In cellular networks, User Equipment (UE) handoff from one Base Station (BS) to another, giving rise to the load balancing problem among the BSs. To address this problem, BSs can work collaboratively to deliver a smooth migration (or handoff) and satisfy the UEs' service requirements. This paper formulates the load balancing problem as a Markov game and proposes a Robust Multi-agent Attention Actor-Critic (Robust-MA3C) algorithm that can facilitate collaboration among the BSs (i.e., agents). In particular, to solve the Markov game and find a Nash equilibrium policy, we embrace the idea of adopting a nature agent to model the system uncertainty. Moreover, we utilize the self-attention mechanism, which encourages high-performance BSs to assist low-performance BSs. In addition, we consider two types of schemes, which can facilitate load balancing for both active UEs and idle UEs. We carry out extensive evaluations by simulations, and simulation results illustrate that, compared to the state-of-the-art MARL methods, Robust-\ours~scheme can improve the overall performance by up to 45%.

本文研究了在蜂窝网络中，基站之间的负载均衡问题，提出了一种鲁棒的多智能体协作算法，使用马尔科夫博弈建模并采用自我注意力机制和两种类型的方案来平衡活跃UE和空闲UE的负载。通过大量模拟实验，与现有MARL方法相比，该方案的整体性能提高了多达45%。

多智能体注意力演员-评论家算法在蜂窝网络负载平衡中的应用