A canonical wireless communication system consists of a transmitter and a receiver. The
information bit stream is transmitted after coding, modulation, and pulse shaping. Due to the …

Wireless communications systems are traditionally designed by independently optimising
signal processing functions based on a mathematical model. Deep learning-enabled …

In this paper, we aim to efficiently and accurately predict the static channel impulse response
(CIR) with only the user's position information and a set of channel instances obtained within …

End-to-end learning of a communications system using the deep learning-based
autoencoder concept has drawn interest in recent research due to its simplicity, flexibility …

Generative adversarial network (GAN) has attracted wide attention because of its
remarkable ability to learn high-dimensional and complex data distributions based on game …

Wireless networks need channel estimation (CE) to function well. Deep learning (DL) has
improved 5G and future-generation network communication reliability and computational …

The practical realization of end-to-end training of communication systems is fundamentally
limited by its accessibility of the channel gradient. To overcome this major burden, the idea …

An end-to-end learning framework is proposed to optimize each module jointly in the
communication system. Recently, convolutional neural network (CNN) and conditional …

In this paper, online deep learning (DL)-based channel estimation algorithm for doubly
selective fading channels is proposed by employing the deep neural network (DNN). With …

Deep learning (DL) has been gradually applied to wireless communication and has
achieved remarkable results. However, training a DL model requires numerous data, and an …