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Found 7 papers, 0 papers with code
Deep Learning Based Active Spatial Channel Gain Prediction Using a Swarm of Unmanned Aerial Vehicles
We develop novel active prediction approaches which consist of both methods for UAV path planning for optimal measurement collection and methods for prediction of CG across space based on the collected measurements.
GAN-RXA: A Practical Scalable Solution to Receiver-Agnostic Transmitter Fingerprinting
We also propose two deep-learning approaches (SD-RXA and GAN-RXA) in this first stage to improve the receiver-agnostic property of the RXA framework.
Multiband Massive IoT: A Learning Approach to Infrastructure Deployment
The first approach is based on a predefined model of the environment, while the second approach is measurement-based model-free approach, which is applicable to any environment.
Destination-Feedback Free Distributed Transmit Beamforming using Guided Directionality
Most existing distributed beamforming approaches rely on the destination radio sending feedback to adjust the transmitters' signals for coherent combining.
UAV Swarm Position Optimization for High Capacity MIMO Backhaul
This capacity increase persists when using a massive MIMO ground station.
Spatial Signal Strength Prediction using 3D Maps and Deep Learning
Machine learning (ML) and artificial neural networks (ANNs) have been successfully applied to simulating complex physics by learning physics models thanks to large data.
Band Assignment in Ultra-Narrowband (UNB) Systems for Massive IoT Access
The objective is to find an assignment of BSs to multiplexing bands in order to maximize the packet decoding probability (PDP).
