山上通訊、偏僻地區等基地台建設無法普及的地方。近幾年來每年以超過 1000 顆以上的速度來生產低軌衛星，而每年生產低軌衛星的速度也有增長的趨勢，因此在天空中低軌衛星的密度也跟著飛速增長。但現今主要的衛星通訊技術仍以單衛星傳輸技術為主，因此本論文提出全新的資源分配模式，將衛星數視為資源的一部分。使用者選擇合適的兩顆衛星來進行 joint 傳輸，不僅讓使用者能夠實現他所期望的 data rate，同時也能夠降低衛星傳送端所需要傳送的功率，達成一個雙贏的局面。
在傳送的過程中，我們考慮較為實際的情況，讓使用者可以為不同衛星選擇合適的 MCS 來傳送。並且透過 SNR 回推的方式來計算衛星端傳送的功率，使傳送訊號的 BLER 維持在 10%，確保一定的服務品質(QoS)。並且我們透過一些技巧來降低使用者選擇衛星的時間複雜度。在最後，我們考慮衛星間功率分配的議題，設法在傳送端的功率最小化的同時，也能使使用者實現他所期望的 data rate。

摘要(英)

Low Earth Orbit (LEO) satellite communication is an integral part of current and future communication technologies. This technology can be applied in areas where traditional base station infrastructure is impractical, such as maritime and remote regions. In recent years, the production rate of LEO satellites has been exceeding 1000 satellites per year, and this trend is expected to continue. Consequently, the density of LEO satellites in the sky is rapidly increasing. However, the predominant satellite communication technology still relies on single satellite transmission.
Therefore, in this paper, we propose a novel resource allocation scheme that treats the number of satellites as part of the available resources. Users can select two suitable satellites for joint transmission, enabling them to achieve their desired data rates while reducing the transmit power required at the satellite, resulting in a win-win situation.
During the transmission process, we consider practical scenarios where users can choose appropriate modulation and coding schemes (MCS) for different satellites. We calculate the transmit power at the satellite by reverse engineering the signal-to-noise ratio (SNR) thresholds to maintain a block error rate (BLER) of 10% and ensure a certain level of quality-of-service (QoS). We also employ techniques to reduce the time complexity associated with user satellite selection. Lastly, we address the issue of power allocation among the satellites, aiming to minimize the transmit power at the transmitter while enabling users to achieve their desired data rates.

關鍵字(中)

★ 大規模MIMO
★ 低地軌道衛星
★ 協同傳輸
★ 服務質量
★ 調變和編碼方案
★ 資源分配

關鍵字(英)

★ Massive MIMO
★ LEO satellites
★ cooperative transmission
★ quality-of-service
★ modulation and coding scheme
★ resource allocation

論文目次

論文摘要 i
Abstract ii
致謝 iv
Contents v
List of Figures vi
List of Tables vi
Chapter 1. Introduction 1
1.1. Satellite Communication 1
1.2. Multiple-Input Multiple-Output LEO System 3
1.3. Organization 7
1.4. Abbreviations 7
1.5. Notation 9
Chapter 2. System Model 11
2.1. Coordinate System 11
2.2. Transmitter 13
2.3. Receiver 13
2.4. Channel Model 16
Chapter 3. Analysis Under Single LEO Satellite Transmission 19
3.1. SNR Analysis Under Non-joint Transmission 19
3.2. Switching Levels for Modulation Coding Schemes 19
3.3. Data Rate Under Non-joint Transmission 24
Chapter 4. Analysis Under Cooperation between LEO Satellites 25
4.1. SNR Analysis Under Joint Transmission 25
4.2. Data Rate Under Joint Transmission 26
4.3. Outage Probability of Data Rate 26
Chapter 5. Reduce The Time Complexity of Selecting Satellites 28
Chapter 6. Minimization of Power Allocation 32
6.1. Power Minimization Under Single-Satellite Transmission 32
6.2. Power Minimization Under Joint Transmission 33
Chapter 7. Simulation Results 36
Chapter 8. Conclusion 46
References 47
Appendix A 49

參考文獻

[1] B. Evans, O. Onireti, T. Spathopoulos and M. A. Imran, "The role of satellites in 5G," 2015 23rd European Signal Processing Conference (EUSIPCO), 2015, pp. 2756-2760.
[2] J. Farserotu and R. Prasad, "A survey of future broadband multimedia satellite systems, issues and trends," in IEEE Communications Magazine, vol. 38, no. 6, pp. 128-133, June 2000.
[3] J. Lin, Z. Hou, Y. Zhou, L. Tian and J. Shi, "Map Estimation Based on Doppler Characterization in Broadband and Mobile LEO Satellite Communications," 2016 IEEE 83rd Vehicular Technology Conference (VTC Spring), 2016, pp. 1-5.
[4] E. Telatar, ‘‘Capacity of multi-antenna Gaussian channels,’’ Eur. Trans. Telecommun., vol. 10, no. 6, pp. 585–595, Nov. 1999.
[5] S. Govindasamy and I. Bergel, ‘‘Uplink performance of multi-antenna cellular networks with co-operative base stations and user-centric clustering,’’ IEEE Trans. Wireless Commun., vol. 17, no. 4, pp. 2703–2717, Apr. 2018.
[6] V. Jungnickel, K. Manolakis, W. Zirwas, B. Panzner, V. Braun, M. Lossow, M. Sternad, R. Apelfrojd, and T. Svensson, ‘‘The role of small cells, coordinated multipoint, and massive MIMO in 5G,’’ IEEE Commun. Mag., vol. 52, no. 5, pp. 44–51, May 2014.
[7] L. Hanzo, Y. Akhtman, J. Akhtman, L. Wang, and M. Jiang, MIMOOFDM for LTE, WiFi and WiMAX: Coherent Versus Non-Coherent and Cooperative Turbo Transceivers. Hoboken, NJ, USA: Wiley, 2011.
[8] P.-D. Arapoglou, K. Liolis, M. Bertinelli, A. Panagopoulos, P. Cottis, and R. De Gaudenzi, ‘‘MIMO over satellite: A review,’’ IEEE Commun. Surveys Tuts., vol. 13, no. 1, pp. 27–51, 1st Quart., 2011.
[9] P.-D. Arapoglou, P. Burzigotti, M. Bertinelli, A. B. Alamanac, and R. De Gaudenzi, ‘‘To MIMO or not to MIMO in mobile satellite broadcasting systems,’’ IEEE Trans. Wireless Commun., vol. 10, no. 9, pp. 2807–2811, Sep. 2011.
[10] R. T. Schwarz, T. Delamotte, K.-U. Storek, and A. Knopp, ‘‘MIMO applications for multibeam satellites,’’ IEEE Trans. Broadcast., vol. 65, no. 4, pp. 664–681, Dec. 2019.
[11] C. Hofmann, K.-U. Storek, R. T. Schwarz, and A. Knopp, ‘‘Spatial MIMO over satellite: A proof of concept,’’ in Proc. IEEE Int. Conf. commun. (ICC), May 2016, pp. 1–6.
[12] R. Richter, I. Bergel, Y. Noam, and E. Zehavi, “Downlink Cooperative MIMO in LEO Satellites,” IEEE Access, vol.8, pp.213 866-213 881, Jun 2020.
[13] Shkelzen Cakaj, “ The Parameters Comparison of the Starlink LEO Satellites Constellation for Different Orbital Shells, ” Front. Comms. Net., 07 May 2021.
[14] W. Wang, Y. Tong, L. Li, A.-A. Lu, L. You, and X. Gao, ‘‘Near optimal timing and frequency offset estimation for 5G integrated LEO satellite communication system,’’ IEEE Access, vol. 7, pp. 113298–113310, 2019.
[15] T. Richardson, M. Shokrollahi, and R. Urbanke, “Design of capacityapproaching irregular low-density parity-check codes,” Information Theory, IEEE Transactions on, vol. 47, no. 2, pp. 619 –637, Feb 2001.
[16] Chu, Eunmi; Yoon, Janghyuk; Jung, Bang C. 2019. "A Novel Link-to-System Mapping Technique Based on Machine Learning for 5G/IoT Wireless Networks" Sensors 19, no. 5: 1196.
[17] 3rd Generation Partnership Project; Technical Specification Group Radio Access Network; NR; Multiplexing and channel coding (Release 16)
[18] S. ten Brink, G. Kramer and A. Ashikhmin, "Design of low-density parity-check codes for modulation and detection," in IEEE Transactions on Communications, vol. 52, no. 4, pp. 670-678, April 2004.
[19] I. Tal and A. Vardy, "How to Construct Polar Codes," in IEEE Transactions on Information Theory, vol. 59, no. 10, pp. 6562-6582, Oct. 2013.
[20] John G. Proakis; Masoud Salehi, “Digital Communications Fifth Edition,” November 2007.
[21] L. You, K.-X. Li, J. Wang, X. Gao, X.-G. Xia, and B. Ottersten, “Massive MIMO transmission for LEO satellite communications,” IEEE J. Sel. Areas Commun., vol. 38, no. 8, pp. 1851–1865, Aug. 2020.
[22] Propagation Delay and Doppler in Non-Terrestrial Networks, document RP-171578, 3GPP TSG RAN1, 2017.
[23] Work Item Description on Study on NR to Support Non-Terrestrial Networks, document RP-171450, 3GPP TSG RAN1, Jun. 2017.
[24] Study on New Radio (NR) to Support Non-Terrestrial Networks (Release 17), document TR 38.811 f10, 3GPP, 2019.
[25] Study on Channel Model for Frequencies From 0.5 to 100 GHz (Release 17), document TR 38.901 g10, 3GPP, 2020.

指導教授

陳永芳(Yung-Fang Chen)

審核日期

2023-8-11

推文