Intelligent On/Off Switching of mmRSUs in Urban Vehicular Networks: A Deep Q-Learning Approach

Abstract

In the next generation of vehicular network applications, complex data processing and reliable and quick message transmissions are critical. Traditional cellular macro base stations and IEEE WAVE technology are incapable of supporting such high data speeds and ultra-reliable low latency communication. The combination of 5G RSUs equipped with mmWave beams (mmRSUs) and edge computing methods have been proposed as a possible solution for meeting such service needs. However, since urban vehicle traffic is often predictable, the mmRSUs need not be kept ON all the time to provide services. Instead, the mmRSUs may be dynamically turned ON/OFF depending on current traffic conditions, hence reducing energy consumption without compromising service. We construct the intelligent switching of mmRSUs as an Integer Linear Program to maximize the system's utility by dynamically turning them on/off in order to spend less energy. We propose a strategy based on Deep Q-Learning to accomplish the goal and demonstrate its usefulness in a city with real traffic.