Performance analysis of a low-complexity MAP algorithm for automatic modulation classification in adaptive OFDM systems

Automatic modulation classification (AMC) techniques enable the estimation of the bit allocation table in adaptive orthogonal frequency division multiplexing (OFDM) systems. In this study, the authors investigate the classification performance of a low-complexity maximum-a-posteriori (MAP) algorithm...

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Bibliographic Details
Published in:IET communications Vol. 10; no. 17; pp. 2363 - 2371
Main Authors: Bahrani, Sima, Derakhtian, Mostafa, Zolghadrasli, Alireza
Format: Journal Article
Language:English
Published: The Institution of Engineering and Technology 24.11.2016
Subjects:
adaptive modulation
adaptive OFDM system
Adaptive systems
Algorithms
AMC technique
automatic modulation classification technique
Automation
bit allocation table estimation
Classification
low‐complexity MAP algorithm
low‐complexity maximum‐a‐posteriori algorithm
Mathematical models
maximum likelihood estimation
modified rate adaptive bit loading algorithm
Modulation
OFDM modulation
Orthogonal Frequency Division Multiplexing
Permissible error
probability
RA bit loading algorithm
signal‐to‐noise ratio
adaptive modulation
adaptive OFDM system
orthogonal frequency division multiplexing
probability
low-complexity maximum-a-posteriori algorithm
modified rate adaptive bit loading algorithm
maximum likelihood estimation
automatic modulation classification technique
AMC technique
bit allocation table estimation
RA bit loading algorithm
signal-to-noise ratio
OFDM modulation
low-complexity MAP algorithm
ISSN:1751-8628, 1751-8636
Online Access:Get full text
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Summary:Automatic modulation classification (AMC) techniques enable the estimation of the bit allocation table in adaptive orthogonal frequency division multiplexing (OFDM) systems. In this study, the authors investigate the classification performance of a low-complexity maximum-a-posteriori (MAP) algorithm for AMC. Also, they derive an approximation of the probability of classification error in a subcarrier for this classifier. Furthermore, based on these theoretical derivations, a novel modified rate adaptive (RA) bit loading algorithm with additional classification error constraint is proposed. Numerical results show that the authors’ proposed method outperforms the RA algorithm significantly in terms of classification performance, while providing reasonable data throughput specifically at higher signal-to-noise ratios.
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ISSN:1751-8628
1751-8636
DOI:10.1049/iet-com.2015.0768