In this study, we consider the application of deep learning (DL) to tabu search (TS) detection
in large multiple-input multiple-output (MIMO) systems. First, we propose a deep neural …

This paper investigates the optimal signal detection problem with a particular interest in
large-scale multiple-input multiple-output (MIMO) systems. The problem is NP-hard and can …

Optimal symbol detection for multiple-input multiple-output (MIMO) systems is known to be
an NP-hard problem. Conventional heuristic algorithms are either too complex to be …

This paper proposes a novel learning to learn method, called learning to learn iterative
search algorithm (LISA), for signal detection in a multi-input multi-output (MIMO) system. The …

In this paper, we investigate the model-driven deep learning (DL) for MIMO detection. In
particular, the MIMO detector is specially designed by unfolding an iterative algorithm and …

Multiple-input multiple-output (MIMO) system is the key technology for long term evolution
(LTE) and 5G. The information detection problem at the receiver side is in general difficult …

This paper presents a low-complexity deep neural network (DNN) based multiple-input-
multiple-output (MIMO) detector for the BPSK and QPSK constellation cases. We employ …

The great success of deep learning (DL) has inspired researchers to develop more accurate
and efficient symbol detectors for multi-input multi-output (MIMO) systems. Existing DL …

To overcome the influence of channel estimation error on signal detection, this paper
presents a model-driven deep learning method for joint channel estimation and signal …

Baek et al. proposed a deep learning-based signal detector for conventional MIMO systems,
which is a pioneering work of applying artificial intelligence into wireless communications …