… NOMA allows more than one user to access the same time-… deep learning to power allocation
to reduce the effect of imperfect SIC under energy-efficient perspective for downlink NOMA …

… , we propose an efficient deep learning-based approach to … typical class of deep learning
model, namely, deep belief network (… existing MC-NOMA with SCMA, SC-NOMA and OFDMA in …

… decoding order by leveraging deep learning to training through … In Section IV, we introduce
deep learning to approximate the … ) is employed at node S, and S multicasts the following SC …

… -NOMA). With the extensive simulations over Rayleigh fading channels, we reveal that the
DLDet outperforms both C-NOMA and TBS-C-NOMA … C-NOMA and TBS-C-NOMA, respectively…

… the block error rate (BLER) of the NOMA users, goodput, energy efficiency, latency… NOMA
users by optimizing power allocation coefficients. In addition, a novel multi-output deeplearning …

… , the multiuser signals are multiplied using the superposition coding (SC) in the transmitter
which is further transmitted to the end users employing multiple power levels based on …

… proposed deep learning-based resource optimization algorithm. In our simulation, a NOMA-…
to energy-efficient power allocation scheme for 5 G SC-NOMA system with imperfect SIC,” in …

… on SC-NOMA systems based on SWIPT have been examined in [182,183]. In particular, the
work in [182] explored the SWIPT in cooperative MISO SC-NOMA … -based SC-NOMA system, …

… Thus, in this section, we propose the deep learning-based power allocation algorithm as
a solution of the problem, where the user association and subchannel indicators, X and S, …

… The existing NOMA transceiver uses Superposition Coding (SC) for multiplexing different
user signals and follows conventional SIC for decoding users signals, respectively. SIC per…