Deep learning based successive interference cancellation scheme in nonorthogonal multiple access downlink network - Sim, Isaac; Sun, Young Ghyu; Lee, Donggu; Kim, Soo Hyun; Lee, Jiyoung; Kim, Jae Hyun; Shin, Yoan; Kim, Jin Young (Energies, 2020)
In this paper, a deep learning-based successive interference cancellation (SIC) scheme for use in nonorthogonal multiple access (NOMA) communication systems is investigated. NOMA has become a notable technique in the field of mobile wireless communication because of its capacity to overcome orthogonality, unlike a conventional orthogonal frequency division multiple access (OFDMA) communication system. In NOMA communication systems, SIC is one of the decoding schemes applied at receivers for downlink NOMA transmissions. In this paper, a convolutional neural network (CNN)-based SIC scheme is proposed to improve performance of the single base station and multiuser NOMA scheme. In contrast to existing SIC schemes, the proposed CNN-based SIC scheme can effectively mitigate losses resulting from imperfections of the SIC. The simulation results indicate that the CNN-based SIC method can successfully relieve conventional SIC impairments and achieve good detection performance. Consequently, a CNN-based SIC scheme can be considered as a potential technique for use in NOMA detection schemes.
Keywords: Detection performance; Improve performance; Mobile wireless communications; Multiple access downlink; Orthogonal frequency division multiple access; Potential techniques; Successive interference cancellation(SIC); Successive interference cancellations; Convolutional neural network (CNN); Deep learning; Imperfect SIC; Nonorthogonal multiple access (NOMA); Successive interference cancellation (SIC)