A Hybrid LSTM-ResNet Deep Neural Network for Noise Reduction and Classification of V-Band Receiver Signals

Homa Arab,Iman Ghaffari,Serioja Ovidiu Tatu,Steven Dufour,Romaric Mvone Evina

doi:10.1109/access.2022.3147980

Abstract

Noise reduction is one of the most important process used for signal processing in communication systems. The signal-to-noise ratio (SNR) is a key parameter to consider for minimizing the bit error rate (BER). The inherent noise found in millimeter-wave systems is mainly a combination of white noise and phase noise. Increasing the SNR in wireless data transfer systems can lead to reliability and performance improvements. To address this issue, we propose to use a recurrent neural network (RNN) with a long short-term memory (LSTM) autoencoder architecture to achieve signal noise reduction. This design is based on a composite LSTM autoencoder with a single encoder layer and two decoder layers. A V-band receiver test bench is designed and fabricated to provide a high-speed wireless communication system. Constellation diagrams display the output signals measured for various random sequences of PSK and QAM modulated signals. The LSTM autoencoder is trained in real time using various noisy signals. The trained system is then used to reduce noise levels in the tested signals. The SNR of the designed receiver is of the order of 11.8dB, and it increases to 13.66dB using the three-level LSTM autoencoder. Consequently, the proposed algorithm reduces the bit error rate from 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−8</sup> to 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−11</sup> . The performance of the proposed algorithm is comparable to other noise reduction strategies. Augmented denoised signals are fed into a ResNet-152 deep convolutional network to perform the final classification. The demodulation types are classified with an accuracy of 99.93%. This is confirmed by experimental measurements.

Highlights

Amplitude and phase noise reduction is important in all fields of signal processing, including RF and microwave communications, and data analysis
Most noise reduction algorithms applied to RF signals are based on a time-frequency representation of the input, and on digital denoising techniques, such as the short-time Fourier transform (STFT), the singular value decomposition (SVD), and the fast wavelet transform (FWT) [1]
The experimental results show that the combination of the augmentation technique, residual network (ResNet)-152, and an long short-term memory (LSTM) network has achieved an accuracy of 99.93%

Summary

INTRODUCTION

Amplitude and phase noise reduction is important in all fields of signal processing, including RF and microwave communications, and data analysis. The proposed approach is based on deep learning techniques, which gained popularity in recent years in the communication industry, mainly to cancel noise distortion in receiver signals. They are powerful on-time methods which apply to both the phase and the amplitude noise. The experimental results were obtained from a V-band six-port based receiver designed for millimeter-wave wireless communications. This six-port technology, which was developed for direct-conversion radio receivers, has been studied in many applications [10], [11].

EXPERIMENTAL SETUP

Attenuator Phase shifter

RECURRENT NEURAL NETWORKS WITH LONG SHORT-TERM MEMORY

RESNET CONVOLUTIONAL NETWORKS

Findings

CONCLUSION