 |
|
|
|
以作者查詢圖書館館藏 、以作者查詢臺灣博碩士 、以作者查詢全國書目 、勘誤回報 、線上人數：51 、訪客IP：13.59.70.123
姓名 宋晏州(Yan-Zhou Soong) 查詢紙本館藏 畢業系所 通訊工程學系 論文名稱 基於自編碼器的低軌衛星通訊之 波束空間通道估計
(Autoencoder-based Beamspace Channel Estimation for LEO Satellite Communications)檔案 [Endnote RIS 格式]
[相關文章]
[文章引用]
[完整記錄]
[館藏目錄]
至系統瀏覽論文 ( 永不開放)
摘要(中) 低軌道衛星（LEO）通訊在6G 技術的發展中發揮著至關重要的角色，實現廣域覆
蓋和無處不在的無線服務。LEO 衛星運行在地球表面上空500 公里至2000 公里的高度
範圍內。最近，毫米波通信在LEO 衛星通訊系統中獲得了極大的關注，提供了更高的
數據傳輸速率潛力。然而，由於更高的工作頻率和更長的傳輸距離導致通道路徑損耗增
加，最高可達到180 dB。毫米波技術的應用在LEO 衛星通訊中帶來了挑戰。
為了應對這一挑戰，一種高效的解決方案是採用使用多輸入多輸出（MIMO）技術
的混合波束成形，該技術利用大量的小型天線元件且具有較少射頻（RF）鏈的架構。
MIMO 波束成形的成功實施依賴於準確的通道狀態訊息（CSI）。在LEO 衛星通訊系統
中，由於衛星的快速移動大約為7.5km 每公里，這導致通道快速的變化，準確的通道估
計和CSI 追蹤至關重要。然而，隨著天線數量的增加，獲得完整的CSI 使計算複雜度大
大提升。
與地面通訊不同，LEO 衛星通訊主要涉及衛星和地面站之間的直視路徑（LOS）通
道，因此到達角（AOA）和離去角（AOD）的多樣性較少。因此，當使用巨量天線MIMO
技術時，LEO 衛星通訊通道會呈現稀疏特性。在本論文中，我們的目標是利用這種特有
的稀疏通道特性，提出低複雜度且高效的發送端預編碼和接收端壓縮感知技術的設計，
以解決在稀疏通道下通道估計的問題。主要的設計挑戰可以總結如下：
(1). 巨量天線 MIMO 是LEO 衛星通訊中克服長距離和大維度通道路徑損耗的關鍵
技術。然而隨著天線元件數量的增加，通道估計的計算複雜度也增加。因此在
降低估計複雜度的同時保持估計性能的高效發送端和接收端的設計至關重要。
(2). LEO 衛星通訊通常部署在廣闊無障礙區域，距離地面500 公里至2000 公里左右。因此多徑傳播路徑的數量會比傳統通訊系統還要少，通道變得以直視路徑
為主或只有少數傳播路徑。這導致整個衛星到地面通訊的通道具有高度稀疏的
特性。因此重要的是研究能夠利用這稀疏特性以減少計算複雜度的高效通道估
計方法。
(3). 在研究的MIMO 系統架構中，由於通道的稀疏性，只有在衛星到地面通訊通道
的特定AOA 和AOD 下需要估計的無線通道。這就提出了在發送端設計預編
碼和在接收端設計壓縮感知等訊號處理技術以有效估計這些通道的問題。
(4). 對於只包含少數非零數值的稀疏通道，需要稀疏通道重建演算法來提高準確性
和穩定性。借助本篇所提出的預編碼波形和壓縮感知架構，設計一個計算複雜
度低的稀疏通道重建演算法非常重要，該算法可以從接收端的壓縮接收訊號中
提取稀疏通道成分。摘要(英) Low Earth Orbit (LEO) satellite communication plays a crucial role in the development of
6G technology, enabling wide-area coverage and ubiquitous wireless services. LEO satellites
operate within a height range of 500 to 2000 kilometers above the Earth′s surface. Recently,
millimeter-wave communication has gained significant attention in LEO satellite
communication systems, offering higher data transmission rate potential. However, the use of
mmWave in LEO satellite communication faces the challenge of increased channel path loss
due to higher operating frequencies and longer transmission distances, with losses reaching up
to 180 dB.
To address this challenge, an efficient solution is to adopt hybrid beamforming using
Multiple-Input Multiple-Output (MIMO) technology, which utilizes a large number of small
antenna elements with fewer Radio Frequency (RF) chains. The successful implementation of
MIMO beamforming relies on accurate Channel State Information (CSI). In LEO satellite
communication systems, the fast movement of satellites leads to rapid channel variations,
making accurate channel estimation and CSI tracking crucial. However, with an increasing
number of antennas, obtaining complete CSI becomes computationally intensive.
Unlike ground communication, LEO satellite communication primarily involves Line-of-
Sight (LOS) channels between satellites and ground stations, resulting in fewer variations in iv
the angles of arrival (AOA) and departure (AOD). Therefore, when employing a large number
of antennas for MIMO technology, the LEO satellite communication channel exhibits sparse
characteristics. In this thesis, our goal is to utilize this inherent sparse channel property and
design low-complexity yet efficient transmitter precoding and receiver compressive sensing
techniques to address the channel estimation problem. The main design challenges can be
summarized as follows:
(1). Massive MIMO is a key technology in LEO satellite communication for overcoming
long-distance and large channel path losses. However, as the number of antenna
elements increases, the computational complexity of channel estimation also
increases. Therefore, it is crucial to develop efficient transmitter and receiver designs
that reduce estimation complexity while maintaining estimation performance.
(2). LEO satellite communication is typically deployed in wide-open areas ranging from
500 to 2000 kilometers above the ground. Consequently, the number of multipath
propagation paths significantly decreases, and the channel becomes dominated by
LOS or only a few propagation paths. This results in highly sparse characteristics of
the channel along the entire satellite-ground communication link. Hence, it is
important to investigate efficient channel estimation methods that leverage this sparse
property to reduce computational complexity.
(3). In the studied MIMO system architecture, due to the sparsity of the channel, only a
few wireless channel components need to be estimated for specific AOA and AOD in
the satellite-to-ground communication channel. This poses the challenge of designing default waveforms at the transmitter and signal processing techniques such as
compressive sensing at the receiver to effectively estimate these channels.
(4). For sparse channels that contain only a few non-zero channel coefficients, sparse
channel reconstruction algorithms are needed to improve accuracy and stability. With
the proposed pilot waveforms and compressive sensing framework, designing
v
computationally affordable sparse channel reconstruction algorithms becomes crucial,
which can extract sparse channel components from the compressed received signal at
the receiver.關鍵字(中) ★ 低地球軌道（LEO）衛星通訊
★ 壓縮感知（Compressive Sensing，CS）
★ 多 輸入多輸出（MIMO）技術
★ 毫米波通訊
★ 混合波束成形（Hybrid Beamforming）
★ 通道估計關鍵字(英) ★ Low earth orbit (LEO) satellite communication
★ Compressive sensing (CS)
★ Multi-input multi-output (MIMO) technology
★ Millimeter wave communication
★ Hybrid beamforming
★ Channel estimation論文目次 摘要 ....................................................i
Abstract ..............................................iii
Acknowledgmentsvi ......................................vi
Contents ..............................................vii
List of Figures ........................................ix
List of Tables.......................................... x
List of Symbols ........................................xi
Chapter 1 Introduction.................................. 1
1-1 Overview and Motivations ............................1
1-2 Literature Survey ...................................3
1.2.1 Satellite communication system using millimeter wave MIMO technology .........................................3
1.2.2 Sparse Channel Estimation .........................5
Chapter 2 Technical Theory ..............................7
2-1 Compressed Sensing (CS) .............................7
2-2 Machine Learning (ML) ..............................10
2-2-1 Autoencoder (AE) .................................11
Chapter 3 Hybrid Beamforming for LEO Satellite Communication...........................................12
3-1 Physical Channel Model..............................12
3-2 Beamspace Channel Model ............................16
3-3 Compressive Sensing Based Hybrid Beamforming Architecture............................................18
Chapter 4 Autoencoder assists in designing sensing matrix and approximation methods...............................22
4-1 Autoencoder Assists in Designing Sensing Matrix ....22
4-2 Low Rank Approximation for Hybrid Beamforming Architecture ...........................................29
4-3 Sparse Channel Estimation ..........................31
4-3-1 Training a Sensing Matrix ........................31
4-3-2 Sparse Channel Reconstruction ....................33
Chapter 5 Simulation Results and Discussions ...........37
5-1 Parameter Setting ..................................37
5-2 Simulation Results .................................39
5.2.1 Effect of Autoencoder Assisted Design Sensing Matrix Performance ............................................39
5.2.2 Effect of Hybrid Beamforming System in Low rank approximation Performance ..............................41
5.2.2 Effect of Physical Time on the Performance of Low Rank Approximation...........................................48
Chapter 6 Conclusion ...................................52
Reference ..............................................53參考文獻 [1] Z. Xiao et al., "LEO Satellite Access Network (LEO-SAN) towards 6G: Challenges and
Approaches," IEEE Wireless Commun., doi: 10.1109/MWC.011.2200310.
[2] L. You, K.-X. Li, J. Wang, X. Gao, X.-G. Xia, and B. Otterstenx, “LEO satellite
communications with massive MIMO,” in Proc. IEEE Int. Conf. Commun., Dublin,
Ireland, 2020, pp. 1–6.
[3] X. Li, J. Fang, H. Li, and P. Wang, “Millimeter wave channel estimation via exploiting
joint sparse and low-rank structures,” IEEE Trans. Wireless Commun., vol. 17, no. 2,
pp. 1123–1133, 2017.
[4] Z. Xiao, P. Xia, and X.-G. Xia, “Channel estimation and hybrid precoding for
millimeter-wave mimo systems: A low-complexity overall solution,” IEEE Access, vol.
5, pp. 16 100–16 110, 2017.
[5] X. Li, J. Fang, H. Duan, Z. Chen, and H. Li, “Fast beam alignment for millimeter wave
communications: A sparse encoding and phaseless decoding approach,” IEEE Trans.
Signal Process., vol. 67, no. 17, pp. 4402–4417, Sep. 2019.
[6] R. Feng, C. X. Wang, J. Huang and X. Gao, “Recent advances of ultramassive multipleinput,
multiple-output technologies: realizing a sixth-generation future in spatial and
beam domains,” IEEE Veh. Technol. Mag., vol. 18, no. 1, pp. 70-79, Mar. 2023.
[7] Y. Liu, C. Li, J. Li, and L. Feng, “Robust Energy-Efficient Hybrid Beamforming Design
for Massive MIMO LEO Satellite Communication Systems,” IEEE Access, vol. 10, pp.
63 085–63 099, 2022.
[8] G. Zang, Y. Cui, H. V. Cheng, F. Yang, L. Ding, and H. Liu, “Optimal hybrid
beamforming for multiuser massive MIMO systems with individual SINR constraints,”
IEEE Wireless Commun. Lett., vol. 8, no. 2, pp. 532–535, Apr. 2019.
[9] A. F. Molisch, V. V. Ratnam, S. Han, Z. Li, S. L. H. Nguyen, L. Li, and K. Haneda,“Hybrid beamforming for massive MIMO: A survey,” IEEE Commun. Mag., vol. 55,
no. 9, pp.134–141, 2017.
[10] Z. Li, S. Han, S. Sangodoyin, R. Wang, and A. F. Molisch, “Joint optimization of hybrid
beamforming for multi-user massive MIMO downlink,” IEEE Trans. Wireless
Commun., vol. 17, no. 6, pp. 3600–3614, Jun. 2018.
[11] 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.
[12] Y. Su, Y. Liu, Y. Zhou, J. Yuan, H. Cao, and J. Shi, “Broadband LEO Satellite
Communications: Architectures and Key Technologies,” IEEE Wirel. Commun., vol. 26,
no. 2, pp. 55–61, 2019.
[13] Z. Lin, M. Lin, B. Champagne, W.-P. Zhu, and N. Al-Dhahir, “Robust hybrid
beamforming for satellite-terrestrial integrated networks,” in Proc. IEEE Int. Conf.
Acoust., Speech Signal Process. (ICASSP), May 2020, pp. 1–5.
[14] D. Peng, A. Bandi, Y. Li, S. Chatzinotas, and B. Ottersten, “Hybrid beamforming, user
scheduling, and resource allocation for integrated terrestrial-satellite communication,”
IEEE Trans. Veh. Technol., vol. 70, no. 9, pp. 8868–8882, Sep. 2021.
[15] R. Mohammadian, A. Amini, and B. H. Khalaj, “Compressive sensing-based pilot
design for sparse channel estimation in OFDM systems,” IEEE Commun. Lett., vol. 21,
no. 1, pp. 4–7, Jan. 2017.
[16] P. Cheng, Z. Chen, Y. Rui, Y. Jay Guo, L. Gui, M. Tao, and Q. T. Zhang, “Channel
estimation for OFDM systems over doubly selective channels: a distributed compressive
sensing based approach,” IEEE Trans. Commun., vol. 61, no. 10, pp. 4173–4185, Oct.
2013.
[17] Y. Nan, L. Zhang, and X. Sun, “Efficient downlink channel estimation scheme based onblock-structured compressive sensing for TDD massive MU-MIMO systems,” IEEE
Wireless Commun. Lett., vol. 4, no. 4, 1067 pp. 345–348, Aug. 2015.
[18] H. He, C.-K. Wen, S. Jin, and G. Y. Li, “Deep learning-based channel estimation for
beamspace mmWave massive MIMO systems,” IEEE Wireless Commun. Lett., vol. 7,
no. 5, pp. 852–855, Oct. 2018.
[19] C.-K. Wen, W.-T. Shih, and S. Jin, “Deep learning for massive MIMO CSI feedback,”
IEEE Wireless Commun. Lett., vol. 7, no. 5, pp. 748–751, Oct. 2018.
[20] T. Kim and D. J. Love, “Virtual AoA and AoD estimation for sparse millimeter wave
MIMO channels,” in Proc. IEEE 16th Int. Workshop Signal Process. Adv. Wireless
Commun., 2015, pp. 146–150.
[21] A. M. Sayeed, “Deconstructing multiantenna fading channels,” IEEE Transactions on
Signal Processing, vol. 50, no. 10, pp. 2563–2579, 2002.
[22] J. Mo, P. Schniter, and R. W. Heath, ‘‘Channel estimation in broadband millimeter wave
MIMO systems with few-bit ADCs,’’ IEEE Trans. Signal Process., vol. 66, no. 5, pp.
1141–1154, Mar. 2018.
[23] M. Zhang, H. Zhang, D. Yuan, and M. Zhang, “Learning-based sparse data
reconstruction for compressed data aggregation in IoT networks,” IEEE Internet Things
J., vol. 8, no. 14, pp. 11732–11742, Jul. 2021.
[24] J. Mo, P. Schniter, N. G. Prelcic, and R. W. Heath, ‘‘Channel estimation in millimeter
wave MIMO systems with one-bit quantization,’’ in Proc. 48th Asilomar Conf. Signals,
Syst. Comput., Pacific Grove, CA, USA, Nov. 2014, pp. 957–961.
[25] S. Montagner, N. Benvenuto, and P. Baracca, “Channel estimation using a 2D DFT for
millimeter-wave systems,” in Proc. IEEE 81st Veh. Technol. Conf. (VTC Spring), May
2015, pp. 1–5.
[26] M. Soleimani, R. C. Elliott, W. A. Krzymien, J. Melzer, and P. Mousavi, “Hybrid beamforming and DFT-based channel estimation for millimeter wave MIMO systems,”
in Proc. IEEE 28th Annu. Int. Symp. Pers., Indoor, Mobile Radio Commun. (PIMRC),
Oct. 2017, pp. 1–7.
[27] N. J. Myers and R. W. Heath, “Message passing-based joint CFO and channel estimation
in mmWave systems with one-bit ADCs,” IEEE Trans. Wireless Commun., vol. 18, no.
6, pp. 3064–3077, Jun. 2019.
[28] K. Usman. (2017) Introduction to orthogonal matching pursuit. Telkom University.
[Online]. Available: http://korediantousman.staff.telkomuniversity.ac.id
[29] S. G. Mallat and Z. Zhang, “Matching pursuits with time-frequency dictionaries,” IEEE
Trans. Signal Process., vol. 41, no. 12, pp. 3397–3415, Dec. 1993.
[30] J. Tropp, “Just relax: Convex programming methods for identifying sparse signals in
noise,” IEEE Trans. Inf. Theory, vol. 52, no. 3, pp. 1030–1051, Mar. 2006.
[31] Michael Grant and Stephen Boyd. CVX: Matlab software for disciplined convex
programming, version 2.0 beta. http://cvxr.com/cvx, September 2013.指導教授 古孟霖 楊明勳(Meng-Lin Ku Ming-Hsun Yang) 審核日期 2023-12-25 推文 plurk
funp
live
udn
HD
myshare
netvibes
friend
youpush
delicious
baidu
網路書籤 Google bookmarks
del.icio.us
hemidemi
facebook
twitter
google
reddit