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 …
Semantics-empowered communications: A tutorial-cum-survey
Along with the springing up of the semantics-empowered communication (SemCom)
research, it is now witnessing an unprecedentedly growing interest towards a wide range of …
research, it is now witnessing an unprecedentedly growing interest towards a wide range of …
Wireless networks design in the era of deep learning: Model-based, AI-based, or both?
A Zappone, M Di Renzo… - IEEE Transactions on …, 2019 - ieeexplore.ieee.org
This paper deals with the use of emerging deep learning techniques in future wireless
communication networks. It will be shown that the data-driven approaches should not …
communication networks. It will be shown that the data-driven approaches should not …
Deep learning-based end-to-end wireless communication systems with conditional GANs as unknown channels
In this article, we develop an end-to-end wireless communication system using deep neural
networks (DNNs), where DNNs are employed to perform several key functions, including …
networks (DNNs), where DNNs are employed to perform several key functions, including …
Machine learning in the air
Thanks to the recent advances in processing speed, data acquisition and storage, machine
learning (ML) is penetrating every facet of our lives, and transforming research in many …
learning (ML) is penetrating every facet of our lives, and transforming research in many …
Model-free training of end-to-end communication systems
The idea of end-to-end learning of communication systems through neural network (NN)-
based autoencoders has the shortcoming that it requires a differentiable channel model. We …
based autoencoders has the shortcoming that it requires a differentiable channel model. We …
Hybrid precoding for multiuser millimeter wave massive MIMO systems: A deep learning approach
AM Elbir, AK Papazafeiropoulos - IEEE Transactions on …, 2019 - ieeexplore.ieee.org
In multi-user millimeter wave (mmWave) multiple-input-multiple-output (MIMO) systems,
hybrid precoding is a crucial task to lower the complexity and cost while achieving a …
hybrid precoding is a crucial task to lower the complexity and cost while achieving a …
Joint antenna selection and hybrid beamformer design using unquantized and quantized deep learning networks
In millimeter-wave communications, multiple-input-multiple-output (MIMO) systems use large
antenna arrays to achieve high gain and spectral efficiency. These massive MIMO systems …
antenna arrays to achieve high gain and spectral efficiency. These massive MIMO systems …
Deep learning for wireless communications
Existing communication systems exhibit inherent limitations in translating theory to practice
when handling the complexity of optimization for emerging wireless applications with high …
when handling the complexity of optimization for emerging wireless applications with high …
CNN-based precoder and combiner design in mmWave MIMO systems
AM Elbir - IEEE Communications Letters, 2019 - ieeexplore.ieee.org
Hybrid beamformer design is a crucial stage in millimeter-wave (mmWave) MIMO systems.
In this letter, we propose a convolutional neural network (CNN) framework for the joint …
In this letter, we propose a convolutional neural network (CNN) framework for the joint …