1 citations · 2 across the 15 of their papers we have counts for
9 papers · 1 filter
Optimization of Iterative Blind Detection based on Expectation Maximization and Belief Propagation
Luca Schmid, Tomer Raviv, Nir Shlezinger +1
We study iterative blind symbol detection for block-fading linear inter-symbol interference channels. Based on the factor graph framework, we design a joint channel estimation and…
Asynchronous Online Adaptation via Modular Drift Detection for Deep Receivers
Nicole Uzlaner, Tomer Raviv, Nir Shlezinger +1
Deep learning is envisioned to facilitate the operation of wireless receivers, with emerging architectures integrating deep neural networks (DNNs) with traditional modular receiver…
Information Compression in the AI Era: Recent Advances and Future Challenges
Jun Chen, Yong Fang, Ashish Khisti +3
This survey articles focuses on emerging connections between the fields of machine learning and data compression. While fundamental limits of classical (lossy) data compression are…
Rapid Optimization of Superposition Codes for Multi-Hop NOMA MANETs via Deep Unfolding
Tomer Alter, Nir Shlezinger
Various communication technologies are expected to utilize mobile ad hoc networks (MANETs). By combining MANETs with non-orthogonal multiple access (NOMA) communications, one can s…
Adaptive and Flexible Model-Based AI for Deep Receivers in Dynamic Channels
Tomer Raviv, Sangwoo Park, Osvaldo Simeone +2
Artificial intelligence (AI) is envisioned to play a key role in future wireless technologies, with deep neural networks (DNNs) enabling digital receivers to learn to operate in ch…
Deep Unfolding Hybrid Beamforming Designs for THz Massive MIMO Systems
Nhan Thanh Nguyen, Mengyuan Ma, Nir Shlezinger +3
Hybrid beamforming (HBF) is a key enabler for wideband terahertz (THz) massive multiple-input multiple-output (mMIMO) communications systems. A core challenge with designing HBF sy…