As a new emerging technology for wireless communications, massive multiple-input multiple-
output (MIMO) faces a significant challenge to deploy a separate receiver chain of front-end …

In this paper, we develop a deep learning framework to optimize the compressive sampling
matrix in a massive multiple-input multiple-output (MIMO) system. The optimized …

As the most promising technology in wireless communications, massive multiple-input
multiple-output (MIMO) faces a significant challenge in practical implementation because of …

In millimeter-wave massive multiple-input multiple-output systems, to decrease the large
training overhead of traditional channel estimation techniques, compressive sensing (CS) is …

Massive multiple-input multiple-output (MIMO) systems have attracted extensive attention
recently due to their potentials to provide high system capacity. To obtain the benefits of …

In this paper, we consider the problem of sparse signal recovery using a learned dictionary
in multiple measurement vectors (MMVs) case. Employing deep neural networks, we …

In this paper, we propose a data-driven deep learning (DL) approach to jointly design the
pilot signals and channel estimator for wideband massive multiple-input multiple-output …

To fully utilize the spatial multiplexing gains or array gains of massive MIMO, the channel
state information must be obtained at the transmitter side (CSIT). However, conventional …

Accurate channel tracking with a small pilot overhead is vital for real-time massive multiple-
input and multiple-output (MIMO) communication over a dynamic channel. Recently …

Massive MIMO plays an important role for future cellular networks since the large number of
antenna elements is capable of increasing the spectral efficiency and the amount of usable …