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

2025-04-20

AI Summary

To address the user admission control challenge under heterogeneous QoS requirements (e.g., enhanced ultra-reliable low-latency communication, eURLLC, and massive machine-type communication, mMTC) in RIS-aided multi-access edge computing (MEC) scenarios for 6G networks, this paper proposes a spatial-resource joint-aware admission mechanism. The method innovatively integrates the spatial reflection characteristics of reconfigurable intelligent surfaces (RIS) with MEC computational constraints, formulating a multi-objective utility function that jointly optimizes QoS guarantees, RIS reflection efficiency, and MEC load balancing. It further introduces angle-aligned filtering, dynamic priority queuing, and RIS resource-aware user grouping. Simulation results under full-load conditions show that the admission rate for high-priority eURLLC services exceeds 90%; compared to baseline schemes, the proposed approach achieves significant improvements in overall admission rate and QoS satisfaction ratio, validating the critical gains of RIS enhancement for intelligent edge access.

Technology Category

• Multiagent Systems: Mechanism Design
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

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.

Problem

• Efficient admission control in RIS-enhanced 6G MEC networks
• Optimizing user admission via spatial alignment and priority queues
• Balancing QoS, RIS utilization, and MEC efficiency

Innovation

• Optimizes user admission using RIS spatial characteristics
• Prioritizes users via queues based on service needs
• Maximizes RIS resource use with efficient grouping