Learning-Based Link Scheduling in Millimeter-wave Multi-connectivity Scenarios
This addresses the problem of reliable and seamless connectivity for users in millimeter-wave networks, but it is incremental as it builds on existing multi-connectivity solutions.
The paper tackles the problem of inefficient link scheduling in millimeter-wave multi-connectivity networks, which can cause high interference, energy consumption, or unsatisfied QoS. The result shows that their learning-based solution approaches the optimum and outperforms baseline methods.
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 frequencies, connectivity with multiple cells can drastically increase the network performance in terms of throughput and reliability. However, an inefficient link scheduling, i.e., over and under-provisioning of connections, can lead either to high interference and energy consumption or to unsatisfied user's quality of service (QoS) requirements. In this work, we present a learning-based solution that is able to learn and then to predict the optimal link scheduling to satisfy users' QoS requirements while avoiding communication interruptions. Moreover, we compare the proposed approach with two base line methods and the genie-aided link scheduling that assumes perfect channel knowledge. We show that the learning-based solution approaches the optimum and outperforms the base line methods.