With the large number of antennas and subcarriers the overhead due to pilot transmission
for channel estimation can be prohibitive in wideband massive multiple-input multiple-output …

This letter proposes a deep learning-based pilot design scheme to minimize the sum mean
square error (MSE) of channel estimation for multi-user distributed massive multiple-input …

One of the fundamental challenges to realize massive Multiple-Input Multiple-Output (MIMO)
communications is the accurate acquisition of channel state information for a plurality of …

Massive multi-input multi-output (MIMO) has aroused extensive interest in communication
industry, as it can effectively increase communication system capacity and reduce transmit …

Massive multiple-input multiple-output (mMIMO) is a critical component in upcoming 5G
wireless deployment as an enabler for high data rate communications. mMIMO is effective …

In frequency-division duplexing systems, the downlink channel state information (CSI)
acquisition scheme leads to high training and feedback overhead. In this work, we propose …

Channel estimation is one of the key issues in practical massive multiple-input multiple-
output (MIMO) systems. Compared with conventional estimation algorithms, deep learning …

To support the ever increasing number of devices in massive multiple-input multiple-output
(mMIMO) systems, an excessive amount of overhead is required for conventional orthogonal …

In this letter, we propose a deep learning (DL)-based channel estimation scheme for the
massive multiple-input multiple-output (MIMO) system. Unlike existing studies, we develop …

This paper proposes a deep learning-based channel estimation method for multi-cell
interference-limited massive MIMO systems, in which base stations equipped with a large …