The current applications’ requirement for higher data rates typically results in an increase in the signal’s Peak-to-Average Power Ratio (PAPR), which harms the transmitter’s Power Amplifier (PA) efficiency. The Magnitude Modulation (MM) technique was developed to address this issue in bandlimited single-carrier (SC) communications, leaving a trade-off between the attainable PAPR reduction and an increase in the bit error rate (BER) upon MM signal demodulation. Because Deep Learning (DL) algorithms are proving to be applicable in the majority of fields, two models based on current state-of-the-art Artificial Neural Networks (ANNs) are proposed in this paper to replace conventional demodulators. It is also shown that DL demodulators can leverage on the symbols’ memory that is embedded within the transmitted MM signal, caused by the MM procedure. Both systems’ BER performance is analyzed and compared, demonstrating that it is possible to mitigate the MM effect on demodulation.
Deep Learning-based Demodulator for Magnitude Modulated Signals
2023-06-01
2521991 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Demodulator for AM and SSB-SC Signals
NTRS | 1984
|Buffered Telemetry Demodulator
Online Contents | 1995
Digital Synchronous Demodulator
NTRS | 1992
|Buffered Telemetry Demodulator
NTRS | 1995
|