M-QAM Demodulation based on Machine Learning
Roberto Neves Toledo, Cristiano Akamine, Fadi Jerji, Leandro Augusto da Silva
Abstract
Roberto Neves Toledo, Cristiano Akamine, Fadi Jerji, Leandro Augusto da Silva
Abstract
This paper presents a new Quadrature Amplitude Modulation (M-QAM) demodulation method using Machine Learning techniques. The new method significantly reduces the demodulation complexity for high-order constellations while maintains the demodulation accuracy. The experimental results demonstrate a performance gain of up to 1485% for 4096-QAM in comparison with the classical Log-Likelihood Ratio demodulator.
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This paper presents a new Quadrature Amplitude Modulation (M-QAM) demodulation method using Machine Learning techniques. The new method significantly reduces the demodulation complexity for high-order constellations while maintains the demodulation accuracy. The experimental results demonstrate a performance gain of up to 1485% for 4096-QAM in comparison with the classical Log-Likelihood Ratio demodulator.
Key concepts: Demodulation, Quadrature amplitude modulation, QAM, Computer science, Electronic engineering, Constellation, Modulation (music), Amplitude modulation