Collaborative Edge Learning in MIMO-NOMA Uplink Transmission Environment

TL;DR

This paper introduces DACEL, a delay-aware collaborative edge learning framework for MIMO-NOMA uplink environments, optimizing data transmission and reducing learning delay through a novel channel allocation algorithm.

Contribution

It proposes a new collaborative edge learning framework, DACEL, with a delay analysis and a channel allocation algorithm tailored for MIMO-NOMA uplink systems.

Findings

The proposed algorithm reduces learning delay compared to baseline schemes.

DACEL effectively coordinates data sources, edge server, and base station for low-latency learning.

Simulation results validate the delay performance improvements of the proposed method.

Abstract

Multiple-input multiple-output non-orthogonal multiple access (MIMO-NOMA) cellular network is promising for supporting massive connectivity. This paper exploits low-latency machine learning in the MIMO-NOMA uplink transmission environment, where a substantial amount of data must be uploaded from multiple data sources to a one-hop away edge server for machine learning. A delay-aware edge learning framework with the collaboration of data sources, the edge server, and the base station, referred to as DACEL, is proposed. Based on the delay analysis of DACEL, a NOMA channel allocation algorithm is further designed to minimize the learning delay. The simulation results show that the proposed algorithm outperforms the baseline schemes in terms of learning delay reduction.