… outperform existing symbol detection methods for MIMO-OFDM systems in … detection in
MIMOOFDM systems, which takes advantage of the inherent structure knowledge of MIMO-OFDM …

… II, the system model of MIMO-OFDM and conventional symbol detection methods are introduced.
… the underlying neural networks for symbol detection while we are focusing on using the …

… Building on a reservoir computing (RC)-based approach towards symbol detection, we …
neural network to take advantage of the structure knowledge inherent in the MIMO-OFDM system. …

… neural network (DNN) and adopted to achieve minimum bit error rates in IM-MIMO-OFDM …
of employing a DNN for the detection of IM-MIMO-OFDM in terms of error performance. The …

… symbol detection against Doppler frequency shift [28]–[30]. In this article, … neural network
and deep learning based methods are introduced innovatively to replace interpolation method …

… On the other hand, deep neural networks (DNNs) have … Since the optimum decoding for
MIMO-OFDM-IM grows … connected DNN based MIMO-OFDM-IM to jointly detect the transmitted …

… connected deep neural network (FC-DNN), which is more robust than traditional methods in
… We develop a model-driven NN for MIMO-OFDM signal detection in this paper based on the …

… coherent detection. The performance of the … NETWORK In this section, we will consider
the problem of sequential estimation of MIMO-OFDM multipath channel within a neural network …

… aided OFDM receivers, proposed deep neural networks has … deep neural networks and
training method in OFDM channel … With proposed SimNet, we made OFDM detection in the …

… The contribution of this work is a novel deep convolutional neural network (DCNN) approach
for AMC in MIMO-OFDM systems, … The MMSE MIMO detection multiplies ym[n] by Q[n]: …