Admission Control with Reconfigurable Intelligent Surfaces for 6G Mobile Edge Computing

TL;DR

The paper tackles admission control in 6G RIS-enhanced MEC networks with heterogeneous QoS requirements. It proposes a utility-maximization framework that jointly optimizes admission decisions, RIS element allocation, and MEC resources, encapsulated by the decision variables $x_i$, $z_i$, and $r_i$ in the objective $\max_{\{x_i,z_i,r_i\}} \sum_i (\gamma_1 P_i x_i U_i + \gamma_2 R_{RIS} - \gamma_3 \text{Penalty})$. The contributions include a three-phase algorithm (angular filtering, priority-based grouping, RIS resource allocation) and a formal optimization that balances user QoS, RIS utilization, and fairness penalties, validated by simulations. Results show RIS-enhanced configurations significantly improve admission rates and latency, especially in congested sectors and for high-priority eURLLC traffic, demonstrating the practical impact of jointly considering RIS, MEC, and user priorities in admission decisions.

Abstract

As 6G networks must support diverse applications with heterogeneous quality-of-service requirements, efficient allocation of limited network resources becomes important. This paper addresses the critical challenge of user admission control in 6G networks enhanced by Reconfigurable Intelligent Surfaces (RIS) and Mobile Edge Computing (MEC). We propose an optimization framework that leverages RIS technology to enhance user admission based on spatial characteristics, priority levels, and resource constraints. Our approach first filters users based on angular alignment with RIS reflection directions, then constructs priority queues considering service requirements and arrival times, and finally performs user grouping to maximize RIS resource utilization. The proposed algorithm incorporates a utility function that balances Quality of Service (QoS) performance, RIS utilization, and MEC efficiency in admission decisions. Simulation results demonstrate that our approach significantly improves system performance with RIS-enhanced configurations. For high-priority eURLLC services, our method maintains over 90% admission rates even at maximum load, ensuring mission-critical applications receive guaranteed service quality.

Figures (4)

• Figure 1: Admission rate comparison
• Figure 2: Two sectors comparison
• Figure 3: Three sectors comparison
• Figure 4: Latency comparison