University of Oulu

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,

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

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Author: Soorki, Mehdi Naderi1,2; Saad, Walid1; Bennis, Mehdi2
Organizations: 1Wireless@VT, Bradley Department of Electrical and Computer Engineering, Virginia Tech, Blacksburg, VA, USA
2Centre for Wireless Communications, University of Oulu, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.4 MB)
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Language: English
Published: Institute of Electrical and Electronics Engineers, 2019
Publish Date: 2020-05-04


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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Series: IEEE Global Communications Conference
ISSN: 2334-0983
ISSN-E: 2576-6813
ISSN-L: 2334-0983
ISBN: 978-1-7281-0962-6
ISBN Print: 978-1-7281-0963-3
Pages: 1 - 6
Article number: 9013431
DOI: 10.1109/GLOBECOM38437.2019.9013431
Host publication: 2019 IEEE Global Communications Conference, GLOBECOM 2019
Conference: IEEE Global Communications Conference
Type of Publication: A4 Article in conference proceedings
Field of Science: 213 Electronic, automation and communications engineering, electronics
Funding: This research was supported by the U.S. National Science Foundation under Grants CNS-1836802 and CNS-1814477.
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