Reinforcement Learning-based Joint Power and Resource Allocation for URLLC in 5G

Next-generation wireless networks are moving rapidly towards supporting heterogeneous services that bring along several challenges in radio resource allocation. In this paper, we address the problem of multiplexing Ultra-Reliable Low-Latency Communication (URLLC) users and enhanced Mobile Broadband (eMBB) users on a shared channel of 5G New Radio (NR). We propose a joint power and resource allocation algorithm based on Q-learning. The proposed algorithm is crafted carefully to improve reliability and latency of URLLC users without hindering throughput of eMBB users. In particular, the algorithm rewards the actions that mitigate inter-cell interference as well as improve transmission and scheduling delays. We compare our results with a priority-based proportional fairness algorithm with fixed power allocation that relies on giving URLLC users priority in resource scheduling. Simulation results reveal that our algorithm is able to achieve 4% increase in reliability as well as lower latency results in high traffic load scenarios.