Model-based deep learning
Signal processing, communications, and control have traditionally relied on classical
statistical modeling techniques. Such model-based methods utilize mathematical …
statistical modeling techniques. Such model-based methods utilize mathematical …
[HTML][HTML] Towards artificial intelligence-aided mimo detection for 6g communication systems: a review of current trends, challenges and future directions
In recent times, artificial intelligence (AI) has gained considerable attention as a highly
promising technology for enhancing the performance of multiple-input multiple-output …
promising technology for enhancing the performance of multiple-input multiple-output …
Non-orthogonal multiple access enhanced multi-user semantic communication
Semantic communication has served as a novel paradigm and attracted a broad interest
from researchers. One critical aspect of it is the multi-user semantic communication theory …
from researchers. One critical aspect of it is the multi-user semantic communication theory …
Integrated sensing and communications towards proactive beamforming in mmWave V2I via multi-modal feature fusion (MMFF)
The future of vehicular communication networks relies on mmWave massive multi-input-
multi-output antenna arrays for intensive data transfer and massive vehicle access …
multi-output antenna arrays for intensive data transfer and massive vehicle access …
Deep-neural-network-based receiver design for downlink non-orthogonal multiple-access underwater acoustic communication
The excavation of the ocean has led to the submersion of numerous autonomous vehicles
and sensors. Hence, there is a growing need for multi-user underwater acoustic …
and sensors. Hence, there is a growing need for multi-user underwater acoustic …
Data augmentation for deep receivers
T Raviv, N Shlezinger - IEEE Transactions on Wireless …, 2023 - ieeexplore.ieee.org
Deep neural networks (DNNs) allow digital receivers to learn to operate in complex
environments. To do so, DNNs should preferably be trained using large labeled data sets …
environments. To do so, DNNs should preferably be trained using large labeled data sets …
Distributed deep joint source-channel coding over a multiple access channel
SF Yilmaz, C Karamanlı… - ICC 2023-IEEE …, 2023 - ieeexplore.ieee.org
We consider distributed image transmission over a noisy multiple access channel (MAC)
using deep joint source-channel coding (DeepJSCC). It is known that Shannon's separation …
using deep joint source-channel coding (DeepJSCC). It is known that Shannon's separation …
Modular model-based bayesian learning for uncertainty-aware and reliable deep MIMO receivers
In the design of wireless receivers, deep neural networks (DNNs) can be combined with
traditional model-based receiver algorithms to realize modular hybrid model-based/data …
traditional model-based receiver algorithms to realize modular hybrid model-based/data …
Improving user performance in cooperative NOMA millimeter wave networks under two-phase operation protocol
Improving user performance is the most important target of 5G and beyond 5G millimeter
wave cellular network systems. Thus, cooperative communication with assistance from …
wave cellular network systems. Thus, cooperative communication with assistance from …
A deep convolutional-LSTM neural network for signal detection of downlink NOMA system
B Panda, P Singh - AEU-International Journal of Electronics and …, 2023 - Elsevier
Non-orthogonal multiple access (NOMA) techniques have drawn much attention for massive
connectivity, heterogeneous data traffic with ultra-low latency requirements, and ultra-high …
connectivity, heterogeneous data traffic with ultra-low latency requirements, and ultra-high …