… In this section, we present a MIMO-OFDM system that comprises a transmitter sending signals
to a … creating an N T × N R MIMO channel model that is modeled by the 5G channel profile. …

… —Machine learning (ML) starts to be widely used to enhance the performance of multi-user
multiple-input multipleoutput (MU-MIMO… models, MU-MIMO receive algorithms must allow for …

… on machine learning for multiple input multiple output (MIMO) - orthogonal frequency division
multiplexing (OFDM… The learning of proposed AM model is based on the generated training …

… In this paper, we exploit the multidimensional characteristics of MIMO orthogonal frequency
division multiplexing (OFDM) systems and propose a machine learning (ML) method …

… interests in applying machine learning (ML) techniques to … -based MIMO symbol detectors
usually adopt offline learning … -time symbol detection task in MIMO-OFDM systems. Two novel …

… a machine learning based broadband hybrid precoding for mmWave massive MIMO with
DS… frequency-selective precoders for fully-digital MIMO. Moreover, we extend the PCAbased …

… In this section machine learning is applied to AMC in the MIMO-OFDM system described in
… to machine learning is provided as well as the connection between machine learning and …

… -OFDM) systems with the objective of minimizing the memory cost. In [31], the … MIMO-OFDM
systems based on limited channel state information (CSI). In [32] and [33], machine learning…

… We first simulate the Autoencoder-SVM framework in MIMO-OFDM systems to evaluate the
performance of proposed link adaptation method which is based on the SINRs. Since the set …

… Recently, there have been new flexible approaches to use machine learning algorithms for
effective link adaptation. Machine learning algorithms are inherently data-driven, and rather …