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An M-QAM Signal Modulation Recognition Algorithm in AWGN Channel
المؤلفون المشاركون
المصدر
العدد
المجلد 2019، العدد 2019 (31 ديسمبر/كانون الأول 2019)، ص ص. 1-17، 17ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2019-05-12
دولة النشر
مصر
عدد الصفحات
17
التخصصات الرئيسية
الملخص EN
Computing the distinct features from input data, before the classification, is a part of complexity to the methods of automatic modulation classification (AMC) which deals with modulation classification and is a pattern recognition problem.
However, the algorithms that focus on multilevel quadrature amplitude modulation (M-QAM) which underneath different channel scenarios is well detailed.
A search of the literature revealed that few studies were performed on the classification of high-order M-QAM modulation schemes such as 128-QAM, 256-QAM, 512-QAM, and 1024-QAM.
This work focuses on the investigation of the powerful capability of the natural logarithmic properties and the possibility of extracting higher order cumulant’s (HOC) features from input data received raw.
The HOC signals were extracted under the additive white Gaussian noise (AWGN) channel with four effective parameters which were defined to distinguish the types of modulation from the set: 4-QAM∼1024-QAM.
This approach makes the classifier more intelligent and improves the success rate of classification.
The simulation results manifest that a very good classification rate is achieved at a low SNR of 5 dB, which was performed under conditions of statistical noisy channel models.
This shows the potential of the logarithmic classifier model for the application of M-QAM signal classification.
furthermore, most results were promising and showed that the logarithmic classifier works well under both AWGN and different fading channels, as well as it can achieve a reliable recognition rate even at a lower signal-to-noise ratio (less than zero).
It can be considered as an integrated automatic modulation classification (AMC) system in order to identify the higher order of M-QAM signals that has a unique logarithmic classifier to represent higher versatility.
Hence, it has a superior performance in all previous works in automatic modulation identification systems.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Ali, Ahmed K.& Erçelebi, Ergun. 2019. An M-QAM Signal Modulation Recognition Algorithm in AWGN Channel. Scientific Programming،Vol. 2019, no. 2019, pp.1-17.
https://search.emarefa.net/detail/BIM-1210747
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Ali, Ahmed K.& Erçelebi, Ergun. An M-QAM Signal Modulation Recognition Algorithm in AWGN Channel. Scientific Programming No. 2019 (2019), pp.1-17.
https://search.emarefa.net/detail/BIM-1210747
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Ali, Ahmed K.& Erçelebi, Ergun. An M-QAM Signal Modulation Recognition Algorithm in AWGN Channel. Scientific Programming. 2019. Vol. 2019, no. 2019, pp.1-17.
https://search.emarefa.net/detail/BIM-1210747
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1210747
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
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