This letter describes a real-valued sparse Bayesian learning (SBL) approach for massive
multiple-input multipleoutput (MIMO) downlink channel estimation. The main idea of the …

The efficient and highly accurate channel state information (CSI) at the base station is
essential to achieve the potential benefits of massive multiple input multiple output (MIMO) …

The pilot contamination problem creates a limitation to the potential benefits of massive
multiple input multiple output (MIMO) systems. To mitigate the pilot contamination, in this …

A new block low-rank channel estimation algorithm is put forward for the problem that the
complexity of the channel estimator matrix inverse computation for the traditional linear …

In this paper, a channel estimation method based on superimposed pilots with second-order
statistics is proposed for multi-cell Multiple-Input, Multiple-Output (MIMO) networks. In the …

In this Letter, a compressed channel estimation approach referred to as burst weighted‐
minimisation is proposed to alleviate the overwhelming pilot overhead in the downlink path …

Recently, compressive sensing based massive multiple input multiple output (MIMO)
channel estimation (CE) has attracted intensive research interest. By exploiting the …

Channel estimation is a crucial part of massive MIMO systems and without accurate channel
state information, the promising array and multiplexing gains cannot be achieved. In typical …

In massive MIMO systems, different wireless channels starting from the same user antenna
to different base station (BS) antennas usually share a common channel support. In this …

Massive multiple-input multiple-output (MIMO) is a powerful supporting technology to meet
the energy/spectral efficiency and reliability requirement of Internet of Things (IoT) network …