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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 addressthis problem, BSs can work collaboratively to deliver a smooth migration (orhandoff) and satisfy the UEs' service requirements. This paper formulates theload balancing problem as a Markov game and proposes a Robust Multi-agentAttention Actor-Critic (Robust-MA3C) algorithm that can facilitatecollaboration among the BSs (i.e., agents). In particular, to solve the Markovgame and find a Nash equilibrium policy, we embrace the idea of adopting anature agent to model the system uncertainty. Moreover, we utilize theself-attention mechanism, which encourages high-performance BSs to assistlow-performance BSs. In addition, we consider two types of schemes, which canfacilitate load balancing for both active UEs and idle UEs. We carry outextensive evaluations by simulations, and simulation results illustrate that,compared to the state-of-the-art MARL methods, Robust-\ours~scheme can improvethe overall performance by up to 45%.

Multi-agent Attention Actor-Critic Algorithm for Load Balancing in Cellular Networks