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Found 7 papers, 0 papers with code
Self-Supervised Learning for User Localization
Following this, we utilize the encoder portion of the AE models to extract relevant features from labeled data, and finetune an MLP-based Position Estimation Model to accurately deduce user locations.
AI in 6G: Energy-Efficient Distributed Machine Learning for Multilayer Heterogeneous Networks
Adept network management is key for supporting extremely heterogeneous applications with stringent quality of service (QoS) requirements; this is more so when envisioning the complex and ultra-dense 6G mobile heterogeneous network (HetNet).
Smart Traffic Monitoring System using Computer Vision and Edge Computing
In this paper, we focus on two common traffic monitoring tasks, congestion detection, and speed detection, and propose a two-tier edge computing based model that takes into account of both the limited computing capability in cloudlets and the unstable network condition to the TMC.
Machine Learning-based Signal Detection for PMH Signals in Load-modulated MIMO System
By leveraging machine learning, the proposed detection scheme can accurately obtain information of the channel from a few of the received symbols with little resource cost and achieve comparable detection results as that of the optimal ML detector.
Indoor Localization Using Visible Light Via Fusion Of Multiple Classifiers
A multiple classifiers fusion localization technique using received signal strengths (RSSs) of visible light is proposed, in which the proposed system transmits different intensity modulated sinusoidal signals by LEDs and the signals received by a Photo Diode (PD) placed at various grid points.
Indoor Localization by Fusing a Group of Fingerprints Based on Random Forests
Recently, we first proposed a GrOup Of Fingerprints (GOOF) to improve the localization accuracy and reduce the burden of building fingerprints.
Localization by Fusing a Group of Fingerprints via Multiple Antennas in Indoor Environment
We first build a GrOup Of Fingerprints (GOOF), which includes five different fingerprints, namely, RSS, covariance matrix, signal subspace, fractional low order moment, and fourth-order cumulant, which are obtained by different transformations of the received signals from multiple antennas in the offline stage.
