Multi-agent Deep Reinforcement Learning for Multi-Cell Interference Mitigation

Abstract

Multi-cell interference management techniques typically require sharing channel state information (CSI) among all cells involved, making the algorithms ineffective for practical uses. To overcome this shortcoming, an interference mitigation technique that does not require explicit CSI or coordination among neighboring cells is developed in this paper. The algorithm leverages distributed deep reinforcement learning to this end and delivers a faster and more spectrally-efficient solution than state-of-the-art centralized techniques. An important aspect of our proposed solution is that it scales very well with the number of cells in the network. The effectiveness of the proposed algorithm is verified by simulation over millimeter-wave networks with two to seven cells. Interestingly, the penalty for not sharing CSI decreases as the number of cells increases. In particular, for a 7-cell network, the proposed algorithm without sharing CSI achieves 92% of the spectral efficiency obtained by sharing CSI.