Learning-based Load Balancing Handover in Mobile Millimeter Wave Networks

TL;DR

This paper introduces a learning-based load balancing handover algorithm for mobile mmWave networks that optimizes user association and resource allocation to improve sum rate and reduce handovers.

Contribution

It proposes a novel deep reinforcement learning approach to jointly address load balancing and handover management in mobile mmWave networks.

Findings

Reduces the number of rate violations and handovers.

Increases overall sum rate of users.

Demonstrates effectiveness through simulations.

Abstract

Millimeter-wave (mmWave) communication is a promising solution to the high data rate demands in the upcoming 5G and beyond communication networks. When it comes to supporting seamless connectivity in mobile scenarios, resource and handover management are two of the main challenges in mmWave networks. In this paper, we address these two problems jointly and propose a learning-based load balancing handover in multi-user mobile mmWave networks. Our handover algorithm selects a backup base station and allocates the resource to maximize the sum rate of all the users while ensuring a target rate threshold and preventing excessive handovers. We model the user association as a non-convex optimization problem. Then, by applying a deep deterministic policy gradient (DDPG) method, we approximate the solution of the optimization problem. Through simulations, we show that our proposed algorithm minimizes the number of the events where a user's rate is less than its minimum rate requirement and minimizes the number of handovers while increasing the sum rate of all users.