Energy-Efficient UAV-Enabled MEC Systems: NOMA, FDMA, or TDMA Offloading?

2025-10-25

AI Summary

This study investigates energy-efficient task offloading in unmanned aerial vehicle-assisted mobile edge computing (UAV-MEC) systems, comparing NOMA, FDMA, and TDMA under both infinite and finite blocklength (short-packet) communication constraints. Method: We formulate a joint optimization problem—simultaneously optimizing offloading ratios, time-slot allocation, and UAV 3D placement—to maximize system energy efficiency, and propose an alternating optimization algorithm supported by theoretical modeling and extensive simulations. Contribution/Results: (1) TDMA consistently outperforms FDMA across all considered scenarios; (2) under finite blocklength, NOMA is not universally superior—its energy efficiency falls below that of FDMA when user channels and task sizes are symmetric; (3) we reveal fundamental differences in energy consumption characteristics among multiple access schemes under short-packet constraints. The proposed framework significantly reduces total system energy consumption, providing both theoretical foundations and a practical optimization paradigm for low-power UAV-MEC deployment.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Multiagent Systems: Mechanism Design
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

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

Abstract

Unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) systems can use different multiple access schemes to coordinate multi-user task offloading. However, it is still unknown which scheme is the most energy-efficient, especially when the offloading blocklength is finite. To answer this question, this paper minimizes and compares the MEC-related energy consumption of non-orthogonal multiple access (NOMA), frequency division multiple access (FDMA), and time division multiple access (TDMA)-based offloading schemes within UAV-enabled MEC systems, considering both infinite and finite blocklength scenarios. Through theoretically analysis of the minimum energy consumption required by these three schemes, two novel findings are presented. First, TDMA consistently achieves lower energy consumption than FDMA in both infinite and finite blocklength cases, due to the degrees of freedom afforded by sequential task offloading. Second, NOMA does not necessarily achieve lower energy consumption than FDMA when the offloading blocklength is finite, especially when the channel conditions and the offloaded task data sizes of two user equipments (UEs) are relatively symmetric. Furthermore, an alternating optimization algorithm that jointly optimizes the portions of task offloaded, the offloading times of all UEs, and the UAV location is proposed to solve the formulated energy consumption minimization problems. Simulation results verify the correctness of our analytical findings and demonstrate that the proposed algorithm effectively reduces MEC-related energy consumption compared to benchmark schemes that do not optimize task offloading portions and/or offloading times.

Problem

• Compares energy efficiency of NOMA, FDMA, and TDMA schemes
• Determines optimal offloading method for UAV-enabled MEC systems
• Minimizes energy consumption considering finite blocklength constraints

Innovation

• Compares NOMA, FDMA, TDMA energy efficiency in UAV-MEC
• Proposes alternating optimization for task offloading and UAV location
• Analyzes energy consumption under finite blocklength constraints