论文标题
在存在随机阻塞的情况下，基于随机学习的稳健波束形成设计
Stochastic Learning-Based Robust Beamforming Design for RIS-Aided Millimeter-Wave Systems in the Presence of Random Blockages
论文作者
论文摘要
毫米波（MMWave）通信的基本挑战在于它对存在的障碍的敏感性，这会影响通信链接的连通性以及整个网络的可靠性。在本文中，我们分析了可重构的智能表面（RIS）辅助MMWave通信系统，以增强在存在随机阻塞的情况下网络可靠性和连接性。为了在存在随机阻塞的情况下增强杂交模拟数字波束的鲁棒性，我们基于总和中断概率的最小化制定了随机优化问题。为了解决提出的优化问题，我们基于随机块梯度下降方法引入了低复杂性算法，该算法在不搜索所有潜在阻塞链接的所有组合的情况下学习了明智的阻塞模式。数值结果证实了所提出的算法的性能优势，从中断概率和有效数据速率方面。
A fundamental challenge for millimeter wave (mmWave) communications lies in its sensitivity to the presence of blockages, which impact the connectivity of the communication links and ultimately the reliability of the entire network. In this paper, we analyze a reconfigurable intelligent surface (RIS)-aided mmWave communication system for enhancing the network reliability and connectivity in the presence of random blockages. To enhance the robustness of hybrid analog-digital beamforming in the presence of random blockages, we formulate a stochastic optimization problem based on the minimization of the sum outage probability. To tackle the proposed optimization problem, we introduce a low-complexity algorithm based on the stochastic block gradient descent method, which learns sensible blockage patterns without searching for all combinations of potentially blocked links. Numerical results confirm the performance benefits of the proposed algorithm in terms of outage probability and effective data rate.
