Distributed interference coordination based on multi-agent deep reinforcement learning - Tingting LIU; Yi’nan LUO; Chenyang YANG (Tongxin xuebao)
A distributed interference coordination strategy based on multi-agent deep reinforcement learning was investigated to meet the requirements of file downloading traffic in interference networks.By the proposed strategy transmission scheme could be adjusted adaptively based on the interference environment and traffic requirements with limited amount of information exchanged among nodes.Simulation results show that the user satisfaction loss of the proposed strategy from the optimal strategy with perfect future information does not exceed 11% for arbitrary number of users and traffic requirements.