Priority-Based Clustered Random Access Scheme for Efficient 5G mMTC and URLLC Performance

In the absence of efficient access management, the massive deployment of machine-type communication devices (MTCDs) in fifth-generation (5G) wireless networks can lead to congestion, increased access delay, and network overload. Notwithstanding the small amount of data generated by MTCDs during the random access procedure, the high level of signalling associated with massive deployments results in increased energy consumption. The coexistence of massive machine-type communication (mMTC) and ultra-reliable low-latency communication (URLLC) further complicates access resource allocation and latency guarantees. To maintain a balanced trade-off between access delay, energy efficiency, and resource contention, this paper proposes a priority-based clustered random access scheme (PCRAS) that satisfies the massive access demands of MTCDs while accounting for heterogeneous QoS requirements. The scheme reduces signalling overhead by aggregating mMTC traffic through clustering, while prioritising URLLC devices using dedicated preambles and power ramping. Analytical modelling employs M/G/1 queueing theory to evaluate the average access delay for mMTC traffic and Network Calculus to derive worst-case backlog and delay bounds for URLLC traffic. Simulation results in a 5G NR scenario with 20 clusters show that PCRAS attains a 12.24 ms minimum URLLC access delay at 200 devices and approximately 300 ms at 2000 devices, while sustaining ≈ 90% access success at 1200 devices and reducing the average energy per successful access compared with ACB. These results demonstrate that PCRAS improves the latency–energy–fairness trade-off under massive access while remaining backwards compatible with NR random access procedures.