Proactive Load Balancing Through Constrained Policy Optimization for Ultra-Dense Networks

• Published in: IEEE communications letters Vol. 26; no. 10; pp. 2415 - 2419
• Main Authors: Huang, Miaona, Chen, Jun
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.10.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Bayesian additive regression tree; constrained policy optimization; Constraints; Handover; Load balancing; Load management; Load modeling; mobility load balancing; Optimization; Quality of service; Regression analysis; Regression tree analysis; Reinforcement learning; ultra-dense networks; User experience; Wireless networks
• ISSN: 1089-7798; 1558-2558
• DOI: 10.1109/LCOMM.2022.3190284

Designing an intelligent self-organizing network (SON) architecture is challenging for future wireless networks. To meet the needs of SON, the reactive self-organizing model of the traditional network needs to be transformed into an active and interactive one. Due to the user mobility and small coverage of cells in ultra-dense networks (UDNs), the network load usually becomes unbalanced, leading to deteriorated network performance, such as low throughput, radio link failure, and poor user experience. Therefore, the technique of mobility load balancing (MLB) is critical to ensuring a seamless user experience among cells. This letter proposes an active and interactive MLB strategy for UDNs, which transforms the original reactive MLB into a forward-aware and active one. In particular, user mobility is first predicted based on the Bayesian additive regression tree (BART). Then, with the mobility predictions, the joint mobility robust optimization and MLB problem subject to users' rate constraint is solved via safe reinforcement learning. The pertaining simulation results show that the proposed method can improve the network performance and realize intelligent mobile management for future UDNs.