用深度神经网络加速智能表面辅助毫米波通信的波束成形设计
DNN-Based Precoding in RIS-Aided mmWave MIMO Systems With Practical Phase Shift
- 用训练好的DNN替代传统穷举搜索，快速选择最优波束成形码字
- 即使用户与智能表面距离变化，仍保持接近最优的频谱效率
- 适合需要低延迟波束成形的毫米波通信系统研发人员
本文研究了在直视路径受阻的毫米波多输入多输出（MIMO）系统中，通过可重构智能表面（RIS）增强传输性能的波束成形设计，以最大化系统吞吐量。考虑毫米波通信中的视距（LoS）和多径效应特性，采用连续相移的优化方法存在计算复杂度高、耗时长的问题。为降低计算开销，提出使用经排列的离散傅里叶变换（DFT）向量进行码本设计，并结合实际或理想RIS系统的振幅响应。尽管采用离散相移后穷举搜索仍具高复杂度，本文转而使用训练好的深度神经网络（DNN）实现更快速的码字选择。仿真结果表明，即使在测试阶段用户与RIS距离发生变化，该DNN方法仍能维持次优的频谱效率，展现出其在提升RIS辅助系统性能方面的潜力。
原文摘要 · Abstract (English)
In this paper, the precoding design is investigated for maximizing the throughput of millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems with obstructed direct communication paths. In particular, a reconfigurable intelligent surface (RIS) is employed to enhance MIMO transmissions, considering mmWave characteristics related to line-of-sight (LoS) and multipath effects. The traditional exhaustive search (ES) for optimal codewords in the continuous phase shift is computationally intensive and time-consuming. To reduce computational complexity, permuted discrete Fourier transform (DFT) vectors are used for finding codebook design, incorporating amplitude responses for practical or ideal RIS systems. However, even if the discrete phase shift is adopted in the ES, it results in significant computation and is time-consuming. Instead, the trained deep neural network (DNN) is developed to facilitate faster codeword selection. Simulation results show that the DNN maintains sub-optimal spectral efficiency even as the distance between the end-user and the RIS has variations in the testing phase. These results highlight the potential of DNN in advancing RIS-aided systems.
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