This paper shows that deep neural network (DNN) can be used for efficient and distributed
channel estimation, quantization, feedback, and downlink multiuser precoding for a …

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 …

Massive multiple-input multiple-output (MIMO) is one of the most promising techniques for
next generation wireless communications due to its superior capability to provide high …

Massive multiple-input multiple-output (MIMO) is widely recognized as a promising
technology for future 5G wireless communication systems. To achieve the theoretical …

We propose a novel method for massive multiple-input multiple-output (massive MIMO) in
frequency division duplexing (FDD) systems. Due to the large frequency separation between …

We consider the problem of downlink training and channel estimation in frequency division
duplex (FDD) massive MIMO systems, where the base station (BS) equipped with a large …

There is much discussion in industry and academia about possible technical solutions to
address the growth in demand for wireless broadband. Massive multiple-input multiple …

Most works on cell-free massive multiple-input multiple-output (MIMO) consider non-
cooperative precoding strategies at the base stations (BSs) to avoid extensive channel state …

In this letter, we study the channel state information (CSI) feedback based on the deep
autoencoder (AE) considering the feedback errors and feedback delay in the frequency …

Accurate downlink channel state information (CSI) is required to be fed back to the base
station (BS) in frequency division duplexing (FDD) massive multiple-input multiple-output …