In this paper, we investigate signal detection in emerging dynamic spatial modulation (DSM)
based MIMO systems, where the existing mapping and detection methods do not work …

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 …

We investigate the optimal signal detection problem in large-scale multiple-input multiple-
output (MIMO) system with the generalized spatial modulation (GSM) scheme, which can be …

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 …

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 …

Spatial Modulation (SM) is a recently developed low-complexity Multiple-Input Multiple-
Output scheme that jointly uses antenna indices and a conventional signal set to convey …

This paper aims to devise a generalized maximum likelihood (ML) estimator to robustly
detect signals with unknown noise statistics in multiple-input multiple-output (MIMO) …

In this paper, we investigate the classical detection problem for vehicle networks with
multiple antennas, by considering practical communication scenarios, where the interfering …

The emerging massive/large-scale multiple-input multiple-output (LS-MIMO) systems that
rely on very large antenna arrays have become a hot topic of wireless communications …

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 …