Deep Learning Based Successive Interference Cancellation Scheme in Nonorthogonal Multiple Access Downlink Network

Isaac Sim,Jin Young Kim,Yoan Shin,Jae-Hyun Kim,Donggu Lee,Soo Hyun Kim,Young Ghyu Sun,Jiyoung Lee

doi:10.3390/en13236237

Isaac Sim, Jin Young Kim + Show 6 more

Open Access

https://doi.org/10.3390/en13236237

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Journal: Energies	Publication Date: Nov 26, 2020
Citations: 12	License type: CC BY 4.0

Affiliation: Kwangwoon University, Soongsil University, Ajou University

Abstract

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.

Highlights

Since the nonorthogonal multiple access (NOMA) system was proposed, related issues have been actively studied
NOMA, the transmitted signal from the base station (BS) is a multiplexed superposition signal with power allocation which depends on channel state information (CSI)
The convolutional neural network (CNN) algorithm is a promising candidate in that it can effectively extract the characteristics of the input signal by processing signals through convolutional layers

Summary

Introduction

Since the nonorthogonal multiple access (NOMA) system was proposed, related issues have been actively studied. Tuan et al [10] These studies proposed optimization methods that involve applying a deep learning scheme when SIC decodes on UE and the BS knows the perfect CSI of each UE. In the imperfect SIC of NOMA schemes, the error propagation of the SIC scheme is one of the critical issues To solve this problem, the convolutional neural network (CNN) algorithm is a promising candidate in that it can effectively extract the characteristics of the input signal by processing signals through convolutional layers. A CNN-based SIC scheme is proposed for mitigation of the decoding loss caused by imperfect SIC. The proposed SIC scheme can mitigate the losses caused by imperfect SIC and improve the sum rate of the decoded signal.

System Model

Imperfections of Conventional SIC

CNN-Based

Simulation Results

Conclusions