Data Sensing and Offloading in Edge Computing Networks: TDMA or NOMA?

TL;DR

This paper compares TDMA and NOMA for data offloading in edge computing, proposing algorithms for throughput maximization, and finds TDMA often outperforms NOMA under certain conditions.

Contribution

It introduces low-complexity algorithms for joint radio-and-computation resource allocation in multiuser offloading, analyzing their performance in edge computing networks.

Findings

TDMA with optimized sequence outperforms NOMA in throughput.

NOMA performs better with time-sharing and identical sensing capabilities.

Proposed algorithms are either optimal or near-optimal in simulations.

Abstract

With the development of Internet-of-Things (IoT), we witness the explosive growth in the number of devices with sensing, computing, and communication capabilities, along with a large amount of raw data generated at the network edge. Mobile (multi-access) edge computing (MEC), acquiring and processing data at network edge (like base station (BS)) via wireless links, has emerged as a promising technique for real-time applications. In this paper, we consider the scenario that multiple devices sense then offload data to an edge server/BS, and the offloading throughput maximization problems are studied by joint radio-and-computation resource allocation, based on time-division multiple access (TDMA) and non-orthogonal multiple access (NOMA) multiuser computation offloading. Particularly, we take the sequence of TDMA-based multiuser transmission/offloading into account. The studied problems are NP-hard and non-convex. A set of low-complexity algorithms are designed based on decomposition approach and exploration of valuable insights of problems. They are either optimal or can achieve close-to-optimal performance as shown by simulation. The comprehensive simulation results show that the sequence optimized TDMA scheme achieves better throughput performance than the NOMA scheme, while the NOMA scheme is better under the assumptions of time-sharing strategy and the identical sensing capability of the devices.