Many performance gains achieved by massive multiple-input and multiple-output depend on
the accuracy of the downlink channel state information (CSI) at the transmitter (base station) …

In this paper, reinforcement learning (RL) based end-to-end channel prediction (CP) and
beamforming (BF) algorithms are proposed for multi-user downlink system. Different from the …

Channel state information (CSI) plays a critical role in achieving the potential benefits of
massive multiple input multiple output (MIMO) systems. In frequency division duplex (FDD) …

Wireless links using massive MIMO transceivers are vital for next generation wireless
communications networks. Precoding in Massive MIMO transmission requires accurate …

To fully unlock the benefits of multiple-input multiple-output (MIMO) networks, downlink
channel state information (CSI) is required at the base station (BS). In frequency division …

In the literature, machine learning (ML) has been implemented at the base station (BS) and
user equipment (UE) to improve the precision of downlink channel state information (CSI) …

In frequency division duplex mode, downlink channel state information (CSI) should be sent
to the base station via a feedback link to obtain the benefits of the massive multiple-input …

Acquiring downlink channel state information (CSI) at basestation (gNB) is crucial for
optimizing performance in massive MIMO FDD systems. Deep learning (DL) architectures …

针对现阶段大规模MIMO 系统中基于深度学习的信道状态信息(CSI) 反馈算法待训练参数过多,
可解释性不强的问题, 提出了2 种基于深度展开的CSI 反馈算法. 一种是基于可学习参数的近似 …

In frequency-division duplex (FDD) massive multiple-input multiple-output (MIMO) systems,
the growing number of base station antennas leads to prohibitive feedback overhead for …