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Found 8 papers, 0 papers with code
Self-Supervised Millimeter Wave Indoor Localization using Tiny Neural Networks
In this work, we propose to use tiny neural networks (NNs) to learn the relationship between angle difference-of-arrival (ADoA) measurements and locations of a receiver in an indoor environment.
Indoor Millimeter Wave Localization using Multiple Self-Supervised Tiny Neural Networks
We consider the localization of a mobile millimeter-wave client in a large indoor environment using multilayer perceptron neural networks (NNs).
ORACLE: Occlusion-Resilient and Self-Calibrating mmWave Radar Network for People Tracking
In this work, we present ORACLE, an autonomous system that (i) integrates automatic relative position and orientation estimation from multiple radar devices by exploiting the trajectories of people moving freely in the radars' common fields of view, and (ii) fuses the tracking information from multiple radars to obtain a unified tracking among all sensors.
Machine Learning-Based Distributed Authentication of UWAN Nodes with Limited Shared Information
We propose a technique to authenticate received packets in underwater acoustic networks based on the physical layer features of the underwater acoustic channel (UWAC).
A Review of Indoor Millimeter Wave Device-based Localization and Device-free Sensing Technologies and Applications
The commercial availability of low-cost millimeter wave (mmWave) communication and radar devices is starting to improve the penetration of such technologies in consumer markets, paving the way for large-scale and dense deployments in fifth-generation (5G)-and-beyond as well as 6G networks.
Millimeter Wave Localization with Imperfect Training Data using Shallow Neural Networks
In this work, we propose a shallow neural network model to localize mmWave devices indoors.
SQLR: Short-Term Memory Q-Learning for Elastic Provisioning
As more and more application providers transition to the cloud and deliver their services on a Software as a Service (SaaS) basis, cloud providers need to make their provisioning systems agile enough to meet Service Level Agreements.
A Novel Hyperparameter-free Approach to Decision Tree Construction that Avoids Overfitting by Design
Decision trees are an extremely popular machine learning technique.
