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

摘要/Abstract

摘要：

正交时频空调制能够在高多普勒频偏下实现宽带可靠通信,是6G感通算融合场景中的潜在应用技术之一。针对该系统中接收机算法复杂度高、性能受限的问题,提出了一种基于修正残差神经网络的联合信道估计和信号检测方案,在无需获得显式信道信息的情况下直接恢复传输符号信息。根据时延-多普勒域信道的稳定性,将深度学习技术引入到接收机设计中,采用嵌入式导频的数据帧结构,设计了一种能够充分提取信号特征的轻量级残差神经网络模型,可以直接对时延-多普勒域信号输入输出关系进行拟合,实现隐式的信道估计并完成信号检测。联合设计方案利用实际通信链路中采集的数据进行离线训练,获取最优网络模型用于在线检测,以离线训练时间为代价来减少在线检测的耗时,同时借助误差反向传播机制和梯度下降准则实现信道估计和信号检测的联合优化,有效提升通信性能。仿真结果表明,与传统接收算法对比,所提方案兼具更强的鲁棒性和良好的泛化性,不仅降低了算法的复杂度,同时将误码率性能也提升了2 dB左右。

关键词: 正交时频空调制, 深度学习, 信道估计, 信号检测

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

中图分类号:

TN911

周硕,周一青,张冲,邢旺. ResNet使能的OTFS联合信道估计和信号检测[J]. 西安电子科技大学学报, 2023, 50(3): 19-30.

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.

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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