This paper proposes a deep learning approach to channel sensing and downlink hybrid
beamforming for massive multiple-input multiple-output systems operating in the time …

This paper proposes a deep learning approach to channel sensing and downlink hybrid
analog and digital beamforming for massive multiple-input multiple-output systems with a …

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 …

In this paper we study the channel estimation problem for a CP-OFDM based hybrid analog-
digital massive MIMO system. In contrast to a conventional MIMO system, two additional …

Massive multiple-input multiple-output (MIMO) communication is an emerging technology
that uses an excess of transmit antennas to realize high spectral efficiency. Achieving …

This letter considers uplink massive MIMO systems with 1-bit analog-to-digital converters
(ADCs) and develops a deep-learning based channel estimation framework. In this …

In this letter, we present a deep learning based linear precoder design to improve the
performance for uplink multiuser multiple-input multiple-output (MU-MIMO) systems with one …

A low-complexity fully connected hybrid precoding design is proposed for multiuser massive
MIMO systems over frequency-selective fading channels. Digital precoding and analog …

In this paper, we investigate the downlink robust precoding with imperfect channel state
information (CSI) for massive multiple-input-multiple-output (MIMO) communications. With …

In multi-user millimeter wave (mmWave) multiple-input-multiple-output (MIMO) systems,
hybrid precoding is a crucial task to lower the complexity and cost while achieving a …