803 citations · 3.5k across the 244 of their papers we have counts for
12 papers · 2 filters
Learning to Broadcast for Ultra-Reliable Communication with Differential Quality of Service via the Conditional Value at Risk
Roy Karasik, Osvaldo Simeone, Hyeryung Jang +1
Broadcast/multicast communication systems are typically designed to optimize the outage rate criterion, which neglects the performance of the fraction of clients with the worst cha…
An Information-Theoretic Analysis of The Cost of Decentralization for Learning and Inference Under Privacy Constraints
Sharu Theresa Jose, Osvaldo Simeone
In vertical federated learning (FL), the features of a data sample are distributed across multiple agents. As such, inter-agent collaboration can be beneficial not only during the…
Predicting Flat-Fading Channels via Meta-Learned Closed-Form Linear Filters and Equilibrium Propagation
Sangwoo Park, Osvaldo Simeone
Predicting fading channels is a classical problem with a vast array of applications, including as an enabler of artificial intelligence (AI)-based proactive resource allocation for…
Wireless Federated Langevin Monte Carlo: Repurposing Channel Noise for Bayesian Sampling and Privacy
Dongzhu Liu, Osvaldo Simeone
Most works on federated learning (FL) focus on the most common frequentist formulation of learning whereby the goal is minimizing the global empirical loss. Frequentist learning, h…
Prediction of mmWave/THz Link Blockages through Meta-Learning and Recurrent Neural Networks
Anders E. Kalør, Osvaldo Simeone, Petar Popovski
Wireless applications that use high-reliability low-latency links depend critically on the capability of the system to predict link quality. This dependence is especially acute at…
Fast Power Control Adaptation via Meta-Learning for Random Edge Graph Neural Networks
Ivana Nikoloska, Osvaldo Simeone
Power control in decentralized wireless networks poses a complex stochastic optimization problem when formulated as the maximization of the average sum rate for arbitrary interfere…
HyperRNN: Deep Learning-Aided Downlink CSI Acquisition via Partial Channel Reciprocity for FDD Massive MIMO
Yusha Liu, Osvaldo Simeone
In order to unlock the full advantages of massive multiple input multiple output (MIMO) in the downlink, channel state information (CSI) is required at the base station (BS) to opt…
Meta-ViterbiNet: Online Meta-Learned Viterbi Equalization for Non-Stationary Channels
Tomer Raviv, Sangwoo Park, Nir Shlezinger +3
Deep neural networks (DNNs) based digital receivers can potentially operate in complex environments. However, the dynamic nature of communication channels implies that in some scen…
Channel-Driven Monte Carlo Sampling for Bayesian Distributed Learning in Wireless Data Centers
Dongzhu Liu, Osvaldo Simeone
Conventional frequentist learning, as assumed by existing federated learning protocols, is limited in its ability to quantify uncertainty, incorporate prior knowledge, guide active…
Joint Source-Channel Coding for Semantics-Aware Grant-Free Radio Access in IoT Fog Networks
Johannes Dommel, Zoran Utkovski, Osvaldo Simeone +1
A fog-radio access network (F-RAN) architecture is studied for an Internet-of-Things (IoT) system in which wireless sensors monitor a number of multi-valued events and transmit in…
Single-RF Multi-User Communication Through Reconfigurable Intelligent Surfaces: An Information-Theoretic Analysis
Roy Karasik, Osvaldo Simeone, Marco Di Renzo +1
Reconfigurable intelligent surfaces (RISs) are typically used in multi-user systems to mitigate interference among active transmitters. In contrast, this paper studies a setting wi…
Federated Learning over Wireless Device-to-Device Networks: Algorithms and Convergence Analysis
Hong Xing, Osvaldo Simeone, Suzhi Bi
The proliferation of Internet-of-Things (IoT) devices and cloud-computing applications over siloed data centers is motivating renewed interest in the collaborative training of a sh…