论文标题

通过隐式渠道估计,学习反思和亮板上的智能反射表面

Learning to Reflect and to Beamform for Intelligent Reflecting Surface with Implicit Channel Estimation

论文作者

Jiang, Tao, Cheng, Hei Victor, Yu, Wei

论文摘要

由大量可调反射元件组成的智能反射表面(IRS)能够通过智能地反映从基础站(BS)向用户的电磁波来增强蜂窝网络中的无线传播环境。但是,在IRS,相位变速器的最佳调整是一个具有挑战性的问题,因为由于反射元素的被动性质,很难直接测量IRS,BS和用户之间的通道。本文提倡一种机器学习方法,能够基于系统目标,而不是遵循传统的首次估算通道的范式,而是提倡一种机器学习方法,它能够直接优化BS处的波束形式和IRS的反射系数。这是通过使用深层神经网络将映射从接收到的飞行员(加上任何其他信息(例如用户位置)到优化的系统配置)的参数来实现的,并通过采用置换不变/eparianiant/Equivariant图形神经网络(GNN)体系结构来捕获蜂窝网络中不同用户之间的交互。仿真结果表明,所提出的基于隐式通道估计方法的方法是可以推广的,可以解释,并且可以有效地学会从试点数量少得多的飞行员数量的总和率或最低率目标最大化,而不是传统的基于渠道估计的方法。

Intelligent reflecting surface (IRS), which consists of a large number of tunable reflective elements, is capable of enhancing the wireless propagation environment in a cellular network by intelligently reflecting the electromagnetic waves from the base-station (BS) toward the users. The optimal tuning of the phase shifters at the IRS is, however, a challenging problem, because due to the passive nature of reflective elements, it is difficult to directly measure the channels between the IRS, the BS, and the users. Instead of following the traditional paradigm of first estimating the channels then optimizing the system parameters, this paper advocates a machine learning approach capable of directly optimizing both the beamformers at the BS and the reflective coefficients at the IRS based on a system objective. This is achieved by using a deep neural network to parameterize the mapping from the received pilots (plus any additional information, such as the user locations) to an optimized system configuration, and by adopting a permutation invariant/equivariant graph neural network (GNN) architecture to capture the interactions among the different users in the cellular network. Simulation results show that the proposed implicit channel estimation based approach is generalizable, can be interpreted, and can efficiently learn to maximize a sum-rate or minimum-rate objective from a much fewer number of pilots than the traditional explicit channel estimation based approaches.

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