Carlo Fischione | DeepAI

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∙ 08/27/2023

A Comparison of Neural Networks for Wireless Channel Prediction

The performance of modern wireless communications systems depends critic...

0 Oscar Stenhammar, et al. ∙

∙ 08/07/2023

Off-the-grid Blind Deconvolution and Demixing

We consider the problem of gridless blind deconvolution and demixing (GB...

0 Saeed Razavikia, et al. ∙

∙ 04/26/2023

Green UAV-enabled Internet-of-Things Network with AI-assisted NOMA for Disaster Management

Unmanned aerial vehicle (UAV)-assisted communication is becoming a strea...

0 Muhammad Ali Jamshed, et al. ∙

∙ 03/01/2023

Computing Functions Over-the-Air Using Digital Modulations

Over-the-air computation (AirComp) is a known technique in which wireles...

0 Saeed Razavikia, et al. ∙

∙ 11/08/2022

Federated Learning Using Three-Operator ADMM

Federated learning (FL) has emerged as an instance of distributed machin...

0 Shashi Kant, et al. ∙

∙ 11/01/2022

Over-the-Air Computation for Distributed Systems: Something Old and Something New

Facing the upcoming era of Internet-of-Things and connected intelligence...

0 Zheng Chen, et al. ∙

∙ 10/31/2022

A-LAQ: Adaptive Lazily Aggregated Quantized Gradient

Federated Learning (FL) plays a prominent role in solving machine learni...

0 Afsaneh Mahmoudi, et al. ∙

∙ 10/31/2022

Blind Asynchronous Over-the-Air Federated Edge Learning

Federated Edge Learning (FEEL) is a distributed machine learning techniq...

0 Saeed Razavikia, et al. ∙

∙ 04/16/2022

FedCau: A Proactive Stop Policy for Communication and Computation Efficient Federated Learning

This paper investigates efficient distributed training of a Federated Le...

0 Afsaneh Mahmoudi, et al. ∙

∙ 11/19/2021

Over-the-Air Federated Learning with Retransmissions (Extended Version)

Motivated by increasing computational capabilities of wireless devices, ...

0 Henrik Hellström, et al. ∙

∙ 05/27/2021

Wireless for Control: Over-the-Air Controller

In closed-loop wireless control systems, the state-of-the-art approach p...

0 Pangun Park, et al. ∙

∙ 04/26/2021

Simultaneous Wireless Information and Power Transfer for Federated Learning

In the Internet of Things, learning is one of most prominent tasks. In t...

0 José Mairton B. da Silva Jr., et al. ∙

∙ 12/07/2020

Dynamic Clustering in Federated Learning

In the resource management of wireless networks, Federated Learning has ...

0 Yeongwoo Kim, et al. ∙

∙ 10/21/2020

Full-Duplex and Dynamic-TDD: Pushing the Limits of Spectrum Reuse in Multi-Cell Communications

Although in cellular networks full-duplex and dynamic time-division dupl...

0 José Mairton B. da Silva Jr., et al. ∙

∙ 08/31/2020

Wireless for Machine Learning

As data generation increasingly takes place on devices without a wired c...

0 Henrik Hellström, et al. ∙

∙ 06/22/2020

Wireless Avionics Intra-Communications: A Survey of Benefits, Challenges, and Solutions

In the aeronautics industry, wireless avionics intra-communications have...

0 Pangun Park, et al. ∙

∙ 03/23/2020

The Internet of Things as a Deep Neural Network

An important task in the Internet of Things (IoT) is field monitoring, w...

41 Rong Du, et al. ∙

∙ 03/19/2020

A Hybrid Model-based and Data-driven Approach to Spectrum Sharing in mmWave Cellular Networks

Inter-operator spectrum sharing in millimeter-wave bands has the potenti...

0 Hossein S. Ghadikolaei, et al. ∙

∙ 01/23/2020

Wirelessly-powered Sensor Networks Power Allocation for Channel Estimation and Energy Beamforming

Wirelessly-powered sensor networks (WPSNs) are becoming increasingly imp...

0 Rong Du, et al. ∙

∙ 12/03/2019

Bayesian Model Selection for Change Point Detection and Clustering

We address the new problem of estimating a piece-wise constant signal wi...

0 Othmane Mazhar, et al. ∙

∙ 11/30/2018

Fast and Reliable Initial Access with Random Beamforming for mmWave Networks

Millimeter-wave (mmWave) communications rely on directional transmission...

0 Yanpeng Yang, et al. ∙

∙ 08/06/2018

Low-latency Networking: Where Latency Lurks and How to Tame It

While the current generation of mobile and fixed communication networks ...

0 Xiaolin Jiang, et al. ∙

∙ 06/06/2018

Learning Kolmogorov Models for Binary Random Variables

We summarize our recent findings, where we proposed a framework for lear...

0 Hadi Ghauch, et al. ∙

∙ 05/23/2018

A Unified Framework for Training Neural Networks

The lack of mathematical tractability of Deep Neural Networks (DNNs) has...

0 Hadi Ghauch, et al. ∙

∙ 02/28/2018

Low-Overhead Coordination in Sub-28 Millimeter-Wave Networks

In this paper, we present some contributions from our recent investigati...

0 Hadi Ghauch, et al. ∙

∙ 02/18/2018

Reducing Initial Cell-search Latency in mmWave Networks

Millimeter-wave (mmWave) networks rely on directional transmissions, in ...

0 Yanpeng Yang, et al. ∙

∙ 01/11/2018

How to Split UL/DL Antennas in Full-Duplex Cellular Networks

To further improve the potential of full-duplex communications, networks...

0 José Mairton B. da Silva Jr., et al. ∙

∙ 10/07/2017

Interference Model Similarity Index and Its Applications to mmWave Networks: Extended version

In wireless communication networks, interference models are routinely us...

0 Hossein Shokri-Ghadikolaei, et al. ∙

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