August 15, 2025Open Access

Incentivizing Cooperation for Handover Strategies in LEO Constellations via Fairness-guided MARL

XLXiupu LangShanghai Jiao Tong University LSLin SunNanjing University PHPeiqi HuangShanghai Jiao Tong University

Key Points

DHO-F achieves an average improvement of 3.48% in the social welfare function metric, enhancing service continuity.
Fairness in handover strategies is modeled using a multi-objective Markov decision process to optimize outcomes.
The approach utilizes Multi-agent Proximal Policy Optimization for better collaboration among agents, focusing on fairness and throughput.
DHO-F outperforms centralized algorithms, establishing state-of-the-art performance under medium-to-high load scenarios.

Abstract

Handover (HO) is one of the pivotal technologies for mobility management in highly dynamic mega LEO Earth Orbit satellite constellations (MLSCs). Due to the lack of channel reservation under random access (RA) and the prohibitive overhead incurred by centralized HO scheduling, intense competition among massive connections (e.g., IoRT nodes) results in significant degradation of service continuity. To solve the above challenges, we propose a distributed fairness-guided handover strategy (DHO-F) to dynamically select the best HO target and sub-channel. Specifically, the DHO-F optimization problem is formulated based on max-min egalitarian fairness and further modeled as a multi-objective Markov decision process (MOMDP), where fairness is expressed by the social welfare function (SWF). To address MOMDP, the analytical form of the policy gradient to maximize fairness is derived. Subsequently, Multi-agent Proximal Policy Optimization (MAPPO) with distributed cooperation is exploited to achieve long-term maximization. The fairness-guided MAPPO (FG-MAPPO) features a hybrid network architecture that simultaneously takes into account maximizing individual link rates and fairness among UEs. It reconciles these two conflicting objectives through the collaboration between a throughput-oriented (TO) network and a fairness-oriented (FO) network. Additionally, a distributed training framework is implemented to improve the sample efficiency and data diversity for on-policy FG-MAPPO. FG-MAPPO is fully compatible with 3GPP’s conditional handover (CHO) framework, demonstrating that it can be implemented in real world. Extensive evaluations demonstrate that DHO-F demonstrates superior performance even compared to centralized algorithms, achieving an average improvement of 3.48% in SWF metric. Moreover, DHO-F establishes new SOTA performance in balancing fairness and rate maximization across medium-to-high load scenarios compared to IDQN, ISAC, and MAPPO.

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