Online Learning-Based Downlink Transmission Coordination in Ultra-Dense Millimeter Wave Heterogeneous Networks

In heterogeneous ultra-dense networks with millimeter wave macro cells and small cells, base stations (BSs) and mobile user equipments (UEs) perform beamforming operations to establish highly directional links. In spite of the spatial diversity achieved through directional links, as a number of BSs are densely deployed, inter-cell interference caused by concurrent directional transmissions of adjacent BSs becomes severe, resulting in downlink performance degradation in the network. However, it is very difficult to manage inter-cell interference because of the nature of the time-varying wireless fading environment, the dynamic changes in beam propagation directivity, and unpredictable UEs' locations. In this paper, we propose an online learning-based transmission coordination algorithm based on the framework of multi-armed bandits to learn the unknown characteristics of inter-BS interference and exploit learned data to derive an optimal policy for maximizing the number of successful downlink transmissions. Through the numerical simulations, we verify the effectiveness of the proposed online learning-based inter-BS interference management scheme.