Learning-Based Link Scheduling in Millimeter-wave Multi-connectivity Scenarios

Multi-connectivity is emerging as a promising solution to provide reliable communications and seamless connectivity for the millimeter-wave frequency range. Due to the blockage sensitivity at such high frequency range, coordination among the network elements and connectivity with multiple cells can drastically increase the network performance in terms of throughput and reliability. Inefficient link scheduling can lead either to high interference and energy consumption or to unsatisfied user QoS requirements. In this work, we present a learning-based solution that is able to learn and then to predict the optimal link scheduling for several types of users and quality of service requirements. Moreover, we compare the proposed approach with two base line methods and the optimal link scheduling that assumes perfect channel knowledge. We show that the learning-based solution approaches the optimum and outperforms the two considered base line methods.