Deep learning-aided 6G wireless networks: A comprehensive survey of revolutionary PHY architectures
Deep learning (DL) has proven its unprecedented success in diverse fields such as
computer vision, natural language processing, and speech recognition by its strong …
computer vision, natural language processing, and speech recognition by its strong …
A survey of deep learning based NOMA: State of the art, key aspects, open challenges and future trends
SAH Mohsan, Y Li, AV Shvetsov, J Varela-Aldás… - Sensors, 2023 - mdpi.com
Non-Orthogonal Multiple Access (NOMA) has become a promising evolution with the
emergence of fifth-generation (5G) and Beyond-5G (B5G) rollouts. The potentials of NOMA …
emergence of fifth-generation (5G) and Beyond-5G (B5G) rollouts. The potentials of NOMA …
Deep-learning-assisted IoT-based RIS for cooperative communications
Reconfigurable intelligent surfaces (RISs) are software-controlled passive devices that can
be used as relay systems to reflect incoming signals from a source to a destination in a …
be used as relay systems to reflect incoming signals from a source to a destination in a …
Randomized iterative methods for low-complexity large-scale MIMO detection
In this paper, we introduce a randomized iterative method for signal detection in uplink large-
scale multiple-input multiple-output (MIMO) systems, which not only achieves a low …
scale multiple-input multiple-output (MIMO) systems, which not only achieves a low …
DLNet: Deep learning-aided massive MIMO decoder
Traditional MIMO decoding schemes are complex, impractical, and perform poorly for
massive multiple-input multiple-output (M-MIMO) systems. Deep learning (DL) has recently …
massive multiple-input multiple-output (M-MIMO) systems. Deep learning (DL) has recently …
Deep-learning based signal detection for MIMO-OTFS systems
In this paper, we propose a DL-based detector model based on two-dimensional
convolutional neural network (2D-CNN) which can readily exploit the delay-Doppler channel …
convolutional neural network (2D-CNN) which can readily exploit the delay-Doppler channel …
Real-time machine learning for symbol detection in MIMO-OFDM systems
Recently, there have been renewed interests in applying machine learning (ML) techniques
to wireless systems. Nevertheless, ML-based approaches often require a large amount of …
to wireless systems. Nevertheless, ML-based approaches often require a large amount of …
Reservoir computing meets extreme learning machine in real-time MIMO-OFDM receive processing
In this paper, we consider a real-time deep learning-based symbol detection approach for
MIMO-OFDM systems. To exploit the temporal correlation of the wireless channel and the …
MIMO-OFDM systems. To exploit the temporal correlation of the wireless channel and the …
Detect to learn: Structure learning with attention and decision feedback for MIMO-OFDM receive processing
The limited over-the-air (OTA) pilot symbols in multiple-input-multiple-output orthogonal-
frequency-division-multiplexing (MIMO-OFDM) systems presents a major challenge for …
frequency-division-multiplexing (MIMO-OFDM) systems presents a major challenge for …
CSI-free geometric symbol detection via semi-supervised learning and ensemble learning
J Zhang, C Masouros, Y Huang - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Symbol detection (SD) plays an important role in a digital communication system. However,
most SD algorithms require channel state information (CSI), which is often difficult to …
most SD algorithms require channel state information (CSI), which is often difficult to …