Ultra-Reliable Indoor Millimeter Wave Communications Using Multiple Artificial Intelligence-Powered Intelligent Surfaces

Abstract

In this paper, a novel framework for guaranteeing ultra-reliable millimeter wave (mmW) communications using multiple artificial intelligence (AI)-enabled reconfigurable intelligent surfaces (RISs) is proposed. The use of multiple AI-powered RISs allows changing the propagation direction of the signals transmitted from a mmW access point (AP) thereby improving coverage particularly for non-line-of-sight (NLoS) areas. However, due to the possibility of highly stochastic blockage over mmW links, designing an intelligent controller to jointly optimize the mmW AP beam and RIS phase shifts is a daunting task. In this regard, first, a parametric risk-sensitive episodic return is proposed to maximize the expected bitrate and mitigate the risk of mmW link blockage. Then, a closed-form approximation of the policy gradient of the risk-sensitive episodic return is analytically derived. Next, the problem of joint beamforming for mmW AP and phase shift control for mmW RISs is modeled as an identical payoff stochastic game within a cooperative multi-agent environment, in which the agents are the mmW AP and the RISs. Two centralized and distributed controllers are proposed to control the policies of the mmW AP and RISs. To <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">directly</i> find a near optimal solution, the parametric functional-form policies for the controllers are modeled using deep recurrent neural networks (RNNs). The deep RNN-based controllers are then trained based on the derived closed-form gradient of the risk-sensitive episodic return. It is proved that the gradient update algorithm converges to the same locally optimal parameters as the deep RNN-based centralized and distributed controllers. Simulation results show that the error between the policies of the optimal and the RNN-based controllers is less than 1.5%. Moreover, the variance of the achievable rates resulting from the deep RNN-based controllers is 60% less than the variance of the risk-averse baseline.