Federated Learning and Meta Learning: Approaches, Applications, and Directions
Over the past few years, significant advancements have been made in the field of machine
learning (ML) to address resource management, interference management, autonomy, and …
learning (ML) to address resource management, interference management, autonomy, and …
Federated and meta learning over non-wireless and wireless networks: A tutorial
In recent years, various machine learning (ML) solutions have been developed to solve
resource management, interference management, autonomy, and decision-making …
resource management, interference management, autonomy, and decision-making …
Meta learning-based MIMO detectors: Design, simulation, and experimental test
Deep neural networks (NNs) have exhibited considerable potential for efficiently balancing
the performance and complexity of multiple-input and multiple-output (MIMO) detectors …
the performance and complexity of multiple-input and multiple-output (MIMO) detectors …
Graph Neural Network-Enhanced Expectation Propagation Algorithm for MIMO Turbo Receivers
Deep neural networks (NNs) are considered a powerful tool for balancing the performance
and complexity of multiple-input multiple-output (MIMO) receivers due to their accurate …
and complexity of multiple-input multiple-output (MIMO) receivers due to their accurate …
Algorithm parameters selection method with deep learning for EP MIMO detector
H Chen, G Yao, J Hu - IEEE Transactions on Vehicular …, 2021 - ieeexplore.ieee.org
Expectation Propagation (EP)-based Multiple-Input Multiple-Output (MIMO) detector
achieves exceptional performance in high-dimensional systems with high-order modulations …
achieves exceptional performance in high-dimensional systems with high-order modulations …
Learning-based stabilization of Markov jump linear systems
In this paper, we explore the stabilization problem of discrete-time Markov jump linear
systems from a new perspective. We establish a novel learning-based framework that …
systems from a new perspective. We establish a novel learning-based framework that …
Extrinsic graph neural network-aided expectation propagation for Turbo-MIMO receiver
Deep neural networks (NNs) promise excellent performance and high efficiency in
constructing multiple-input multiple-output (MIMO) receivers. Recently, graph NNs (GNNs) …
constructing multiple-input multiple-output (MIMO) receivers. Recently, graph NNs (GNNs) …
A Deep-Learning-Aided Message Passing Detector for MIMO SC-FDMA
Y Zeng, Y Ge, X Tan, Z Ji, Z Zhang… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
In this paper, a multiple-input multiple-output (MIMO) single carrier frequency division
multiple access (SC-FDMA) detector is proposed, named simplified generalized …
multiple access (SC-FDMA) detector is proposed, named simplified generalized …
MetaSSD: Meta-Learned Self-Supervised Detection
Deep learning-based symbol detector gains increasing attention due to the simple algorithm
design than the traditional model-based algorithms such as Viterbi and BCJR. The …
design than the traditional model-based algorithms such as Viterbi and BCJR. The …
Extrinsic Versus App Information Feedback in Turbo Vep Mu-Mimo Receivers: Optimization Via Deep Unfolding.
A Michon, C Poulliat, A Mekhiche… - ICASSP 2024-2024 …, 2024 - ieeexplore.ieee.org
The joint use of Soft-Input Soft-Output (SISO) detectors and channel decoders in an iterative
manner has received growing attention for Multi-User Multiple-Input Multiple-Output (MU …
manner has received growing attention for Multi-User Multiple-Input Multiple-Output (MU …