The rfml ecosystem: A look at the unique challenges of applying deep learning to radio frequency applications
While deep machine learning technologies are now pervasive in state-of-the-art image
recognition and natural language processing applications, only in recent years have these …
recognition and natural language processing applications, only in recent years have these …
Gradient-free training of autoencoders for non-differentiable communication channels
Training of autoencoders using the back-propagation algorithm is challenging for non-
differential channel models or in an experimental environment where gradients cannot be …
differential channel models or in an experimental environment where gradients cannot be …
Deep learning for fast and reliable initial access in AI-driven 6G mm wave networks
We present DeepIA, a deep neural network (DNN) framework for fast and reliable initial
access (IA) for artificial intelligence (AI)-driven 6G millimeter wave (mmWave) networks …
access (IA) for artificial intelligence (AI)-driven 6G millimeter wave (mmWave) networks …
Hydra-RAN Perceptual Networks Architecture: Dual-Functional Communications and Sensing Networks for 6G and Beyond
RI Abd, KS Kim, DJ Findley - IEEE Access, 2023 - ieeexplore.ieee.org
After researchers devoted considerable efforts to developing 5G standards, their passion
began to focus on establishing the basics for the standardization of 6G and beyond. The …
began to focus on establishing the basics for the standardization of 6G and beyond. The …
Benchmarking and interpreting end-to-end learning of MIMO and multi-user communication
End-to-end autoencoder (AE) learning has the potential of exceeding the performance of
human-engineered transceivers and encoding schemes, without a priori knowledge of …
human-engineered transceivers and encoding schemes, without a priori knowledge of …
Learning a physical layer scheme for the MIMO interference channel
This paper presents a novel physical layer scheme for multiple-input multiple-output (MIMO)
communication systems based on unsupervised deep learning (DL) using an autoencoder …
communication systems based on unsupervised deep learning (DL) using an autoencoder …
Model-based end-to-end learning for WDM systems with transceiver hardware impairments
We propose an autoencoder (AE)-based transceiver for a wavelength division multiplexing
(WDM) system impaired by hardware imperfections. We design our AE following the …
(WDM) system impaired by hardware imperfections. We design our AE following the …
Recent advances in constellation optimization for fiber-optic channels
Recent advances in constellation optimization for fiber-optic channels Page 1 Recent
advances in constellation optimization for fiber-optic channels Metodi P. Yankov(1), Ognjen …
advances in constellation optimization for fiber-optic channels Metodi P. Yankov(1), Ognjen …
Breaking wireless propagation environmental uncertainty with deep learning
ME Morocho-Cayamcela, M Maier… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
Wireless propagation loss modeling has gained significant attention due to its critical
importance in forthcoming dynamic wireless technologies. Stochastic and map-based …
importance in forthcoming dynamic wireless technologies. Stochastic and map-based …
Detection and channel equalization with deep learning for low resolution MIMO systems
A Klautau, N González-Prelcic… - 2018 52nd Asilomar …, 2018 - ieeexplore.ieee.org
Deep learning (DL) provides a framework for designing new communication systems that
embrace practical impairments. In this paper, we present an exploration of DL as applied to …
embrace practical impairments. In this paper, we present an exploration of DL as applied to …