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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Maximum Entropy-Based Interference-Plus-Noise Covariance Matrix Reconstruction for Robust Adaptive Beamforming

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Author(s):
Mohammadzadeh, Saeed [1] ; Nascimento, Vitor H. [1] ; de Lamare, Rodrigo C. [2] ; Kukrer, Osman [3]
Total Authors: 4
Affiliation:
[1] Univ Sao Paulo, Dept Elect Syst Engn, BR-05508900 Sao Paulo - Brazil
[2] Pontificia Univ Catolica Rio de Janeiro, CETUC, BR-22451900 Rio de Janeiro, RJ - Brazil
[3] Eastern Mediterranean Univ, TR-99450 Famagusta - Turkey
Total Affiliations: 3
Document type: Journal article
Source: IEEE SIGNAL PROCESSING LETTERS; v. 27, p. 845-849, 2020.
Web of Science Citations: 0
Abstract

To ensure signal receiving quality, robust adaptive beamforming (RAB) is of vital importance in modern communications. In this letter, we propose a new low-complexity RAB approach based on interference-plus-noise covariance matrix (IPNC) reconstruction and steering vector (SV) estimation. In this method, the IPNC and desired signal covariance matrices are reconstructed by estimating all interference powers as well as the desired signal power using the principle of maximum entropy power spectrum (MEPS). Numerical simulations demonstrate that the proposed method can provide superior performance to several previously proposed beamformers. (AU)

FAPESP's process: 19/19387-9 - Signal processing techniques for beamforming and coding schemes in IoT communication systems
Grantee:Saeed Mohammadzadeh
Support Opportunities: Scholarships in Brazil - Post-Doctoral
FAPESP's process: 18/12579-7 - ELIOT: enabling technologies for IoT
Grantee:Vitor Heloiz Nascimento
Support Opportunities: Research Projects - Thematic Grants