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Multi-connectivity is emerging as a promising solution to provide reliablecommunications and seamless connectivity for the millimeter-wave frequencyrange. Due to the blockage sensitivity at such high frequencies, connectivitywith multiple cells can drastically increase the network performance in termsof throughput and reliability. However, an inefficient link scheduling, i.e.,over and under-provisioning of connections, can lead either to highinterference and energy consumption or to unsatisfied user's quality of service(QoS) requirements. In this work, we present a learning-based solution that isable to learn and then to predict the optimal link scheduling to satisfy users'QoS requirements while avoiding communication interruptions. Moreover, wecompare the proposed approach with two base line methods and the genie-aidedlink scheduling that assumes perfect channel knowledge. We show that thelearning-based solution approaches the optimum and outperforms the base linemethods.

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