Joint Codebook and UE Selection for mmWave Multi-gNB Coordination Based on Dual-Encoder

To meet the growing demand for network throughput, millimeter-wave (mmWave) communication has emerged as a promising solution. However, the inherent directional transmission of mmWave introduces severe interference in multi-gNB networks. While Deep Reinforcement Learning (DRL)-based joint selection of User Equipments (UEs) and codebooks is widely used for network optimization, conventional Multi-layer Perceptron (MLP)-based models suffer from performance degradation when the number of gNBs and UEs varies. In this paper, we propose a Dual-Encoder Q-network (DEQ) model to capture the correlations among gNBs and UEs for joint UE and codebook selection. Furthermore, we implement a sequential selection process for each gNB to enhance inter-cell coordination. Simulation results demonstrate that the proposed DEQ significantly improves network throughput while ensuring UE fairness across both seen and unseen dense scenarios.