  In this study, we propose a novel machine learning based algorithm to improve
the performance of beyond 5 generation (B5G) wireless communication system that
is assisted by Orthogonal Frequency Division Multiplexing (OFDM) and
Non-Orthogonal Multiple Access (NOMA) techniques. The non-linear soft margin
support vector machine (SVM) problem is used to provide an automatic modulation
classifier (AMC) and a signal power to noise and interference ratio (SINR)
estimator. The estimation results of AMC and SINR are used to reassign the
modulation type, codding rate, and transmit power through frames of eNode B
connections. The AMC success rate versus SINR, total power consuming, and sum
capacity are evaluated for OFDM-NOMA assisted 5G system. Results show
improvement of success rate compared of some published method. Furthermore, the
algorithm directly computes SINR after signal is detected by successive
interference cancellation (SIC) and before any signal decoding. Moreover,
because of the direct sense of physical channel, the presented algorithm can
discount occupied symbols (overhead signaling) for channel quality information
(CQI) in network communication signaling. The results also prove that the
proposed algorithm reduces the total power consumption and increases the sum
capacity through the eNode B connections. Simulation results in compare to
other algorithms show more successful AMC, efficient SINR estimator, easier
practical implantation, less overhead signaling, less power consumption, and
more capacity achievement.
