Sparse Array Design for Optimum Beamforming Using Deep Learning

This article considers sparse array design for receive beamforming achieving maximum signal-to-interference plus noise ratio (MaxSINR). We develop a design approach based on supervised neural network where class labels are generated using an efficient sparse beamformer spectral analysis (SBSA) appro...

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Bibliographic Details
Published in:IEEE transactions on aerospace and electronic systems 2024-02, Vol.60 (1), p.133-144
Main Authors: Hamza, Syed A., Amin, Moeness G.
Format: Article
Language:English
Subjects:
Array signal processing
Arrays
Artificial neural networks
Beamforming
Classification
Configurations
Deep neural network (DNN)
Interference
Machine learning
maximum signal-to-interference plus noise ratio (MaxSINR)
Narrowband
Neural networks
Optimization
Performance evaluation
Sensor arrays
Sensors
Signal processing algorithms
Signal to noise ratio
sparse arrays
Spectrum analysis
Training
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Summary:This article considers sparse array design for receive beamforming achieving maximum signal-to-interference plus noise ratio (MaxSINR). We develop a design approach based on supervised neural network where class labels are generated using an efficient sparse beamformer spectral analysis (SBSA) approach. SBSA uses explicit information of the unknown narrowband interference environment for training the network and bears close performance to training using exhaustive search by enumerations which is computationally prohibitive for large arrays. The employed deep neural network (DNN) effectively approximates the unknown mapping from the input received data spatial correlations to the output of sparse configuration with effective interference mitigation capability. The problem is posed as a classification problem where the sparse array configuration achieving MaxSINR is one-hot encoded, and indicated by the output layer of DNN. We evaluate the performance of the DNN in terms of the sparse array classification accuracy as well as in terms of the ability of the classified sparse array to mitigate interference and maximize signal power. It is shown that the DNN effectively learns the optimal sparse configuration which has desirable SINR characteristics, hence paving the way for efficient real-time implementation.
ISSN:0018-9251
1557-9603
DOI:10.1109/TAES.2023.3285201