User-Centric HetNet Handover in Industrial Context Based on Pareto-Efficient Multiagent Transformer

Expanding industrial components and network density raise challenges in the domain of mobility management of multiagent systems (MASs), such as multirobot cooperative transportation. This article investigates the heterogeneous network (HetNet) handover problem in the industrial context involves jointly optimizing data rate, block rate, and handover frequency among large-scale mobile user Terminals. Specifically, by introducing user-centric conditional handover features, we leverage Pareto-efficient solutions to address the multiobjective optimization problem of balancing data rate and block probability. The optimization problem is reformulated into a multiagent learning-based Markov cooperative game to cope with dynamic context conditions, introducing a handover penalty factor to enhance service continuity. Furthermore, we develop a Pareto-efficient multiagent transformer with efficient advantage decomposition, leveraging sequential modeling, and distributed computing power of MAS. Extensive simulations demonstrate the superiority of the proposed algorithm, implementing user-centric optimal handover decisions, while also obtaining an additional fairness gain.