3 papers
cs.IT2021
Study of Robust Adaptive Beamforming Based on Low-Complexity DFT Spatial Sampling
Saeed Mohammadzadeh, Vitor H. Nascimento, Rodrigo C. de Lamare +1
In this paper, a novel and robust algorithm is proposed for adaptive beamforming based on the idea of reconstructing the autocorrelation sequence (ACS) of a random process from a s…
cs.IT2020
Low-Cost Maximum Entropy Covariance Matrix Reconstruction Algorithm for Robust Adaptive Beamforming
S. Mohammadzadeh, V. H. Nascimento, R. C. de Lamare
In this letter, we present a novel low-complexity adaptive beamforming technique using a stochastic gradient algorithm to avoid matrix inversions. The proposed method exploits algo…
cs.LG2020
Study of Energy-Efficient Distributed RLS-based Learning with Coarsely Quantized Signals
A. Danaee, R. C. de Lamare, V. H. Nascimento
In this work, we present an energy-efficient distributed learning framework using coarsely quantized signals for Internet of Things (IoT) networks. In particular, we develop a dist…