Deep learning of GNSS acquisition
Signal acquisition is a crucial step in Global Navigation Satellite System (GNSS) receivers,
which is typically solved by maximizing the so-called Cross-Ambiguity Function (CAF) as a …
which is typically solved by maximizing the so-called Cross-Ambiguity Function (CAF) as a …
Augmented physics-based machine learning for navigation and tracking
This article presents a survey of the use of artificial intelligence/machine learning (AI/ML)
techniques in navigation and tracking applications, with a focus on the dynamical models …
techniques in navigation and tracking applications, with a focus on the dynamical models …
A Hybrid ODE-NN Framework for Modeling Incomplete Physiological Systems
This paper proposes a method to learn approximations of missing Ordinary Differential
Equations (ODEs) and states in physiological models where knowledge of the system's …
Equations (ODEs) and states in physiological models where knowledge of the system's …
Deep Learning in Wireless Communication Receiver: A Survey
SR Doha, A Abdelhadi - arxiv preprint arxiv:2501.17184, 2025 - arxiv.org
The design of wireless communication receivers to enhance signal processing in complex
and dynamic environments is going through a transformation by leveraging deep neural …
and dynamic environments is going through a transformation by leveraging deep neural …
Narrowband interference detection via deep learning
Due to the increased usage of spectrum caused by the exponential growth of wireless
devices, detecting and avoiding interference has become an increasingly relevant problem …
devices, detecting and avoiding interference has become an increasingly relevant problem …
VERITAS: Verifying the Performance of AI-native Transceiver Actions in Base-Stations
Artificial Intelligence (AI)-native receivers prove significant performance improvement in high
noise regimes and can potentially reduce communication overhead compared to the …
noise regimes and can potentially reduce communication overhead compared to the …
High-speed Machine Learning-enhanced Receiver for Millimeter-Wave Systems
Machine Learning (ML) is a promising tool to design wireless physical layer (PHY)
components. It is particularly interesting for millimeter-wave (mm-wave) frequencies and …
components. It is particularly interesting for millimeter-wave (mm-wave) frequencies and …
PRONTO: Preamble Overhead Reduction With Neural Networks for Coarse Synchronization
In IEEE 802.11 WiFi-based waveforms, the receiver performs coarse time and frequency
synchronization using the first field of the preamble known as the legacy short training field …
synchronization using the first field of the preamble known as the legacy short training field …
Real-Time AI-Enabled CSI Feedback Experimentation with Open RAN
There is interest from academia and industry to investigate the application of Artificial
Intelligence (AI)/Machine Learning (ML) to various use cases associated with the Air …
Intelligence (AI)/Machine Learning (ML) to various use cases associated with the Air …
Leveraging Machine Learning for More Efficient Real-Time Data Analysis
The present-day state of real-time statistics analysis, in the main, relies on guide evaluation
techniques that require trained records scientists as a way to extract significant insights from …
techniques that require trained records scientists as a way to extract significant insights from …