Energy‐efficient semi‐supervised learning framework for subchannel allocation in non‐orthogonal multiple access systems

Abstract

AbstractNon‐orthogonal multiple access (NOMA) is considered a key candidate technology for next‐generation wireless communication systems due to its high spectral efficiency and massive connectivity. Incorporating the concepts of multiple‐input‐multiple‐output (MIMO) into NOMA can further improve the system efficiency, but the hardware complexity increases. This study develops an energy‐efficient (EE) subchannel assignment framework for MIMO‐NOMA systems under the quality‐of‐service and interference constraints. This framework handles an energy‐efficient co‐training‐based semi‐supervised learning (EE‐CSL) algorithm, which utilizes a small portion of existing labeled data generated by numerical iterative algorithms for training. To improve the learning performance of the proposed EE‐CSL, initial assignment is performed by a many‐to‐one matching (MOM) algorithm. The MOM algorithm helps achieve a low complex solution. Simulation results illustrate that a lower computational complexity of the EE‐CSL algorithm helps significantly minimize the energy consumption in a network. Furthermore, the sum rate of NOMA outperforms conventional orthogonal multiple access.