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In this paper, a novel framework for guaranteeing ultra-reliable millimeterwave (mmW) communications using multiple artificial intelligence (AI)-enabledreconfigurable intelligent surfaces (RISs) is proposed. The use of multipleAI-powered RISs allows changing the propagation direction of the signalstransmitted from a mmW access point (AP) thereby improving coverageparticularly for non-line-of-sight (NLoS) areas. However, due to thepossibility of highly stochastic blockage over mmW links, designing anintelligent controller to jointly optimize the mmW AP beam and RIS phase shiftsis a daunting task. In this regard, first, a parametric risk-sensitive episodicreturn is proposed to maximize the expected bit rate and mitigate the risk ofmmW link blockage. Then, a closed-form approximation of the policy gradient ofthe risk-sensitive episodic return is analytically derived. Next, the problemof joint beamforming for mmW AP and phase shift control for mmW RISs is modeledas an identical payoff stochastic game within a cooperative multi-agentenvironment, in which the agents are the mmW AP and the RISs. Two centralizedand distributed controllers are proposed to control the policies of the mmW APand RISs. To directly find an optimal solution, the parametric functional-formpolicies for these controllers are modeled using deep recurrent neural networks(RNNs). Simulation results show that the error between policies of the optimaland the RNN-based controllers is less than 1.5%. Moreover, the variance of theachievable rates resulting from the deep RNN-based controllers is 60% less thanthe variance of the risk-averse baseline.

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