29 citations · 30 across the 11 of their papers we have counts for
24 papers
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…
Federated Learning Beyond the Star: Local D2D Model Consensus with Global Cluster Sampling
Frank Po-Chen Lin, Seyyedali Hosseinalipour, Sheikh Shams Azam +2
Federated learning has emerged as a popular technique for distributing model training across the network edge. Its learning architecture is conventionally a star topology between t…
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…
Learning and Adaptation for Millimeter-Wave Beam Tracking and Training: a Dual Timescale Variational Framework
Muddassar Hussain, Nicolo Michelusi
Millimeter-wave vehicular networks incur enormous beam-training overhead to enable narrow-beam communications. This paper proposes a learning and adaptation framework in which the…
Semi-Decentralized Federated Learning with Cooperative D2D Local Model Aggregations
Frank Po-Chen Lin, Seyyedali Hosseinalipour, Sheikh Shams Azam +2
Federated learning has emerged as a popular technique for distributing machine learning (ML) model training across the wireless edge. In this paper, we propose two timescale hybrid…
Federated Learning with Communication Delay in Edge Networks
Frank Po-Chen Lin, Christopher G. Brinton, Nicolò Michelusi
Federated learning has received significant attention as a potential solution for distributing machine learning (ML) model training through edge networks. This work addresses an im…