基于深度学习智能反射面辅助通信系统的联合波束成形

Deep Learning Based Joint Beamforming in Intelligent Reflecting Surface Enhanced Wireless Communication Systems

智能反射面(Intelligent Reflecting Surface,IRS)技术被认为是下一代无线通信中颇具潜力的技术之一。现有的IRS辅助多输入多输出(Multiple Input Multiple Output,MIMO)系统的波束成形设计对天线的计算能力要求很高,仍然是一个极具挑战的难题。为了克服这一难题,提出了一种用于IRS辅助多用户MIMO系统的基于深度学习的联合波束成形设计方案,以实现系统多用户和数据传输速率的最大化。该方案利用深度学习卷积神经网络学习和优化了基站端数字波束成形,同时设计出最优IRS端反射波束成形。所提方案克服了神经网络直接预测波束成形矩阵的困难,仅预测从波束成形矩阵中提取的关键特征,对神经网络预测能力的需求大大降低,并将线下训练及优化的结果用于线上,显著降低了实时计算复杂度。仿真结果显示,提出的最优波束成形能获得超过0.5~1 bit/s/Hz的系统和速率性能提升,且该优势会随着用户数增多而增大。

Intelligent reflecting surface(IRS),as one of the most potential technologies in the next-generation wireless communication,plays a significant role.However,the existing IRS-assisted multiple-input multiple-output(MIMO)systems face a challenging problem that the beamforming methods require high computational capabilities of the antennas.To address this challenge,a deep learning(DL)-based joint beamforming design has been proposed for IRS-aided multi-user MIMO communication systems aiming to maximize the sum data rate of all users.The proposed DL-based beamforming scheme utilizes convolutional neural network to jointly optimize digital beamforming at base station and reflection beamforming at IRS.The proposed DL-based beamforming method forecasts the essential features extracted from the beamforming matrix,which overcomes the challenge of direct prediction of beamforming matrix by neural network.This method significantly reduces the demand on the predictive capability of the neural network,and the trained and optimized beamforming designs are using online that can significantly reduce the real-time computational complexity.Simulation results demonstrate that the proposed beamforming design can achieve over 0.5~1 bit/s/Hz data rate improvement,which will be enhanced with the growth of user number.