We introduce a novel physical layer scheme for Multiple Input Multiple Output (MIMO)
communications based on unsupervised deep learning using an autoencoder. This method …

DL has shown great potential to revolutionize communication systems. This article provides
an overview of the recent advancements in DL-based physical layer communications. DL …

To overcome the influence of channel estimation error on signal detection, this paper
presents a model-driven deep learning method for joint channel estimation and signal …

How can machine learning help the design of future communication networks-and how can
future networks meet the demands of emerging machine learning applications? Discover the …

Knowledge of information about the propagation channel in which a wireless system
operates enables better, more efficient approaches for signal transmissions. Therefore …

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

The downlink channel estimation requires a huge pilot overhead in the reconfigurable
intelligent surface (RIS) assisted communication system. By exploiting the powerful learning …

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

Communications systems to date are primarily designed with the goal of reliable transfer of
digital sequences (bits). Next generation (NextG) communication systems are beginning to …

Aiming at the problem that the downlink channel estimation performance is limited due to the
fast time-varying and non-stationary characteristics in the high-speed mobile scenarios, we …