ResNet使能的OTFS联合信道估计和信号检测

doi:10.19665/j.issn1001-2400.2023.03.002

Abstract

Abstract:

Orthogonal time frequency space (OTFS) modulation can realize reliable broadband communication at a high doppler frequency offset,which is one of the potential application technologies in the 6G communication-sensing-computing scenario.In order to solve the problems of high complexity and limited performance of the receiver in this system,a joint channel estimation and signal detection algorithm based on modified ResNet is proposed,with the transmission symbol information recovered directly without obtaining explicit channel information.According to the stability of the delay doppler domain channel,deep learning technology is introduced into the receiver design,and a lightweight residual neural network model that can fully extract the signal features is designed by using the embedded pilot data frame structure.It can directly fit the input-output relationship of delay doppler domain signals to achieve implicit channel estimation and complete signal detection.In the joint design,the optimal network model is obtained by off-line training with the data collected in the actual communication link,which can be used for on-line detection.Meanwhile,the joint optimization of channel estimation and signal detection is realized with the help of an error back propagation mechanism and gradient descent criterion,which effectively improves the communication performance.Simulation results show that the proposed scheme has better robustness and good generalization compared with the traditional receiver algorithm,which not only reduces the algorithm complexity,but also improves the BER performance by about 2dB.

Key words: orthogonal time frequency space modulation, deep learning, channel estimation, signal detection

CLC Number:

TN911

ZHOU Shuo,ZHOU Yiqing,ZHANG Chong,XING Wang. ResNet enabled joint channel estimation and signal detection for OTFS[J].Journal of Xidian University, 2023, 50(3): 19-30.

Figures/Tables 16

References 34

[1]	ZHAO Z, DU Q, WANG D, et al. Overview of Prospects for Service-Aware Radio Access towards 6G Networks[J]. Electronics, 2022, 11(8):1262. doi: 10.3390/electronics11081262
[2]	GONG S, XING C, ZHAO X, et al. Unified IRS-Aided MIMO Transceiver Designs via Majorization Theory[J]. IEEE Transactions on Signal Processing, 2021, 69:3016-3032. doi: 10.1109/TSP.2021.3078571
[3]	GONG S, XING C, LAU V K N, et al. Majorization-Minimization Aided Hybrid Transceivers for MIMO Interference Channels[J]. IEEE Transactions on Signal Processing, 2020, 68:4903-4918. doi: 10.1109/TSP.78
[4]	LIU L, ZHOU Y, YUAN J, et al. Economically Optimal MS Association for Multimedia Content Delivery in Cache-Enabled Heterogeneous Cloud Radio Access Networks[J]. IEEE Journal on Selected Areas in Communications, 2019, 37(7):1584-1593. doi: 10.1109/JSAC.49
[5]	LIU L, ZHOU Y, ZHUANG W, et al. Tractable Coverage Analysis for Hexagonal Macrocell-Based Heterogeneous UDNs with Adaptive Interference-Aware CoMP[J]. IEEE Transactions on Wireless Communications, 2018, 18(1):503-517. doi: 10.1109/TWC.2018.2882434
[6]	DU Q, SONG H, ZHU X. Social-Feature Enabled Communications among Devices toward the Smart IoT Community[J]. IEEE Communications Magazine, 2018, 57(1):130-137.
[7]	LIU L, ZHOU Y, GARCIA V, et al. Load Aware Joint CoMP Clustering and Inter-Cell Resource Scheduling in Heterogeneous Ultra Dense Cellular Networks[J]. IEEE Transactions on Vehicular Technology, 2017, 67(3):2741-2755. doi: 10.1109/TVT.25
[8]	LIN J, WANG G, ATAPATTU S, et al. Transmissive Metasurfaces Assisted Wireless Communications on Railways:Channel Strength Evaluation and Performance Analysis[J]. IEEE Transactions on Communications, 2023, 71(3):1827-1841. doi: 10.1109/TCOMM.2023.3239932
[9]	ZHOU Y, LIU L, WANG L, et al. Service-Aware 6G:An Intelligent and Open Network Based on the Convergence of Communication,Computing and Caching[J]. Digital Communications and Networks, 2020, 6(3):253-260. doi: 10.1016/j.dcan.2020.05.003
[10]	闫实, 彭木根, 王文博. 通信-感知-计算融合:6G愿景与关键技术[J]. 北京邮电大学学报, 2021, 44(4):1-11. doi: 10.13190/j.jbupt.2021-081
	YAN Shi, PENG Mugen, WANG Wenbo. Integration of Communication,Sensing and Computing:the Vision and Key Technologies of 6G[J]. Journal of Beijing University of Posts and Telecommunications, 2021, 44(4):1-11. doi: 10.13190/j.jbupt.2021-081
[11]	LIN J, WANG G, ATAPATTU S, et al. Transmissive Metasurfaces Assisted Wireless Communications on Railways:Channel Strength Evaluation and Performance Analysis[J]. IEEE Transactions on Communications, 2023, 71(3):1827-1841. doi: 10.1109/TCOMM.2023.3239932
[12]	崔新雨, 伍杰, 周一青, 等. 空天地一体化融合组网的挑战与关键技术[J]. 西安电子科技大学学报, 2023, 50(1):1-11.
	CUI Xinyu, WU Jie, ZHOU Yiqing, et al. Challenges of and Key Technologies for the Air-Space-Ground Integrated Network[J]. Journal of Xidian University, 2023, 50(1):1-11.
[13]	李升远, 张馨恬, 唐世阳. 采用OFDM-LFM的MIMO雷达高速目标波形设计[J]. 西安电子科技大学学报, 2018, 45(3):7-12.
	LI Shengyuan, ZHANG Xintian, TANG Shiyang. MIMO Radar Waveform Design via OFDM-LFM for a High Speed Target[J]. Journal of Xidian University, 2018, 45(3):7-12.
[14]	HADANI R, RAKIB S, TSATSANIS M, et al. Orthogonal Time Frequency Space Modulation[C]//2017 IEEE Wireless Communications and Networking Conference (WCNC). Piscataway:IEEE, 2017:1-6.
[15]	RAVITEJA P, HONG Y, VITERBO E, et al. Effective Diversity of OTFS Modulation[J]. IEEE Wireless Communications Letters, 2019, 9(2):249-253. doi: 10.1109/LWC.5962382
[16]	ZHANG C, XING W, YUAN J, et al. Performance of LDPCCoded OTFS Systems over High Mobility Channels[J]. ZTE Communications, 2022, 19(4):45-53.
[17]	HONG Y, THAJ T, VITERBO E. Delay-Doppler Communications:Principles and Applications[M]. Amsterdam:Elsevier, 2022.
[18]	RAVITEJA P, VITERBO E, HONG Y. OTFS Performance on Static Multipath Channels[J]. IEEE Wireless Communications Letters, 2019, 8(3):745-748. doi: 10.1109/LWC.2018.2890643
[19]	ZHANG J, JAYALATH A D S, CHEN Y. Asymmetric OFDM Systems Based on Layered FFT Structure[J]. IEEE Signal Processing Letters, 2007, 14(11):812-815. doi: 10.1109/LSP.2007.903230
[20]	涂岳. 基于深度学习的OTFS信号解调技术研究[D]. 北京: 北京邮电大学, 2021.
[21]	RAVITEJA P, PHAN K T, HONG Y. 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.25
[22]	RAMACHANDRAN M K, CHOCKALINGAM A. MIMO-OTFS in High-Doppler Fading Channels:Signal Detection and Channel Estimation[C]//2018 IEEE Global Communications Conference (GLOBECOM). Piscataway:IEEE, 2018:206-212
[23]	YUAN W, LI S, WEI Z, et al. Data-Aided Channel Estimation for OTFS Systems with a Superimposed Pilot and Data Transmission Scheme[J]. IEEE Wireless Communications Letters, 2021, 10(9):1954-1958. doi: 10.1109/LWC.2021.3088836
[24]	SHEN W, DAI L, AN J, et al. Channel Estimation for Orthogonal Time Frequency Space (OTFS) Massive MIMO[J]. IEEE Transactions on Signal Processing, 2019, 67(16):4204-4217. doi: 10.1109/TSP.78
[25]	SURABHI G D, CHOCKALINGAM A. Low-Complexity Linear Equalization for OTFS Modulation[J]. IEEE Communications Letters, 2019, 24(2):330-334. doi: 10.1109/COML.4234
[26]	TIWARI S, DAS S S, RANGAMGARI V. Low Complexity LMMSE Receiver for OTFS[J]. IEEE Communications Letters, 2019, 23(12):2205-2209. doi: 10.1109/LCOMM.2019.2945564
[27]	ZOU T, XU W, GAO H, et al. Low-Complexity Linear Equalization for OTFS Systems with Rectangular Waveforms[C]//2021 IEEE International Conference on Communications Workshops (ICC Workshops). Piscataway:IEEE, 2021:1-6.
[28]	RAVITEJA P, PHAN K T, HONG Y, et al. Interference Cancellation and Iterative Detection for Orthogonal Time Frequency Space Modulation[J]. IEEE Transactions on Wireless Communications, 2018, 17(10):6501-6515. doi: 10.1109/TWC.2018.2860011
[29]	LI L, LIANG Y, FAN P, et al. Low Complexity Detection Algorithms for OTFS under Rapidly Time-Varying Channel[C]// 2019 IEEE 89th Vehicular Technology Conference (VTC2019-Spring).Piscataway:IEEE, 2019:1-5.
[30]	THAJ T, VITERBO E. Low Complexity Iterative Rake Decision Feedback Equalizer for Zero-Padded OTFS Systems[J]. IEEE Transactions on Vehicular Technology, 2020, 69(12):15606-15622. doi: 10.1109/TVT.2020.3044276
[31]	LI H, DONG Y, GONG C, et al. Low Complexity Receiver via Expectation Propagation for OTFS Modulation[J]. IEEE Communications Letters, 2021, 25(10):3180-3184. doi: 10.1109/LCOMM.2021.3101827
[32]	YUAN W, WEI Z, YUAN J, et al. A simple Variational Bayes Detector for Orthogonal Time Frequency Space (OTFS) Modulation[J]. IEEE Transactions on Vehicular Technology, 2020, 69(7):7976-7980. doi: 10.1109/TVT.25
[33]	YE H, LI G Y, JUANG B H. Power of Deep Learning for Channel Estimation and Signal Detection in OFDM Systems[J]. IEEE Wireless Communications Letters, 2017, 7(1):114-117. doi: 10.1109/LWC.2017.2757490
[34]	HE K, ZHANG X, REN S, et al. Deep Residual Learning for Image Recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE, 2016:770-778.

参数名称	参数值
载波频率/GHz	5
子载波宽度/kHz	30
最大速度/(km·h^-1)	500
OTFS数据帧尺寸	(M,N)=(8,8)
调制方式	BPSK
时延抽头	[0,1]
多普勒抽头	[0,1]

参数名称	参数值
损失函数	MSE
优化器	NAdam,SGDM
学习率	0.001
小批量数据尺寸/样本	8 000
训练集数量/样本	1 000 000
迭代轮次/轮	300

ResNet enabled joint channel estimation and signal detection for OTFS

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