Multi‐input fully CNN for joint pilot decontamination and symbol detection in 5G massive MIMO

This paper presents a multi‐input deep learning‐based joint pilot decontamination and symbol detection (SD) technique for 5G massive multiple‐input multiple‐output (MAMIMO) systems. It consists of a fully convolutional neural network (FCNN) that finds the 5G channel coefficients using pre‐known soun...

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Bibliographic Details
Published in:IET communications 2023-10, Vol.17 (16), p.1899-1906
Main Authors: Victor, Crallet M., Mvuma, Alloys N., Mrutu, Salehe I.
Format: Article
Language:English
Subjects:
5G massive MIMO
Antennas
Artificial neural networks
Bandwidths
channel estimation
Contamination
Decontamination
Deep learning
fully convolutional neural networks
Machine learning
Mean square errors
multi‐input deep neural networks
Neural networks
Orthogonal Frequency Division Multiplexing
Parameter estimation
pilot contamination
Pilots
Random variables
Reference signals
Sensors
Signal detection
Signal processing
symbol detection
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Summary:This paper presents a multi‐input deep learning‐based joint pilot decontamination and symbol detection (SD) technique for 5G massive multiple‐input multiple‐output (MAMIMO) systems. It consists of a fully convolutional neural network (FCNN) that finds the 5G channel coefficients using pre‐known sounding reference signals (SRSs) under pilot contamination and a symbol detector derived from projected gradient descent iterations. The study considers pilot contamination caused by inter‐cell interferences between SRSs during channel estimation (CE). The proposed scheme accepts entire 5G orthogonal frequency modulated (OFDM) data and least square estimates and produces the transmitted OFDM signal. Simulation experiments demonstrated that the proposed technique has better CE and SD performance with reduced trainable parameters. Moreover, it is faster due to the lowest elapsed time during end‐to‐end OFDM symbol detection. This paper proposes a joint pilot decontamination and signal detection for 5G MAMIMO systems. It achieves better detection performance with the lowest number of trainable parameters and memory requirements. It is applicable in 5G OFDM symbol detection and channel estimation under pilot contamination.
ISSN:1751-8628
1751-8636
DOI:10.1049/cmu2.12670