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

Yuang Chen, Fengqian Guo, Chang Wu, Mingyu Peng, Hancheng Lu, Chang Wen Chen

TL;DR

This work tackles ultra-low-latency, computation-intensive IoT tasks in a dense uplink setting by integrating NOMA with a multi-BS MEC framework. It introduces joint task offloading, user grouping, and power allocation, solved via an exact potential game for offloading/grouping and a majorization-minimization approach for power, within an alternating optimization loop. The proposed methodology yields notable improvements in total delay (up to 19.3%) and power consumption (up to 14.7%) over strong baselines, validating its effectiveness for scalable IoT deployments. The framework balances intra-group interference, inter-BS coordination, and heterogeneous device requirements, advancing practical QoS provisioning in edge computing for massive IoT.

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.

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

TL;DR

This work tackles ultra-low-latency, computation-intensive IoT tasks in a dense uplink setting by integrating NOMA with a multi-BS MEC framework. It introduces joint task offloading, user grouping, and power allocation, solved via an exact potential game for offloading/grouping and a majorization-minimization approach for power, within an alternating optimization loop. The proposed methodology yields notable improvements in total delay (up to 19.3%) and power consumption (up to 14.7%) over strong baselines, validating its effectiveness for scalable IoT deployments. The framework balances intra-group interference, inter-BS coordination, and heterogeneous device requirements, advancing practical QoS provisioning in edge computing for massive IoT.

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.
Paper Structure (21 sections, 3 theorems, 27 equations, 8 figures, 1 table, 3 algorithms)

This paper contains 21 sections, 3 theorems, 27 equations, 8 figures, 1 table, 3 algorithms.

Table of Contents

  1. Introduction
  2. Research Motivations and Challenges
  3. Main Contributions
  4. Related Works
  5. Research Status of NOMA-assisted MEC Networks Under DCSI IoT Applications
  6. Survey on Transmission and Computing Schemes in Multi-BS-MEC Architectures
  7. Key Limitations of These Research Works
  8. System Model
  9. Problem Formulation and Analysis
  10. Algorithm Designs and Solutions
  11. Potential Game-Based Joint Task Offloading and User Grouping Scheme
  12. Optimality and Computational Complexity Analysis
  13. Convex Optimization-Based Power Allocation Scheme
  14. AO-Based Joint Task Offloading, User Grouping, and Power Allocation Scheme
  15. Performance Evaluation
  16. ...and 6 more sections

Key Result

Theorem 1

Based on Definition def2, the closed-form expression of the $\Phi_{n}$ is derived by (eq14), which makes Eq. (eq12) established.

Figures (8)

  • Figure 1: The proposed Uplink NOMA-assisted multi-BS-MEC network for IoT access with DSCI tasks.
  • Figure 2: The convergence performance of the proposed joint task offloading, user grouping, and power allocation scheme under different numbers of IoT devices.
  • Figure 3: The distribution of IoT device locations after convergence of the proposed joint task offloading, user grouping, and power allocation scheme.
  • Figure 4: Total and average delay performance under different IoT device access scenarios.
  • Figure 5: Total and average power consumption performance under different IoT device access scenarios.
  • ...and 3 more figures

Theorems & Definitions (10)

  • Definition 1
  • Definition 2
  • Theorem 1
  • proof
  • Corollary 1
  • proof
  • Definition 3
  • Corollary 2
  • proof
  • Definition 4