Paper Info
Title
Adaptive Multi-objective Reinforcement Learning for Pareto Frontier Approximation: A Case Study of Resource Allocation Network in Massive MIMO
Abstract
Multi-Objective Optimization (MOO) has always been an important issue in the field of wireless communications. With the development of 5G networks, more objectives have been concerned to improve the user experience. The relationship between these multiple objectives is complex or even conflicting, which increases the difficulty of solving the MOO problems. Traditional multi-objective optimization algorithms (e.g., genetic algorithm) have higher computation complexity and require to store multiple models for the preference of different objectives. Therefore, in this paper, a multi-objective scheduling model based on the Actor-Critic framework is proposed, which can effectively solve the multi-user scheduling problem under Massive Multiple-Input Multiple-Output (MIMO), and utilize a single model to approximate the Pareto frontier. In the single-cell downlink scheduling scenario, the proposed model is applied to the two objective optimization, i.e., channel capacity and fairness. The simulation results show that the performance of our model is close to the theoretical optimal value in the single-objective case. The Pareto frontier can be uniformly approximated in the multi-objective case, and it has strong robustness to never-seen preference combinations.
Year
DOI
Venue
2021
10.23919/EUSIPCO54536.2021.9615934
29TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO 2021)
Keywords
DocType
ISSN
Massive MIMO, multi-objective reinforcement learning (MORL), Pareto frontier, single cell Multi-User (MU)-MIMO scheduling
Conference
2076-1465
Citations 
PageRank 
References 
0
0.34
0
Authors
7
Name
Order
Citations
PageRank
Ruiqing Chen100.68
Fanglei Sun202.70
Liang Chen300.68
Kai Li424.49
Liantao Wu501.69
Jun Wang62514138.37
Yang Yang79515.08