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

TL;DR

This paper introduces a new AI-driven framework using multiple intelligent surfaces to enhance ultra-reliable millimeter wave communications, effectively managing stochastic blockages and improving coverage in challenging indoor environments.

Contribution

It develops a risk-sensitive reinforcement learning approach with deep RNN controllers for joint beamforming and phase shift optimization in mmW systems, a novel application in this context.

Findings

Error between optimal and RNN controllers is less than 1.5%.

Variance of achievable rates is reduced by 60% with RNN controllers.

Framework improves coverage and reliability in NLoS indoor mmW communications.

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 bit rate 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 directly find an optimal solution, the parametric functional-form policies for these controllers are modeled using deep recurrent neural networks (RNNs). Simulation results show that the error between 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.