A Novel Signal Detection Scheme Based on Adaptive Ensemble Deep Learning Algorithm in SC-FDE Systems

Yuanjian Qiao,Tongliang Xin,Bo He,Jun Li,Wenxin Li

doi:10.1109/access.2020.3006265

Abstract

Reliable signal detection plays an essential role in enhancing the quality of signal transmission in wireless communication systems. In this paper, we combine signal detection theory with a deep learning model and propose a novel signal detection scheme based on adaptive ensemble long short term memory (AE-LSTM) neural network to handle wireless single carrier frequency domain equalization (SC-FDE) systems in an end-to-end manner. The feature information used for offline training of the deep learning model is extracted from the received signal containing channel state information (CSI) after the multi-path channel and fast Fourier transform (FFT), and the labels are assigned according to the constellation map adopted at the transmitter. To improve the adaptability of the system, we utilize the received power under different delays as the adaptive factor to integrate the output of each sub-network. Then the original data generated by the channel model is recovered by using the trained model instead of channel estimation and frequency domain equalization. Comparative experiments on SC-FDE symbol detection demonstrate that the proposed scheme achieves better performance in terms of reliability than the traditional scheme and the similar deep learning scheme.

Highlights

With the development of mobile Internet, 5G technology has become a hot topic in the communication industry and academia [1]
We propose a novel scheme for signal detection based on the adaptive ensemble LSTM (AE-LSTM) neural network, which extracts features from the received signal of the Single carrier frequency domain equalization (SC-FDE) system and assigns label according to the constellation map adopted at the transmitter
To reduce the complexity and obtain a more reliable signal detection performance, we propose a novel signal detection scheme based on adaptive ensemble long short term memory (AE-LSTM) deep learning model in the SC-FDE system

Summary

INTRODUCTION

With the development of mobile Internet, 5G technology has become a hot topic in the communication industry and academia [1]. The purpose of model training is to learn the channel characteristic information and convert the values of neurons in the output layer to constellation points by continually updating the network parameters. In the online test stage, using the trained deep learning model instead of channel estimation and frequency domain equalization process to achieve signal detection. B. ADAPTIVE ENSEMBLE ALGORITHM Figure 3 shows the signal detection scheme based on the AE-LSTM deep learning model. In the adaptive ensemble model, the network continuously adjusts the value of adaptive coefficient according to the amount of the received power in different multi-paths and realizes the adaptive ensemble of detection results of subnetworks through comprehensive analysis, which enhances the reliability of signal detection effectively and the adaptability of SC-FDE system. The complexity of the network increases with the number of sub-carriers and the constellation size, this conclusion is only valid in the offline training stage, such as the extension of training time, which has little impact on the online testing stage

EXPERIMENT AND ANALYSIS

CONCLUSION