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.

中文翻译：

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使用隐式信道估计学习反射和波束形成的智能反射面

智能反射面 (IRS) 由大量可调反射元件组成，能够通过智能地将来自基站 (BS) 的电磁波反射给用户，从而增强蜂窝网络中的无线传播环境。然而，IRS 移相器的最佳调谐是一个具有挑战性的问题，因为由于反射元件的无源特性，很难直接测量 IRS、BS 和用户之间的信道。与遵循先估计信道然后优化系统参数的传统范式不同，本文提倡一种机器学习方法，该方法能够基于系统目标直接优化 BS 的波束形成器和 IRS 的反射系数。这是通过使用深度神经网络来参数化从接收到的导频（加上任何附加信息，例如用户位置）到优化系统配置的映射，并通过采用置换不变/等变图神经网络 (GNN) 架构来实现的捕捉蜂窝网络中不同用户之间的交互。仿真结果表明，与传统的基于显式信道估计的方法相比，所提出的基于隐式信道估计的方法是可推广的，可以解释的，并且可以有效地从更少的导频数中学习最大化总速率或最小速率目标。并通过采用置换不变/等变图神经网络 (GNN) 架构来捕获蜂窝网络中不同用户之间的交互。仿真结果表明，与传统的基于显式信道估计的方法相比，所提出的基于隐式信道估计的方法是可推广的，可以解释的，并且可以有效地从更少的导频数中学习最大化总速率或最小速率目标。并通过采用置换不变/等变图神经网络 (GNN) 架构来捕获蜂窝网络中不同用户之间的交互。仿真结果表明，与传统的基于显式信道估计的方法相比，所提出的基于隐式信道估计的方法是可推广的，可以解释的，并且可以有效地从更少的导频数中学习最大化总速率或最小速率目标。