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 …

This paper proposes a novel tensor-train deep neural network (TT-DNN) based channel
estimator to tackle challenges of time-varying channel estimation in multiple-input multiple …

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 …

Incorporating deep learning (DL) into multiple-input multiple-output (MIMO) detection has
been deemed as a promising technique for future wireless communications. However, most …

As future wireless systems trend towards higher carrier frequencies and large antenna
arrays, receivers with one-bit analog-to-digital converters (ADCs) are being explored owing …

In this paper, we propose a model-driven deep learning network for multiple-input multiple-
output (MIMO) detection. The structure of the network is specially designed by unfolding the …

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 …

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 …

Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing (MIMO-OFDM)
system is a promising technology that provides high capacity and high data rate …