arXiv 2103.015312021-03-02eess.SP

基于可重构智能表面的无线网络智能频谱学习

Intelligent Spectrum Learning for Wireless Networks with Reconfigurable Intelligent Surfaces

Bo Yang, Xuelin Cao, Chongwen Huang, Chau Yuen, Lijun Qian, Marco Di Renzo

摘要：

可重构智能表面（RIS）通过适当的相移反射期望信号，是一种提高无线通信可靠性的有效技术。然而，撞击RIS的预期信号通常与干扰信号混合，干扰信号通常是动态的和未知的。具体地，接收到的信号与干扰加噪声比（SINR）可能被从源自非预期用户的RI反射的信号降级。为了解决这个问题，我们引入了智能频谱学习（ISL）的概念，它在RIS控制器中使用经过适当训练的卷积神经网络（CNN）来帮助RIS直接从入射信号中推断干扰信号。通过利用ISL，提出了一种分布式控制算法，通过动态配置RIS元件的活动/非活动二进制状态来最大化接收SINR。仿真结果验证了深度学习提供的性能改进，并证明了所提出的ISL辅助方法的优越性。

英文摘要：

Reconfigurable intelligent surface (RIS) has become a promising technology for enhancing the reliability of wireless communications, which is capable of reflecting the desired signals through appropriate phase shifts. However, the intended signals that impinge upon an RIS are often mixed with interfering signals, which are usually dynamic and unknown. In particular, the received signal-to-interference-plus-noise ratio (SINR) may be degraded by the signals reflected from the RISs that originate from non-intended users. To tackle this issue, we introduce the concept of intelligent spectrum learning (ISL), which uses an appropriately trained convolutional neural network (CNN) at the RIS controller to help the RISs infer the interfering signals directly from the incident signals. By capitalizing on the ISL, a distributed control algorithm is proposed to maximize the received SINR by dynamically configuring the active/inactive binary status of the RIS elements. Simulation results validate the performance improvement offered by deep learning and demonstrate the superiority of the proposed ISL-aided approach.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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