Leveraging large language models for wireless symbol detection via in-context learning
Deep neural networks (DNNs) have made significant strides in tackling challenging tasks in
wireless systems, especially when an accurate wireless model is not available. However …
wireless systems, especially when an accurate wireless model is not available. However …
In-context learning for MIMO equalization using transformer-based sequence models
Large pre-trained sequence models, such as transformer-based architectures, have been
recently shown to have the capacity to carry out in-context learning (ICL). In ICL, a decision …
recently shown to have the capacity to carry out in-context learning (ICL). In ICL, a decision …
Cell-free multi-user MIMO equalization via in-context learning
Large pre-trained sequence models, such as transformers, excel as few-shot learners
capable of in-context learning (ICL). In ICL, a model is trained to adapt its operation to a new …
capable of in-context learning (ICL). In ICL, a model is trained to adapt its operation to a new …
Adaptive and Flexible Model-Based AI for Deep Receivers in Dynamic Channels
Artificial intelligence (AI) is envisioned to play a key role in future wireless technologies, with
deep neural networks (DNNs) enabling digital receivers to learn how to operate in …
deep neural networks (DNNs) enabling digital receivers to learn how to operate in …
Uncertainty quantification in deep learning based kalman filters
Various algorithms combine deep neural networks (DNNs) and Kalman filters (KFs) to learn
from data to track in complex dynamics. Unlike classic KFs, DNN-based systems do not …
from data to track in complex dynamics. Unlike classic KFs, DNN-based systems do not …
Reconfigurable AI Modules Aided Channel Estimation and MIMO Detection
Deep learning (DL) based channel estimation (CE) and multiple input and multiple output
detection (MIMODet), as two separate research topics, have provided convinced evidence to …
detection (MIMODet), as two separate research topics, have provided convinced evidence to …
Calibrating Bayesian Learning via Regularization, Confidence Minimization, and Selective Inference
The application of artificial intelligence (AI) models in fields such as engineering is limited by
the known difficulty of quantifying the reliability of an AI's decision. A well-calibrated AI model …
the known difficulty of quantifying the reliability of an AI's decision. A well-calibrated AI model …
Calibrating Wireless AI via Meta-Learned Context-Dependent Conformal Prediction
Modern software-defined networks, such as Open Radio Access Network (O-RAN) systems,
rely on artificial intelligence (AI)-powered applications running on controllers interfaced with …
rely on artificial intelligence (AI)-powered applications running on controllers interfaced with …
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 …
Inter-Mode-Interference-Aware OAM Detector via Deep Learning
Increasing communication bandwidth is a highly effective method for improving
communication system throughput. However, the sub-6GHz frequency band is already …
communication system throughput. However, the sub-6GHz frequency band is already …