论文标题
无源IRS辅助Sumrate最大化的最佳梁形图模型学习
Model-Free Learning of Optimal Beamformers for Passive IRS-Assisted Sumrate Maximization
论文作者
论文摘要
尽管智能反射表面（IRSS）是一项具有成本效益的技术，有望在未来的无线网络中高光谱效率，但是获得最佳的IRS波束形式，这是一个充满挑战的问题，具有一些实际的限制。假设使用完全通知的，无感应的IRS操作，我们引入了一个新的数据驱动的零阶随机梯度上升（ZOSGA）算法，以在IRS AIDED的下行链路设置中进行Sumrate优化。 Zosga不需要访问渠道模型或网络结构信息，并且仅基于常规有效的渠道状态信息，可以与标准的短期预编码共同学习最佳的长期IRS光束器。在最新的（SOTA）收敛分析的支持下，详细的模拟证实，在各种情况下，Zosga也表现出SOTA经验行为，始终超过标准完全基于模型的基线。
Although Intelligent Reflective Surfaces (IRSs) are a cost-effective technology promising high spectral efficiency in future wireless networks, obtaining optimal IRS beamformers is a challenging problem with several practical limitations. Assuming fully-passive, sensing-free IRS operation, we introduce a new data-driven Zeroth-order Stochastic Gradient Ascent (ZoSGA) algorithm for sumrate optimization in an IRS-aided downlink setting. ZoSGA does not require access to channel model or network structure information, and enables learning of optimal long-term IRS beamformers jointly with standard short-term precoding, based only on conventional effective channel state information. Supported by state-of-the-art (SOTA) convergence analysis, detailed simulations confirm that ZoSGA exhibits SOTA empirical behavior as well, consistently outperforming standard fully model-based baselines, in a variety of scenarios.
