Adaptive clustering of NOMA users in the power domain
Non-orthogonal multiple access, User clustering, Evolutionary algorithms
The technique of Non-Orthogonal Multiple Access (NOMA, Non-Orthogonal Multiple Access) is intended to enable the transmission of two or more users sharing the same resources of time, frequency, and code, and thus significantly improve the spectral efficiency of wireless communication networks in future generations. Signal multiplexing can be achieved in the Power Domain NOMA (PD-NOMA), where superposed signals are transmitted with sufficiently different power levels. The efficiency of this method fundamentally depends on two previous processing steps: an adequate clustering of users (transmission candidates) with different channel gains and the choice of power levels that will be used to transmit each signal. The solutions presented in the literature to solve the user clustering problem do not consider the dynamics of the communication systems, that is, the temporal variation of the number of users and the channel conditions. To consider these dynamic characteristics when grouping users in NOMA systems, this work proposes a new clustering technique based on a modification of the evolutionary algorithm DenStream, chosen for its evolutionary capacity, robustness to noise, and online processing. Results show that the proposed clustering technique follows the system's dynamics, clustering all users and favoring the uniformity of the transmission rate between the clusters.