University of Oulu

C. Chaccour, M. N. Soorki, W. Saad, M. Bennis and P. Popovski, "Risk-Based Optimization of Virtual Reality over Terahertz Reconfigurable Intelligent Surfaces," ICC 2020 - 2020 IEEE International Conference on Communications (ICC), Dublin, Ireland, 2020, pp. 1-6, doi: 10.1109/ICC40277.2020.9149411

Risk-based optimization of virtual reality over terahertz reconfigurable intelligent surfaces

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Author: Chaccour, Christina1; Soorki, Mehdi Naderi2; Saad, Walid1;
Organizations: 1Wireless@ VT, Bradly Department of Electrical and Computer Engineering, Virginia Tech, Blacksburg, VA USA
2Faculty of Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran
3Centre for Wireless Communications, University of Oulu, Finland
4Department of Electronic Systems, Aalborg University, Denmark
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.8 MB)
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Language: English
Published: Institute of Electrical and Electronics Engineers, 2020
Publish Date: 2021-02-19


In this paper, the problem of associating reconfigurable intelligent surfaces (RISs) to virtual reality (VR) users is studied for a wireless VR network. In particular, this problem is considered within a cellular network that employs terahertz (THz) operated RISs acting as base stations. To provide a seamless VR experience, high data rates and reliable low latency need to be continuously guaranteed. To address these challenges, a novel risk-based framework based on the entropic value-at-risk is proposed for rate optimization and reliability performance. Furthermore, a Lyapunov optimization technique is used to reformulate the problem as a linear weighted function, while ensuring that higher order statistics of the queue length are maintained under a threshold. To address this problem, given the stochastic nature of the channel, a policy-based reinforcement learning (RL) algorithm is proposed. Since the state space is extremely large, the policy is learned through a deep-RL algorithm. In particular, a recurrent neural network (RNN) RL framework is proposed to capture the dynamic channel behavior and improve the speed of conventional RL policy-search algorithms. Simulation results demonstrate that the maximal queue length resulting from the proposed approach is only within 1% of the optimal solution. The results show a high accuracy and fast convergence for the RNN with a validation accuracy of 91.92%.

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Series: IEEE International Conference on Communications
ISSN: 1550-3607
ISSN-E: 1938-1883
ISSN-L: 1550-3607
ISBN: 978-1-7281-5089-5
ISBN Print: 978-1-7281-5090-1
Article number: 9149411
DOI: 10.1109/ICC40277.2020.9149411
Host publication: 2020 IEEE International Conference on Communications, ICC 2020
Conference: IEEE International Conference on Communications
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 Grant CNS-1836802, and in part, by the Academy of Finland Project CARMA, by the Academy of Finland Project MISSION, by the Academy of Finland Project SMARTER, as well as by the INFOTECH Project NOOR.
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