From the 2 of 52 linked papers with an AI index.
6 papers · 2 filters
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…
Modular Hypernetworks for Scalable and Adaptive Deep MIMO Receivers
Tomer Raviv, Nir Shlezinger
Deep neural networks (DNNs) were shown to facilitate the operation of uplink multiple-input multiple-output (MIMO) receivers, with emerging architectures augmenting modules of clas…
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…