From the 2 of 38 linked papers with an AI index.
4 papers · 1 filter
Rapid and Power-Aware Learned Optimization for Modular Receive Beamforming
Ohad Levy, Nir Shlezinger
Multiple-input multiple-output (MIMO) systems play a key role in wireless communication technologies. A widely considered approach to realize scalable MIMO systems involves archite…
Model-Based Machine Learning for Max-Min Fairness Beamforming Design in JCAS Systems
Mengyuan Ma, Tianyu Fang, Nir Shlezinger +3
Joint communications and sensing (JCAS) is expected to be a crucial technology for future wireless systems. This paper investigates beamforming design for a multi-user multi-target…
Deep Unfolding-Empowered MmWave Massive MIMO Joint Communications and Sensing
Nhan Thanh Nguyen, Ly V. Nguyen, Nir Shlezinger +3
In this paper, we propose a low-complexity and fast hybrid beamforming design for joint communications and sensing (JCAS) based on deep unfolding. We first derive closed-form expre…
Uncertainty-Aware and Reliable Neural MIMO Receivers via Modular Bayesian Deep Learning
Tomer Raviv, Sangwoo Park, Osvaldo Simeone +1
Deep learning is envisioned to play a key role in the design of future wireless receivers. A popular approach to design learning-aided receivers combines deep neural networks (DNNs…