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

TL;DR

This paper compares the energy efficiency of NOMA, FDMA, and TDMA offloading schemes in UAV-enabled MEC systems under finite and infinite blocklengths, revealing TDMA's consistent advantage and nuanced NOMA performance.

Contribution

It provides a theoretical comparison of three multiple access schemes' energy consumption in UAV-MEC systems and proposes an optimization algorithm for energy minimization.

Findings

TDMA always consumes less energy than FDMA.

NOMA's energy efficiency is not always better than FDMA with finite blocklengths.

The proposed algorithm reduces energy consumption effectively.

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.