基于深度CLSTM的集成传感和通信车辆网络预测波束形成

Chang Liu;Xuemeng Liu;Shuangyang Li;Weijie Yuan;Derrick Wing Kwan Ng
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引用次数: 4

摘要

预测波束形成设计是实现高移动性集成传感与通信(ISAC)的一项重要任务,它高度依赖于信道预测(CP)的精度,即预测用户的角度参数。然而,CP的性能高度依赖于估计的历史信道陈述信息(CSI),并且存在估计误差,这导致大多数传统CP方法的性能下降。为了进一步提高预测精度,本文针对车辆网络中的ISAC问题,提出了一种卷积长短期记忆(CLSTM)递归神经网络(CLRNet)来预测车辆的角度,为预测波束形成的设计提供依据。在开发的CLRNet中,采用卷积神经网络(CNN)模块和LSTM模块,利用车辆历史角度估计的空间特征和时间依赖性来进行角度预测。最后,数值结果表明,所开发的基于clrnet的方法对估计误差具有鲁棒性,并且可以显著优于最先进的基准,实现了ISAC系统出色的和速率性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deep CLSTM for Predictive Beamforming in Integrated Sensing and Communication-Enabled Vehicular Networks
Predictive beamforming design is an essential task in realizing high-mobility integrated sensing and communication (ISAC), which highly depends on the accuracy of the channel prediction (CP), i.e., predicting the angular parameters of users. However, the performance of CP highly depends on the estimated historical channel stated information (CSI) with estimation errors, resulting in the performance degradation for most traditional CP methods. To further improve the prediction accuracy, in this paper, we focus on the ISAC in vehicle networks and propose a convolutional long-short term memory (CLSTM) recurrent neural network (CLRNet) to predict the angle of vehicles for the design of predictive beamforming. In the developed CLRNet, both the convolutional neural network (CNN) module and the LSTM module are adopted to exploit the spatial features and the temporal dependency from the estimated historical angles of vehicles to facilitate the angle prediction. Finally, numerical results demonstrate that the developed CLRNet-based method is robust to the estimation error and can significantly outperform the state-of-the-art benchmarks, achieving an excellent sum-rate performance for ISAC systems.
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