Dealing with Limited Backhaul Capacity in Millimeter-Wave Systems: A Deep Reinforcement Learning Approach

Abstract

Millimeter-wave (mmWave) communication is a key technology of fifth generation wireless systems to achieve the expected 1000x data rate. With large bandwidth at the mmWave band, the link capacity between users and base stations (BSs) can be much higher compared to sub-6 GHz wireless systems. Meanwhile, due to the high cost of infrastructure upgrade, it would be difficult for operators to drastically enhance the capacity of backhaul links between mmWave BSs and the core network. As a result, the data rate provided by backhaul may not be sufficient to support all mmWave links; hence, the backhaul connection becomes the new bottleneck. On the other hand, as mmWave channels are subject to random blockage, the data rates of mmWave users significantly vary over time. With limited backhaul capacity and highly dynamic data rates of users, how to allocate backhaul resource to each user remains a challenge for mmWave systems. In this article, we present a deep reinforcement learning (DRL) approach to address this challenge. By learning the blockage pattern, the system dynamics can be captured and predicted, resulting in efficient utilization of backhaul resource. We begin with a discussion on DRL and its application in wireless systems. We then investigate the problem of backhaul resource allocation and present the DRL-based solution. Finally, we discuss open problems for future research and conclude this article.