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20132026
most citedMulti-Stage Hybrid Federated Learning over Large-Scale D2D-Enabled Fog Networks

127 citations · 356 across the 54 of their papers we have counts for

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15 papers · 1 filter

eess.SP2026

Distributed Optimization with Streaming Data: A Temporal Weighting Perspective

Muhammad Faraz Ul Abrar, Nicolò Michelusi, Erik G. Larsson

Optimization theory is a widely used tool for intelligent decision-making. While classical optimization deals with fixed, time-invariant objective functions, many modern applicatio…

eess.SP2026

Decentralized Time-Varying Optimization for Streaming Data via Temporal Weighting

Muhammad Faraz Ul Abrar, Nicolò Michelusi, Erik G. Larsson

Classical optimization theory largely focuses on fixed objective functions, whereas many modern learning systems operate in dynamic environments where data arrive sequentially and…

eess.SP2026

Interference-Robust Non-Coherent Over-the-Air Computation for Decentralized Optimization

Nicolò Michelusi

Non-coherent over-the-air (NCOTA) computation enables low-latency and bandwidth-efficient decentralized optimization by exploiting the average energy superposition property of wire…

eess.SP2025

Joint UAV Placement and Transceiver Design in Multi-User Wireless Relay Networks

Tzu-Hsuan Chou, Nicolo Michelusi, David J. Love +1

In this paper, a novel approach is proposed to improve the minimum signal-to-interference-plus-noise-ratio (SINR) among users in non-orthogonal multi-user wireless relay networks,…

eess.SP2025★ 4 cited

Unified Analysis of Decentralized Gradient Descent: a Contraction Mapping Framework

Erik G. Larsson, Nicolo Michelusi

The decentralized gradient descent (DGD) algorithm, and its sibling, diffusion, are workhorses in decentralized machine learning, distributed inference and estimation, and multi-ag…

eess.SP2025

Hybrid Fingerprint-based Positioning in Cell-Free Massive MIMO Systems

Manish Kumar, Tzu-Hsuan Chou, Byunghyun Lee +3

Recently, there has been an increasing interest in 6G technology for integrated sensing and communications, where positioning stands out as a key application. In the realm of 6G, c…

eess.SP2023

Propagation Measurements and Analyses at 28 GHz via an Autonomous Beam-Steering Platform

Bharath Keshavamurthy, Yaguang Zhang, Christopher R. Anderson +3

This paper details the design of an autonomous alignment and tracking platform to mechanically steer directional horn antennas in a sliding correlator channel sounder setup for 28…

eess.SP2022★ 4 cited

Non-Coherent Over-the-Air Decentralized Gradient Descent

Nicolo' Michelusi

Implementing Decentralized Gradient Descent (DGD) in wireless systems is challenging due to noise, fading, and limited bandwidth, necessitating topology awareness, transmission sch…

eess.SP2022

Compressed Training for Dual-Wideband Time-Varying Sub-Terahertz Massive MIMO

Tzu-Hsuan Chou, Nicolo Michelusi, David J. Love +1

6G operators may use millimeter wave (mmWave) and sub-terahertz (sub-THz) bands to meet the ever-increasing demand for wireless access. Sub-THz communication comes with many existi…

eess.SP2021

A Robotic Antenna Alignment and Tracking System for Millimeter Wave Propagation Modeling

Bharath Keshavamurthy, Yaguang Zhang, Christopher R. Anderson +3

In this paper, we discuss the design of a sliding-correlator channel sounder for 28 GHz propagation modeling on the NSF POWDER testbed in Salt Lake City, UT. Beam-alignment is mech…

eess.SP2021

Learning-based Spectrum Sensing and Access in Cognitive Radios via Approximate POMDPs

Bharath Keshavamurthy, Nicolo Michelusi

A novel LEarning-based Spectrum Sensing and Access (LESSA) framework is proposed, wherein a cognitive radio (CR) learns a time-frequency correlation model underlying spectrum occup…

eess.SP2020★ 22 cited

Fast Position-Aided MIMO Beam Training via Noisy Tensor Completion

Tzu-Hsuan Chou, Nicolo Michelusi, David J. Love +1

In this paper, a data-driven position-aided approach is proposed to reduce the training overhead in MIMO systems, by leveraging side information and on-the-field measurements. A da…

eess.SP2019

Millimeter Wave Beam Recommendation via Tensor Completion

Tzu-Hsuan Chou, Nicolo Michelusi, David J. Love +1

Accurate and fast beam-alignment is essential to cope with the fast-varying environment in millimeter-wave communications. A data-driven approach is a promising solution to reduce…

eess.SP2019★ 29 cited

Propagation Modeling Through Foliage in a Coniferous Forest at 28 GHz

Yaguang Zhang, Christopher R. Anderson, Nicolo Michelusi +3

The goal of this article is to investigate the propagation behavior of 28-GHz millimeter wave in coniferous forests and model its basic transmission loss. Field measurements were c…

eess.SP2018

Multi-user Beam-Alignment for Millimeter-Wave Networks

Rana A. Hassan, Nicolo Michelusi

Millimeter-wave communications is the most promising technology for next-generation cellular wireless systems, thanks to the large bandwidth available compared to sub-6 GHz network…