Generative AI for physical layer communications: A survey
The recent evolution of generative artificial intelligence (GAI) leads to the emergence of
groundbreaking applications such as ChatGPT, which not only enhances the efficiency of …
groundbreaking applications such as ChatGPT, which not only enhances the efficiency of …
Ko codes: inventing nonlinear encoding and decoding for reliable wireless communication via deep-learning
Landmark codes underpin reliable physical layer communication, eg, Reed-Muller, BCH,
Convolution, Turbo, LDPC, and Polar codes: each is a linear code and represents a …
Convolution, Turbo, LDPC, and Polar codes: each is a linear code and represents a …
Role of deep learning in wireless communications
Traditional communication system design has always been based on the paradigm of first
establishing a mathematical model of the communication channel, then designing and …
establishing a mathematical model of the communication channel, then designing and …
Theoretical perspectives on deep learning methods in inverse problems
In recent years, there have been significant advances in the use of deep learning methods in
inverse problems such as denoising, compressive sensing, inpainting, and super-resolution …
inverse problems such as denoising, compressive sensing, inpainting, and super-resolution …
Toward optimally efficient search with deep learning for large-scale MIMO systems
This paper investigates the optimal signal detection problem with a particular interest in
large-scale multiple-input multiple-output (MIMO) systems. The problem is NP-hard and can …
large-scale multiple-input multiple-output (MIMO) systems. The problem is NP-hard and can …
Neural distributed compressor discovers binning
We consider lossy compression of an information source when the decoder has lossless
access to a correlated one. This setup, also known as the Wyner-Ziv problem, is a special …
access to a correlated one. This setup, also known as the Wyner-Ziv problem, is a special …
Learned Wyner–Ziv compressors recover binning
We consider lossy compression of an information source when the decoder has lossless
access to a correlated one. This setup, also known as the Wyner-Ziv problem, is a special …
access to a correlated one. This setup, also known as the Wyner-Ziv problem, is a special …
Productae: Toward training larger channel codes based on neural product codes
There have been significant research activities in recent years to automate the design of
channel encoders and decoders via deep learning. Due the dimensionality challenge in …
channel encoders and decoders via deep learning. Due the dimensionality challenge in …
Turbo detection aided autoencoder for multicarrier wireless systems: Integrating deep learning into channel coded systems
A variety of deep learning schemes have endeavoured to integrate deep neural networks
(DNNs) into channel coded systems by jointly designing DNN and the channel coding …
(DNNs) into channel coded systems by jointly designing DNN and the channel coding …
Deep learning empowered semi-blind joint detection in cooperative NOMA
In this paper, we propose a multi-user symbol detection in cooperative-non-orthogonal
multiple access (C-NOMA) schemes via deep learning (DL). We use a DL-based detection …
multiple access (C-NOMA) schemes via deep learning (DL). We use a DL-based detection …