  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.
