Search Results for author:
Found 13 papers, 6 papers with code
Orchestrating UAVs for Prioritized Data Harvesting: A Cross-Layer Optimization Perspective
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.
Propagation Measurements and Analyses at 28 GHz via an Autonomous Beam-Steering Platform
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.
Non-Coherent Over-the-Air Decentralized Gradient Descent
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.
Multiscale Adaptive Scheduling and Path-Planning for Power-Constrained UAV-Relays via SMDPs
We describe the orchestration of a decentralized swarm of rotary-wing UAV-relays, augmenting the coverage and service capabilities of a terrestrial base station.
Parallel Successive Learning for Dynamic Distributed Model Training over Heterogeneous Wireless Networks
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.
Compressed Training for Dual-Wideband Time-Varying Sub-Terahertz Massive MIMO
6G operators may use millimeter wave (mmWave) and sub-terahertz (sub-THz) bands to meet the ever-increasing demand for wireless access.
Resource-Efficient and Delay-Aware Federated Learning Design under Edge Heterogeneity
Federated learning (FL) has emerged as a popular technique for distributing machine learning across wireless edge devices.
A Robotic Antenna Alignment and Tracking System for Millimeter Wave Propagation Modeling
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.
Learning-based Spectrum Sensing and Access in Cognitive Radios via Approximate POMDPs
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.
Learning and Adaptation for Millimeter-Wave Beam Tracking and Training: a Dual Timescale Variational Framework
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.
Semi-Decentralized Federated Learning with Cooperative D2D Local Model Aggregations
Federated learning has emerged as a popular technique for distributing machine learning (ML) model training across the wireless edge.
Fast Position-Aided MIMO Beam Training via Noisy Tensor Completion
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.
Multi-Stage Hybrid Federated Learning over Large-Scale D2D-Enabled Fog Networks
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).
