Deep Learning Network for Multiuser Detection in Satellite Mobile Communication System.

Guan Qing Yang,Wu Shuang,He Ya-Ru

doi:10.1155/2019/8613639

Abstract

A multiuser detection (MUD) algorithm based on deep learning network is proposed for the satellite mobile communication system. Due to relative motion between the satellite and users, multiple access interference (MUI) introduced by multipath fading channel reduces system performance. The proposed MUD algorithm based on deep learning network firstly establishes the CINR optimal loss function according to the multiuser access mode and then obtains the best multiuser detection weight through the steepest gradient iteration. Multilayer nonlinear learning obtains interference cancellation sharing weights to achieve maximum signal-to-noise ratio through gradient iteration, which is superior than the traditional serial interference cancellation algorithm and parallel interference cancellation algorithm. Then, the weights with multiuser detection through multilayer network forward learning iteration are obtained with traditional multiuser detecting quality characteristics. The proposed multiuser access detection based on deep learning network algorithm improves the MUD accuracy and reduces the number of traditional multiusers. The performance of the satellite multifading uplink system shows that the proposed deep learning network can provide high precision and better iteration times.

Highlights

Due to high-speed relative motion between mobile users and satellites in the satellite mobile communication system, different users access with the satellite at different elevation angles and multipath channel between satellite and user links is fading. ese factors are creating obstacles for multiuser detection
The user has access to a maximum height at 35°, 25°, 5°, and 15°. e signal bandwidth is 50 MHz, the number of subcarriers is 1024, and the signal mapping method is defined as QPSK, BPSK, 16QAM, OFDM signal
Compared with the iterative sorting least squares (IORLS) algorithm proposed in [12] and the orthogonal signal tracking (OMP) algorithm proposed in [13], the WIC algorithm is improved by two iterations for sharing weight. e simulation results can be obtained from Figure 7

Summary

Introduction

Due to high-speed relative motion between mobile users and satellites in the satellite mobile communication system, different users access with the satellite at different elevation angles and multipath channel between satellite and user links is fading. ese factors are creating obstacles for multiuser detection. In the literature [2, 3], a soft iterative method was proposed for multiuser signal detection, but soft iteration required too much user information and it was not easy to achieve convergence. Tang and Heath [4] proposed a joint MUD scheme for MIMO On this basis, in [5], CP was used to perform multiuser detection by accurately estimating the frequency offset. Durand et al [29] proposed the SIC algorithm based on weighting to detect multiuser in LTE-A systems, which is to improve the signal-to-interference ratio SINR. For multilayer neural network fusion decision, Wei et al [35] proposed a weight-based fuzzy decision algorithm to achieve emotion recognition.

System Model and Problem Formulation

Experimental Classification Results and Analysis

Conclusion