no code implementations • 1 Apr 2024 • Bharath Keshavamurthy, Nicolo Michelusi
This work describes the orchestration of a fleet of rotary-wing Unmanned Aerial Vehicles (UAVs) for harvesting prioritized traffic from random distributions of heterogeneous users with Multiple Input Multiple Output (MIMO) capabilities.
1 code implementation • 16 Feb 2023 • Bharath Keshavamurthy, Yaguang Zhang, Christopher R. Anderson, Nicolo Michelusi, James V. Krogmeier, David J. Love
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 GHz V2X propagation modeling.
no code implementations • 19 Nov 2022 • Nicolo Michelusi
Yet, executing DGD over wireless systems affected by noise, fading and limited bandwidth presents challenges, requiring scheduling of transmissions to mitigate interference and the acquisition of topology and channel state information -- complex tasks in wireless decentralized systems.
1 code implementation • 16 Sep 2022 • Bharath Keshavamurthy, Nicolo Michelusi
We describe the orchestration of a decentralized swarm of rotary-wing UAV-relays, augmenting the coverage and service capabilities of a terrestrial base station.
no code implementations • 7 Feb 2022 • Seyyedali Hosseinalipour, Su Wang, Nicolo Michelusi, Vaneet Aggarwal, Christopher G. Brinton, David J. Love, Mung Chiang
PSL considers the realistic scenario where global aggregations are conducted with idle times in-between them for resource efficiency improvements, and incorporates data dispersion and model dispersion with local model condensation into FedL.
no code implementations • 4 Jan 2022 • Tzu-Hsuan Chou, Nicolo Michelusi, David J. Love, James V. Krogmeier
6G operators may use millimeter wave (mmWave) and sub-terahertz (sub-THz) bands to meet the ever-increasing demand for wireless access.
no code implementations • 27 Dec 2021 • David Nickel, Frank Po-Chen Lin, Seyyedali Hosseinalipour, Nicolo Michelusi, Christopher G. Brinton
Federated learning (FL) has emerged as a popular technique for distributing machine learning across wireless edge devices.
1 code implementation • 14 Oct 2021 • Bharath Keshavamurthy, Yaguang Zhang, Christopher R. Anderson, Nicolo Michelusi, James V. Krogmeier, David J. Love
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.
1 code implementation • 14 Jul 2021 • 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 occupancy of licensed users (LUs) in a radio ecosystem; concurrently, it devises an approximately optimal spectrum sensing and access policy under sensing constraints.
no code implementations • 27 Jun 2021 • Muddassar Hussain, Nicolo Michelusi
This paper proposes a learning and adaptation framework in which the dynamics of the communication beams are learned and then exploited to design adaptive beam-tracking and training with low overhead: on a long-timescale, a deep recurrent variational autoencoder (DR-VAE) uses noisy beam-training feedback to learn a probabilistic model of beam dynamics and enable predictive beam-tracking; on a short-timescale, an adaptive beam-training procedure is formulated as a partially observable (PO-) Markov decision process (MDP) and optimized via point-based value iteration (PBVI) by leveraging beam-training feedback and a probabilistic prediction of the strongest beam pair provided by the DR-VAE.
1 code implementation • 18 Mar 2021 • Frank Po-Chen Lin, Seyyedali Hosseinalipour, Sheikh Shams Azam, Christopher G. Brinton, Nicolo Michelusi
Federated learning has emerged as a popular technique for distributing machine learning (ML) model training across the wireless edge.
no code implementations • 5 Aug 2020 • Tzu-Hsuan Chou, Nicolo Michelusi, David J. Love, James V. Krogmeier
A data tensor is constructed by collecting beam-training measurements on a subset of positions and beams, and a hybrid noisy tensor completion (HNTC) algorithm is proposed to predict the received power across the coverage area, which exploits both the spatial smoothness and the low-rank property of MIMO channels.
1 code implementation • 18 Jul 2020 • Seyyedali Hosseinalipour, Sheikh Shams Azam, Christopher G. Brinton, Nicolo Michelusi, Vaneet Aggarwal, David J. Love, Huaiyu Dai
We derive the upper bound of convergence for MH-FL with respect to parameters of the network topology (e. g., the spectral radius) and the learning algorithm (e. g., the number of D2D rounds in different clusters).