Channel Estimation of IRS-OTFS Communication System with Meta-learning Algorithm

ZHANG Zufan; DUAN Jiahui; WANG Guozhong

doi:10.11999/JEIT230669

Volume 46 Issue 4

Apr. 2024

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Article Navigation > Journal of Electronics & Information Technology > 2024 > 46(4): 1353-1362

ZHANG Zufan, DUAN Jiahui, WANG Guozhong. Channel Estimation of IRS-OTFS Communication System with Meta-learning Algorithm[J]. Journal of Electronics & Information Technology, 2024, 46(4): 1353-1362. doi: 10.11999/JEIT230669

Citation:

ZHANG Zufan, DUAN Jiahui, WANG Guozhong. Channel Estimation of IRS-OTFS Communication System with Meta-learning Algorithm[J]. Journal of Electronics & Information Technology, 2024, 46(4): 1353-1362. doi: 10.11999/JEIT230669

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Channel Estimation of IRS-OTFS Communication System with Meta-learning Algorithm

doi: 10.11999/JEIT230669

1.
School of Communications and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2.
Chongqing College of Electronic Engineering, Chongqing 401331, China

Funds: The National Natural Science Foundation of China (62202077), The Major Project of Science and Technology Research Program of Chongqing Education Commission of China (KJZD-M201900601)

Received Date: 2023-07-04
Rev Recd Date: 2023-12-14

Available Online: 2023-12-26

Publish Date: 2024-04-24

Abstract

Abstract

Focusing on the problem of large channel estimation transmission overhead in Intelligent Reflective Surface IRS) assisted multi-user communication system in high Doppler scenario, an IRS-OTFS communication system is constructed based on the characteristics of Orthogonal Time-Frequency Space (OTFS) modulation, which gives full play to the performance advantages of IRS and OTFS, and on this basis, a Model-Agnostic Meta-Learning (MAML) algorithm with adaptive learning rate is proposed. The IRS-OTFS multi-user channel estimation task is trained offline, the learning rate is adaptively adjusted according to the convergence speed of each task to prevent training imbalance, and the correlation between channels and the few samples and generalization characteristics of MAML algorithm are used to obtain global models and adaptive models, so as to quickly learn the transmission characteristics of new user channels, reduce transmission overhead, and improve the accuracy of channel estimation. Theoretical analysis and simulation results show that the algorithm reduces the transmission overhead by about 50% under the same channel transmission conditions, and has a performance improvement of about 4.8 dB compared with the benchmark algorithm.
- Intelligent Reflecting Surface (IRS),
- Meta-learning,
- Orthogonal Time-Frequency Space (OTFS),
- Channel estimation

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References(24)

References

[1]	HAN Yu, TANG Wankai, JIN Shi, et al. Large intelligent surface-assisted wireless communication exploiting statistical CSI[J]. IEEE Transactions on Vehicular Technology, 2019, 68(8): 8238–8242. doi: 10.1109/TVT.2019.2923997.
[2]	HUANG Chongwen, ZAPPONE A, ALEXANDROPOULOS G C, et al. Reconfigurable intelligent surfaces for energy efficiency in wireless communication[J]. IEEE Transactions on Wireless Communications, 2019, 18(8): 4157–4170. doi: 10.1109/TWC.2019.2922609.
[3]	WU Qingqing, ZHANG Shuowen, ZHENG Beixiong, et al. Intelligent reflecting surface-aided wireless communications: A tutorial[J]. IEEE Transactions on Communications, 2021, 69(5): 3313–3351. doi: 10.1109/TCOMM.2021.3051897.
[4]	NIU Hehao, LIN Zhi, AN Kang, et al. Active RIS assisted rate-splitting multiple access network: Spectral and energy efficiency tradeoff[J]. IEEE Journal on Selected Areas in Communications, 2023, 41(5): 1452–1467. doi: 10.1109/JSAC.2023.3240718.
[5]	LIN Zhi, NIU Hehao, AN Kang, et al. Refracting RIS-aided hybrid satellite-terrestrial relay networks: Joint beamforming design and optimization[J]. IEEE Transactions on Aerospace and Electronic Systems, 2022, 58(4): 3717–3724. doi: 10.1109/TAES.2022.3155711.
[6]	ASIF M, IHSAN A, KHAN W U, et al. Energy-efficient beamforming and resource optimization for STAR-IRS enabled hybrid-NOMA 6G communications[J]. IEEE Transactions on Green Communications and Networking, 2023, 7(3): 1356–1368. doi: 10.1109/TGCN.2023.3281414.
[7]	WANG Peilan, FANG Jun, YUAN Xiaojun, et al. Intelligent reflecting surface-assisted millimeter wave communications: Joint active and passive precoding design[J]. IEEE Transactions on Vehicular Technology, 2020, 69(12): 14960–14973. doi: 10.1109/TVT.2020.3031657.
[8]	CAO Yashuai, LV Tiejun, and NI Wei. Intelligent reflecting surface aided multi-user mmWave communications for coverage enhancement[C]. IEEE 31st Annual International Symposium on Personal, Indoor and Mobile Radio Communications, London, UK, 2020: 1–6. doi: 10.1109/PIMRC48278.2020.9217160.
[9]	WANG Yong, LIN Zhi, NIU Hehao, et al. Secure satellite transmission with active reconfigurable intelligent surface[J]. IEEE Communications Letters, 2022, 26(12): 3029–3033. doi: 10.1109/LCOMM.2022.3207190.
[10]	HADANI R, RAKIB S, TSATSANIS M, et al. Orthogonal time frequency space modulation[C]. IEEE Wireless Communications and Networking Conference (WCNC), San Francisco, USA, 2017: 1–6. doi: 10.1109/WCNC.2017.7925924.
[11]	HADANI R, RAKIB S, MOLISCH A F, et al. Orthogonal time frequency space (OTFS) modulation for millimeter-wave communications systems[C]. IEEE MTT-S International Microwave Symposium (IMS), Honololu, USA, 2017: 681–683. doi: 10.1109/MWSYM.2017.8058662.
[12]	THOMAS A, DEKA K, SHARMA S, et al. IRS-assisted OTFS system: Design and analysis[J]. IEEE Transactions on Vehicular Technology, 2023, 72(3): 3345–3358. doi: 10.1109/TVT.2022.3217140.
[13]	蒋占军, 刘庆达. 高速移动通信系统中OTFS信道估计算法研究[J]. 电子与信息学报, 2021, 43(10): 2878–2885. doi: 10.11999/JEIT200683. JIANG Zhanjun and LIU Qingda. Study on OTFS channel estimation algorithms in high-speed mobile communication systems[J]. Journal of Electronics & Information Technology, 2021, 43(10): 2878–2885. doi: 10.11999/JEIT200683.
[14]	RAVITEJA P, HONG Yi, VITERBO E, et al. Practical pulse-shaping waveforms for reduced-cyclic-prefix OTFS[J]. IEEE Transactions on Vehicular Technology, 2019, 68(1): 957–961. doi: 10.1109/TVT.2018.2878891.
[15]	GUNTURU A, GODALA A R, SAHOO A K, et al. Performance analysis of OTFS waveform for 5G NR mmWave communication system[C]. IEEE Wireless Communications and Networking Conference (WCNC), Nanjing, China, 2021: 1–6. doi: 10.1109/WCNC49053.2021.9417346.
[16]	WANG Zhaorui, LIU Liang, and CUI Shuguang. Channel estimation for intelligent reflecting surface assisted multiuser communications: Framework, algorithms, and analysis[J]. IEEE Transactions on Wireless Communications, 2020, 19(10): 6607–6620. doi: 10.1109/TWC.2020.3004330.
[17]	LIU Chang, LIU Xuemeng, NG D W K, et al. Deep residual learning for channel estimation in intelligent reflecting surface-assisted multi-user communications[J]. IEEE Transactions on Wireless Communications, 2022, 21(2): 898–912. doi: 10.1109/TWC.2021.3100148.
[18]	ELBIR A M and COLERI S. Federated learning for channel estimation in conventional and RIS-assisted massive MIMO[J]. IEEE Transactions on Wireless Communications, 2022, 21(6): 4255–4268. doi: 10.1109/TWC.2021.3128392.
[19]	SINGH G, SRIVASTAVA A, and BOHARA V A. Visible light and reconfigurable intelligent surfaces for beyond 5G V2X communication networks at road intersections[J]. IEEE Transactions on Vehicular Technology, 2022, 71(8): 8137–8151. doi: 10.1109/TVT.2022.3174131.
[20]	MISHRA H B, SINGH P, PRASAD A K, et al. OTFS channel estimation and data detection designs with superimposed pilots[J]. IEEE Transactions on Wireless Communications, 2022, 21(4): 2258–2274. doi: 10.1109/TWC.2021.3110659.
[21]	RAVITEJA P, PHAN K T, and HONG Yi. Embedded pilot-aided channel estimation for OTFS in delay-Doppler channels[J]. IEEE Transactions on Vehicular Technology, 2019, 68(5): 4906–4917. doi: 10.1109/TVT.2019.2906357.
[22]	BAIK S, OH J, HONG S, et al. Learning to forget for meta-learning via task-and-layer-wise attenuation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022, 44(11): 7718–7730. doi: 10.1109/TPAMI.2021.3102098.
[23]	LIU Shikun, JOHNS E, and DAVISON A J. End-to-end multi-task learning with attention[C]. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, USA, 2019: 1871–1880. doi: 10.1109/CVPR.2019.00197.
[24]	PAN Cunhua, REN Hong, WANG Kezhi, et al. Multicell MIMO communications relying on intelligent reflecting surfaces[J]. IEEE Transactions on Wireless Communications, 2020, 19(8): 5218–5233. doi: 10.1109/TWC.2020.2990766.