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Ultra-reliable millimeter-wave communications using an artificial intelligence-powered reflector

Soorki, Mehdi Naderi; Saad, Walid; Bennis, Mehdi (2020-02-27)

 
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https://doi.org/10.1109/GLOBECOM38437.2019.9013431

Soorki, Mehdi Naderi
Saad, Walid
Bennis, Mehdi
Institute of Electrical and Electronics Engineers
27.02.2020

M. N. Soorki, W. Saad and M. Bennis, "Ultra-Reliable Millimeter-Wave Communications Using an Artificial Intelligence-Powered Reflector," 2019 IEEE Global Communications Conference (GLOBECOM), Waikoloa, HI, USA, 2019, pp. 1-6, https://doi.org/10.1109/GLOBECOM38437.2019.9013431

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doi:https://doi.org/10.1109/GLOBECOM38437.2019.9013431
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Abstract

In this paper, a novel framework for guaranteeing ultra-reliable millimeter-wave (mmW) communications using a smart, artificial intelligence (AI)-powered mmW reflector is proposed. The use of an AI-powered reflector allows changing the propagation direction of mmW signals and, thus, improving coverage particularly for non-line-of-sight (LoS) areas. However, due to the possibility of stochastic blockage over mmW links, designing an intelligent phase shift-control policy for the mmW reflector to guarantee ultra-reliable mmW communications becomes very challenging. In this regard, first, based on the framework of risk-sensitive reinforcement learning, a parametric risk-sensitive episodic return is proposed to maximize the expected bit rate while mitigating the risk of non-LoS mmW link in the presence of future stochastic blockage over the mmW links. Then, a closed-form approximation for the gradient of the risk- sensitive episodic return is analytically derived. To \emph{directly} find the optimal policy for the proposed phase-shift controller, a parametric functional-form policy is implemented using a deep recurrent neural network (RNN). Then, based on the derived closed-form gradient of risk-sensitive episodic return, the deep RNN-based parametric functional-form policy is trained. The efficiency of the proposed AI-powered reflector is evaluated in an office environment. Simulation results show that the root-mean- square errors between the optimal and approximate phase shift-control policies of the proposed deep RNN is 1.35% in the worst case. Moreover, on average, the mean value and variance of the achievable rates resulting from the deep RNN-based policy are only 1% and 2% less than the optimal solution for different unknown mobile users’ trajectories, respectively.

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