Reinforcement learning based vehicle-cell association algorithm for highly mobile millimeter wave communication
Khan, Hamza; Elgabli, Anis; Samarakoon, Sumudu; Bennis, Mehdi; Hong, Choong Seon (2019-09-12)
H. Khan, A. Elgabli, S. Samarakoon, M. Bennis and C. S. Hong, "Reinforcement Learning-Based Vehicle-Cell Association Algorithm for Highly Mobile Millimeter Wave Communication," in IEEE Transactions on Cognitive Communications and Networking, vol. 5, no. 4, pp. 1073-1085, Dec. 2019. doi: 10.1109/TCCN.2019.2941191
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https://urn.fi/URN:NBN:fi-fe202002195811
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Abstract
Vehicle-to-everything (V2X) communication is a growing area of communication with a variety of use cases. This paper investigates the problem of vehicle-cell association in millimeter wave (mmWave) communication networks. The aim is to maximize the time average rate per vehicular user (VUE) while ensuring a target minimum rate for all VUEs with low signaling overhead. We first formulate the user (vehicle) association problem as a discrete non-convex optimization problem. Then, by leveraging tools from machine learning, specifically distributed deep reinforcement learning (DDRL) and the asynchronous actor critic algorithm (A3C), we propose a low complexity algorithm that approximates the solution of the proposed optimization problem. The proposed DDRL-based algorithm endows every road side unit (RSU) with a local RL agent that selects a local action based on the observed input state. Actions of different RSUs are forwarded to a central entity, that computes a global reward which is then fed back to RSUs. It is shown that each independently trained RL performs the vehicle-RSU association action with low control overhead and less computational complexity compared to running an online complex algorithm to solve the non-convex optimization problem. Finally, simulation results show that the proposed solution achieves up to 15% gains in terms of sum rate and 20% reduction in VUE outages compared to several baseline designs.
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