1 citations · 1 across the 2 of their papers we have counts for
2 papers
eess.SP2022
Beyond Codebook-Based Analog Beamforming at mmWave: Compressed Sensing and Machine Learning Methods
Hamed Pezeshki, Fabio Valerio Massoli, Arash Behboodi +6
Analog beamforming is the predominant approach for millimeter wave (mmWave) communication given its favorable characteristics for limited-resource devices. In this work, we aim at…
eess.SP2022★ 1 cited
URGLQ: An Efficient Covariance Matrix Reconstruction Method for Robust Adaptive Beamforming
Tao Luo, Peng Chen, Zhenxin Cao +2
The computational complexity of the conventional adaptive beamformer is relatively large, and the performance degrades significantly due to the model mismatch errors and the unwant…