Communication at high carrier frequencies such as millimeter wave (mmWave) and
terahertz (THz) requires channel estimation for very large bandwidths at low SNR. Hence …

Improving the accuracy of channel estimation is a significant topic in the context of wireless
communications. For training-based channel estimations, increasing the length of a training …

Channel estimation in wideband millimeter-wave (mmWave) systems is very challenging
due to the beam squint effect. To solve the problem, we propose a learnable iterative …

We propose a novel deep learning-based channel estimation technique for high-
dimensional communication signals that does not require any training. Our method is …

In modern wireless communication systems, wireless channel modeling has always been a
fundamental task in system design and performance optimization. Traditional channel …

Channel estimation is a critical module to determine the performance of wireless receivers.
For some communication systems, the channels are time-varying and without well-justified …

We propose generative channel modeling to learn statistical channel models from channel
input-output measurements. Generative channel models can learn more complicated …

In this letter we apply deep learning tools to conduct channel estimation for an orthogonal
frequency division multiplexing (OFDM) system based on downlink pilots. To be specific, a …

Traditional communication system design has always been based on the paradigm of first
establishing a mathematical model of the communication channel, then designing and …

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 …