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

Autor(es)

Mohammadzadeh, Saeed ^[1] ; Nascimento, Vitor H. ^[1] ; de Lamare, Rodrigo C. ^[2] ; Kukrer, Osman ^[3]

Afiliação do(s) autor(es)

^[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

• Tipo de documento: Artigo Científico
• Fonte: IEEE SIGNAL PROCESSING LETTERS; v. 27, p. 845-849, 2020.
• Citações Web of Science: 0

Resumo

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)

• Processo FAPESP: 19/19387-9 - Técnicas de processamento de sinais em beamforming e esquemas de codificação em sistemas de comunicações de IoT
• Beneficiário: Saeed Mohammadzadeh
• Modalidade de apoio: Bolsas no Brasil - Pós-Doutorado
• Processo FAPESP: 18/12579-7 - Tecnologias habilitadores para a Internet das Coisas
• Beneficiário: Vitor Heloiz Nascimento
• Modalidade de apoio: Auxílio à Pesquisa - Temático