A Convolution Neural Network based QPSK and 16QAM Modulations Simulator for a Multiuser MIMO-OFDM Transmission Simulation over a Nakagami-m Fading Channel

Wireless communication has been a widespread method of data transfer in the modern era. Various modulation techniques have been found and among them 16QAM has proven to be the most efficient. This study provides a Convolutional Neural Network (CNN) approach for simulating 16QAM and QPSK modulation s...

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Bibliographic Details
Published in2023 5th International Conference on Sustainable Technologies for Industry 5.0 (STI) pp. 1 - 6
Main Authors Ahmed, Mashrukh, Rabiul Alam, Md. Golam
Format Conference Proceeding
LanguageEnglish
Published IEEE 09.12.2023
Subjects
16QAM
CNN
Fading channels
Keras
Libraries
MIMO
Modulation
Nakagami-m fading
Neural networks
OFDM
Phase shift keying
QPSK
Wireless communication
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Summary:Wireless communication has been a widespread method of data transfer in the modern era. Various modulation techniques have been found and among them 16QAM has proven to be the most efficient. This study provides a Convolutional Neural Network (CNN) approach for simulating 16QAM and QPSK modulation schemes over a multiuser MIMO-OFDM using the Keras library over Nakagami-m fading. It then examines the two modulation schemes to determine if QPSK might be a viable alternative to 16QAM. In this paper two analysis have been done, a realistic MIMO OFDM system simulation and weighted belief propagation decoding.
DOI:10.1109/STI59863.2023.10465002