Adaptive Scheduling of Shared Grant-Free Resources for Heterogeneous Massive Machine Type Communication in 5G and Beyond Networks

Abstract

Massive machine-type communication (mMTC) has been identified as a key service type in fifth-generation new radio (5G NR) communication systems. The third-generation partnership (3GPP) project, starting with 5G, has introduced grant-free (GF) or configured grant (CG) scheduling for uplink traffic with small data packets to reduce signaling and latency overheads as compared to prevalent grant-based (GB) schemes. However, when heterogeneous MTC devices compete for pre-configured, shared GF resources, the access results in collisions. No standardized methods exist for ensuring priority-based access in the shared GF scheduling scheme. In this work, we introduce novel methods which utilize both heuristic and multi-objective deep reinforcement learning (DRL) techniques for priority-enabled GF access. The proposed methods adaptively partition GF bandwidth resources per allocation interval for scheduling configured grants to heterogeneous MTC device groups in a way which improves their probability of successful transmission, thereby resulting in a lower average age of information and packet drop rate and simultaneously ensuring fairness. Through extensive simulations set in the context of cyber-physical systems (CPS) with diverse quality-of-service (QoS) requirements across various 5G NR numerology schemes, we exemplify that our proposed approach provides significantly better performance and resource utilization than conventional schemes.