基于深度学习的单载波频域均衡算法研究  

Deep Learning-Based Single-Carrier Frequency-Domain Equalization

作　　者：柏果 程郁凡[1] 唐万斌[1] BAI Guo;CHENG Yufan;TANG Wanbin(National Key Laboratory of Science and Technology on Communications of UESTC,Chengdu,Sichuan 611731,China)

机构地区：[1]电子科技大学通信抗干扰技术国家级重点实验室,四川成都611731

出　　处：《信号处理》2021年第6期922-931,共10页Journal of Signal Processing

基　　金：国家重点研发计划(编号254);国家自然科学基金(U19B2014);通信抗干扰技术国家级重点实验室基金(2102181402)。

摘　　要：单载波频域均衡(Single-Carrier Frequency-Domain Equalization,SC-FDE)是一种有效的抗码间干扰的算法,在无线通信系统中得到了广泛的应用。传统线性SC-FDE算法主要包括信道估计、噪声功率估计和信道均衡三个模块,其中每个模块都是单独优化的。为了联合优化这三个模块,本文提出了一种基于深度学习的SC-FDE算法。为了减少网络收敛所需的训练数据量,本文为SC-FDE中的三个模块分别设计了一个子网络。此外,本文还提出了一种训练机制,通过平等地对待每条无线路径,提高了所提算法的信道泛化能力。仿真结果表明,所提算法可以在较小的训练数据集下收敛,且具有鲁棒的信道泛化能力,与基于最小二乘信道估计和最小均方误差信道均衡的SC-FDE算法相比,所提算法具有更优的误码率性能。Single-carrier frequency-domain equalization(SC-FDE)is an effective algorithm against inter-symbol interference,which has been widely used in wireless communication systems.Conventional linear SC-FDE mainly includes three modules:channel estimation,noise power estimation and channel equalization,where each module is optimized individually.In order to jointly optimize the three modules,this paper proposes a SC-FDE method based on deep learning.To reduce the amount of training data for network convergence,one subnetwork is particularly designed for each module.Further,a training mechanism is designed to enhance the generalization capability of the proposed approach by treating each path equally.Numerical results show that the proposed algorithm can converge at a small training data set and has robust channel generalization ability.Moreover,the proposed algorithm achieves better performance in terms of bit error rate than the schemes based on least square channel estimation and minimum mean-square error channel equalization.

关 键 词：深度学习 单载波频域均衡 信道估计 

分 类 号：TN911.7[电子电信—通信与信息系统]