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Collaborative Edge Learning in MIMO-NOMA Uplink Transmission Environment
- Publication Year :
- 2021
-
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.<br />Comment: 5 pages, 3 figures, accepted for publication in IEEE/CIC ICCC 2021
Details
- Database :
- arXiv
- Publication Type :
- Report
- Accession number :
- edsarx.2106.14356
- Document Type :
- Working Paper