In this paper, we explore low-complexity probabilistic algorithms for soft symbol detection in
high-dimensional multiple-input multiple-output (MIMO) systems. We present a novel …

Antenna selection in Multiple-Input Multiple-Output (MIMO) systems has attracted increasing
attention due to the challenge of keeping a balance between communication performance …

Traditional symbol detection algorithms either perform poorly or are impractical to implement
for Massive Multiple-Input Multiple-Output (MIMO) systems. Recently, several learning …

In this paper, simple methodologies of deep learning application to conventional multiple-
input multiple-output (MIMO) communication systems are presented. The deep learning …

The deep convolutional neural network (CNN) is exploited in this work to conduct the
challenging channel estimation for mmWave massive multiple input multiple output (MIMO) …

Accurate and efficient estimation of the high dimensional channels is one of the critical
challenges for practical applications of massive multiple-input multiple-output (MIMO). In the …

Large scale multiple-input multiple-output (MIMO) or Massive MIMO is one of the pivotal
technologies for future wireless networks. However, the performance of massive MIMO …

Inspired by the phenomenon that the received signals naturally form clusters, we propose a
novel machine learning framework to design multi-input multi-output (MIMO) communication …

In this paper, we develop a dynamic detection network (DDNet) based detector for multiple-
input multiple-output (MIMO) systems. By constructing an improved DetNet (IDetNet) …

Deep neural networks (NNs) have exhibited considerable potential for efficiently balancing
the performance and complexity of multiple-input and multiple-output (MIMO) detectors …