NOMA-Assisted Multi-BS MEC Networks for Delay-Sensitive and Computation-Intensive IoT Applications

2026-02-07

AI Summary

This study addresses the challenges of ultra-low latency and resource constraints—such as uneven subchannel access, inter-group interference, load imbalance, and device heterogeneity—in executing computation-intensive tasks within large-scale Internet-of-Things (IoT) networks. To tackle these issues, the authors develop an uplink non-orthogonal multiple access (NOMA)-assisted multi-base-station mobile edge computing (MEC) framework and jointly optimize task offloading, user grouping, and power allocation to minimize total system latency. They propose a joint decision-making algorithm based on exact potential games (EPG-JDM) to achieve Nash equilibrium and integrate a majorization–minimization (MM) approach to design an efficient power allocation strategy that effectively decouples the multidimensional coupled optimization problem. Simulation results demonstrate that the proposed scheme outperforms state-of-the-art methods by reducing total system latency and power consumption by 19.3% and 14.7%, respectively.

Technology Category

• Multiagent Systems: Mechanism Design
• Machine Learning: Learning on the Edge & Model Compression
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

The burgeoning and ubiquitous deployment of the Internet of Things (IoT) landscape struggles with ultra-low latency demands for computation-intensive tasks in massive connectivity scenarios. In this paper, we propose an innovative uplink non-orthogonal multiple access (NOMA)-assisted multi-base station (BS) mobile edge computing (BS-MEC) network tailored for massive IoT connectivity. To fulfill the quality-of-service (QoS) requirements of delay-sensitive and computation-intensive IoT applications, we formulate a joint task offloading, user grouping, and power allocation optimization problem with the overarching objective of minimizing the system's total delay, aiming to address issues of unbalanced subchannel access, inter-group interference, computational load disparities, and device heterogeneity. To effectively tackle this problem, we first reformulate task offloading and user grouping into a non-cooperative game model and propose an exact potential game-based joint decision-making (EPG-JDM) algorithm, which dynamically selects optimal task offloading and subchannel access decisions for each IoT device based on its channel conditions, thereby achieving the Nash Equilibrium. Then, we propose a majorization-minimization (MM)-based power allocation algorithm, which transforms the original subproblem into a tractable convex optimization paradigm. Extensive simulation experiments demonstrate that our proposed EPG-JDM algorithm significantly outperforms state-of-the-art decision-making algorithms and classic heuristic algorithms, yielding performance improvements of up to 19.3% and 14.7% in terms of total delay and power consumption, respectively.

Problem

• NOMA
• MEC
• IoT
• delay-sensitive
• computation-intensive

Innovation

• NOMA
• Mobile Edge Computing
• Task Offloading
• Potential Game
• Power Allocation