2020•Unpublished venueRequires access

M-QAM Demodulation based on Machine Learning

Roberto Neves Toledo, Cristiano Akamine, Fadi Jerji, Leandro Augusto da Silva

Open publisher page 9 citations

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.

About this research paper

What this paper is about

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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OpenAlex reports 9 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available 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.

Key concepts: Demodulation, Quadrature amplitude modulation, QAM, Computer science, Electronic engineering, Constellation, Modulation (music), Amplitude modulation

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