Research on Interference Management in Ultra–Dense Millimetre Wave Networks

Interference mitigation is critical in ultra-dense millimeter wave (mmWave) networks with directional transmissions. This paper develops a federated learning framework for distributed interference management. Edge devices collectively train a shared neural network model without exchanging local data samples. The model predicts optimal transmit beam directions that minimize interference. We propose communication-efficient mechanisms for model aggregation from many devices. Our simulation results using 3GPP channel models demonstrate fast convergence and prediction accuracy. The learned model adapts effectively to varying network topologies and mobility. Our federated learning approach enables scalable and data-efficient interference management in dense mmWave networks.