NDLI: DNN-based interference mitigation beamformer

Content Provider	IET Digital Library
Author	Ramezanpour, Parham Mosavi, Mohammad Reza
Abstract	In classical beamforming techniques such as minimum mean squared error and minimum variance distortion-less response, for computing optimum weight vector of an antenna array, direction of arrival (DoA) of desired signal should be known as prior knowledge. In this study, a deep neural-network-based beamformer is proposed for estimating the desired signal in presence of noise and interference without requiring prior knowledge on the DoA of the desired signal. In the proposed beamformer, a bidirectional long short-term memory (bi-LSTM) is employed for estimating samples of interferences. On the other hand, samples of the desired signal are estimated by either another bi-LSTM or a convolutional neural network. The signal to interference and noise ratio (SINR) at the output of the proposed beamformer is 10 dB higher than the SINR at the output of the classical beamformers when the number of available snapshots is as low as 100. The proposed beamformer has promising performance when the input interference to signal ratio is as high as 34 dB and the input signal-to-noise ratio is as low as −10 dB.
Starting Page	1788
Ending Page	1794
Page Count	7
ISSN	17518784
Volume Number	14
e-ISSN	17518792
Issue Number	Issue 11, Nov (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-rsn/14/11
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-rsn.2020.0234
Journal	IET Radar, Sonar & Navigation
Publisher Date	2020-08-07
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Antenna Array Antenna Array Signal Processing Array Signal Processing Beamforming Communication Computing Complex Weight Vector Covariance Matrices Deep-neural-network-based Beamformer Digital Signal Processing Direction of Arrival Estimation Directional Gain Pattern DNN-based Interference Mitigation Beamformer DoA Electromagnetic Compatibility High-directional Gain Input Signal-to-noise Ratio Interference Interference Estimator Interference Suppression Interpolation And Function Approximation Least Mean Squares Method Linear Algebra Minimum Mean Squared Error Minimum Variance Distortion Neural Beamformer Neural Computing Technique Neural Nets Noise Figure -10.0 DB Noise Figure 10.0 DB Noise Figure 34.0 DB Numerical Analysis Optimum Weight Vector Output Signal Radio Frequency Interference Received Signal Vector Signal Estimator Signal Processing And Detection Telecommunication Computing
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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