Paper Info
Title
Multiobjective Load Balancing for Multiband Downlink Cellular Networks: A Meta- Reinforcement Learning Approach
Abstract
Load balancing has become a key technique to handle the increasing traffic demand and improve the user experience. It evenly distributes the traffic across network resources by offloading users from overloaded base stations or channels to less crowded ones. Load balancing is a multi-objective optimization problem involving the automatic adjustment of several parameters to simultaneously maximize multiple network performance indicators. However, the existing methods mostly rely on single-objective approaches which lead to sub-optimal solutions. In this paper, we introduce the first multi-objective reinforcement learning (MORL) framework for load balancing. Specifically, we propose a solution based on meta-reinforcement learning (meta-RL) to learn a general policy capable of quickly adapting to new trade-offs between the objectives. We further enhance the generalization of our proposed solution using policy distillation techniques. To showcase the effectiveness of our framework, experiments are conducted based on real-world traffic scenarios. Our results show that our load balancing framework can (i) significantly outperform the existing rule-based and single-objective solutions, (ii) compute better Pareto front approximations compared to MORL baselines, and (iii) quickly adapt to new objective trade-offs.
Year
DOI
Venue
2022
10.1109/JSAC.2022.3191114
IEEE Journal on Selected Areas in Communications
Keywords
DocType
Volume
Downlink communication,load balancing,multi-objective reinforcement learning,meta-reinforcement learning,policy distillation
Journal
40
Issue
ISSN
Citations 
9
0733-8716
0
PageRank 
References 
Authors
0.34
16
8
Name
Order
Citations
PageRank
Amal Feriani100.34
Di Wu2636117.73
Yi Tian Xu303.04
Jimmy Li400.34
Seowoo Jang501.69
Ekram Hossain68586453.41
Xue Liu78823.33
Gregory Dudek82163255.48