Reinforcement Learning Based Vehicle-cell Association Algorithm for Highly Mobile Millimeter Wave Communication

• URL: http://arxiv.org/abs/2001.07915v1
• Date: Wed, 22 Jan 2020 08:51:05 GMT
• Title: Reinforcement Learning Based Vehicle-cell Association Algorithm for Highly Mobile Millimeter Wave Communication
• Authors: Hamza Khan, Anis Elgabli, Sumudu Samarakoon, Mehdi Bennis, and Choong Seon Hong
• Abstract summary: This paper investigates the problem of vehicle-cell association in millimeter wave (mmWave) communication networks. We first formulate the user state (VU) problem as a discrete non-vehicle association optimization problem. The proposed solution achieves up to 15% gains in terms sum of user complexity and 20% reduction in VUE compared to several baseline designs.
• Score: 53.47785498477648
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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