Machine learning for large-scale optimization in 6g wireless networks
The sixth generation (6G) wireless systems are envisioned to enable the paradigm shift from
“connected things” to “connected intelligence”, featured by ultra high density, large-scale …
“connected things” to “connected intelligence”, featured by ultra high density, large-scale …
Intelligent multi-modal sensing-communication integration: Synesthesia of machines
In the era of sixth-generation (6G) wireless communications, integrated sensing and
communications (ISAC) is recognized as a promising solution to upgrade the physical …
communications (ISAC) is recognized as a promising solution to upgrade the physical …
Pervasive machine learning for smart radio environments enabled by reconfigurable intelligent surfaces
GC Alexandropoulos, K Stylianopoulos… - Proceedings of the …, 2022 - ieeexplore.ieee.org
The emerging technology of reconfigurable intelligent surfaces (RISs) is provisioned as an
enabler of smart wireless environments, offering a highly scalable, low-cost, hardware …
enabler of smart wireless environments, offering a highly scalable, low-cost, hardware …
Dilated convolution based CSI feedback compression for massive MIMO systems
Although the frequency-division duplex (FDD) massive multiple-input multiple-output (MIMO)
system can offer high spectral and energy efficiency, it requires to feedback the downlink …
system can offer high spectral and energy efficiency, it requires to feedback the downlink …
Deep learning-based implicit CSI feedback in massive MIMO
Massive multiple-input multiple-output can obtain more performance gain by exploiting the
downlink channel state information (CSI) at the base station (BS). Therefore, studying CSI …
downlink channel state information (CSI) at the base station (BS). Therefore, studying CSI …
MIMO channel estimation using score-based generative models
Channel estimation is a critical task in multiple-input multiple-output (MIMO) digital
communications that substantially affects end-to-end system performance. In this work, we …
communications that substantially affects end-to-end system performance. In this work, we …
Deep learning based physical layer security for terrestrial communications in 5G and beyond networks: A survey
The key principle of physical layer security (PLS) is to permit the secure transmission of
confidential data using efficient signal-processing techniques. Also, deep learning (DL) has …
confidential data using efficient signal-processing techniques. Also, deep learning (DL) has …
A lightweight decentralized-learning-based automatic modulation classification method for resource-constrained edge devices
Due to the computing capability and memory limitations, it is difficult to apply the traditional
deep learning (DL) models to the edge devices (EDs) for realizing lightweight automatic …
deep learning (DL) models to the edge devices (EDs) for realizing lightweight automatic …
Domain generalization in machine learning models for wireless communications: Concepts, state-of-the-art, and open issues
Data-driven machine learning (ML) is promoted as one potential technology to be used in
next-generation wireless systems. This led to a large body of research work that applies ML …
next-generation wireless systems. This led to a large body of research work that applies ML …
Environment semantics aided wireless communications: A case study of mmWave beam prediction and blockage prediction
In this paper, we propose an environment semantics aided wireless communication
framework to reduce the transmission latency and improve the transmission reliability, where …
framework to reduce the transmission latency and improve the transmission reliability, where …