An Adaptive QoS-Aware Priority Scheduling Solution for Dynamic Radio Resource Allocation in Multi-Service 5G Network Slicing Environments

Abid Yaqoob, Gabriel-Miro Muntean

Abstract: 5G Radio Access Network (RAN) slicing provides support for resource isolation and dynamic resource optimization to accommodate the diverse Quality of Service (QoS) needs across Internet of Things (IoT) ecosystems, i.e., enhanced Mobile Broadband (eMBB), Ultra-Reliable Low-Latency Communications (URLLC), and massive Machine Type Communication (mMTC) or Best Effort (BE) services. However, existing optimization- and data-driven radio resource allocation (RRA) models often suffer from rigid/suboptimal resource distribution and increased Service Level Agreement (SLA) violations in dynamic, multi-service environments. This paper introduces an Adaptive QoS-aware Priority Scheduling (AQPS) solution, a novel RRA approach designed to optimize resource distribution for multi-service 5G network slicing deployments in IoT-enabled infrastructures. To optimally allocate resource block groups (RBGs) according to time-varying traffic patterns and IoT network conditions, we formulate an NP-hard optimization problem with the objective of minimizing SLA violations while satisfying QoS requirements for diverse IoT service classes. In this context, AQPS optimizes resource distribution for IoT services by incorporating: (i) minimum guarantee allocation (QoS-driven RBG estimation), (ii) weighted urgency-based resource distribution (computing user urgency by incorporating buffer state, QoS factors, spectral efficiency, and service priorities), and (iii) priority-based round-robin allocation. Extensive trace-driven simulation findings from two comprehensive multiuser IoT-dense urban and enhanced IoT coverage zone scenarios with realistic channel conditions demonstrate that the AQPS solution experiences a very low number of SLA violations while enabling SLA assurance rates of 99.07% and 97.58%, respectively, when compared against state-of-the-art benchmarks, including Deep Reinforcement Learning (DRL), Round Robin (RR), and Stepwise Optimal Algorithm (SOA).

External IDs:doi:10.1109/jiot.2026.3652917