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.
@article{arxiv.2106.14356,
title = {Collaborative Edge Learning in MIMO-NOMA Uplink Transmission Environment},
author = {Mian Guo and Chun Shan and Mithun Mukherjee and Jaime Lloret and Quansheng Guan},
journal= {arXiv preprint arXiv:2106.14356},
year = {2021}
}
Comments
5 pages, 3 figures, accepted for publication in IEEE/CIC ICCC 2021