activity
20162022
most citedDeep Reinforcement Learning for Simultaneous Sensing and Channel Access in Cognitive Networks

1 citations · 1 across the 6 of their papers we have counts for

collaborators

12 papers

physics.optics2022

Topological transitions and surface umklapp scattering in Slack Metasurfaces

Kobi-Yaakov Cohen, Shai Tsesses, Shimon Dolev +3

Metamaterials and metasurfaces are at the pinnacle of wave propagation engineering, yet their design has thus far been mainly focused on deep-subwavelength periodicities, practical…

cs.NI2022

An Online Learning Approach to Shortest Path and Backpressure Routing in Wireless Networks

Omer Amar, Kobi Cohen

We consider the adaptive routing problem in multihop wireless networks. The link states are assumed to be random variables drawn from unknown distributions, independent and identic…

cs.IT20211 cited

Deep Reinforcement Learning for Simultaneous Sensing and Channel Access in Cognitive Networks

Yoel Bokobza, Ron Dabora, Kobi Cohen

We consider the problem of dynamic spectrum access (DSA) in cognitive wireless networks, where only partial observations are available to the users due to narrowband sensing and tr…

cs.LG2021

Accelerated Gradient Descent Learning over Multiple Access Fading Channels

Raz Paul, Yuval Friedman, Kobi Cohen

We consider a distributed learning problem in a wireless network, consisting of N distributed edge devices and a parameter server (PS). The objective function is a sum of the edge…

eess.SP2021

Distributed Learning over Markovian Fading Channels for Stable Spectrum Access

Tomer Gafni, Kobi Cohen

We consider the problem of multi-user spectrum access in wireless networks. The bandwidth is divided into K orthogonal channels, and M users aim to access the spectrum. Each user c…

cs.LG2020

Over-the-Air Federated Learning from Heterogeneous Data

Tomer Sery, Nir Shlezinger, Kobi Cohen +1

Federated learning (FL) is a framework for distributed learning of centralized models. In FL, a set of edge devices train a model using their local data, while repeatedly exchangin…