Tracking Performance of Semi-Supervised Large MarginClassifiers in Automatic Modulation Classification

  • سال انتشار: 1393
  • محل انتشار: فصلنامه سیستم های اطلاعاتی و مخابرات، دوره: 2، شماره: 8
  • کد COI اختصاصی: JR_JIST-2-8_005
  • زبان مقاله: انگلیسی
  • تعداد مشاهده: 479
دانلود فایل این مقاله

نویسندگان

Hamidreza Hosseinzadeh

Department of Electrical and Computer Engineering, Islamic Azad University, Science and Research Branch, Tehran, Iran

Farbod Razzazi

Department of Electrical and Computer Engineering, Islamic Azad University, Science and Research Branch, Tehran, Iran

Afrooz Haghbin

Department of Electrical and Computer Engineering, Islamic Azad University, Science and Research Branch, Tehran, Iran

چکیده

Automatic modulation classification (AMC) in detected signals is an intermediate step between signal detection and demodulation, and is also an essential task for an intelligent receiver in various civil and military applications. In this paper, we propose a semi-supervised Large margin AMC and evaluate it on tracking the received signal to noise ratio (SNR) changes to classify most popular single carrier modulations in non-stationary environments. To achieve this objective, two structures for self-training of large margin classifiers were developed in additive white Gaussian noise (AWGN) channels with priori unknown SNR. A suitable combination of the higher order statistics (HOS) and instantaneous characteristics of digital modulation are selected as effective features. We investigated the robustness of the proposed classifiers with respect to different SNRs of the received signals via simulation results and we have shown that adding unlabeled input samples to the training set, improve the tracking capacity of the presented system to robust against environmental SNR changes. The performance of the automatic modulation classifier is presented in the form of k-fold cross-validation test, classification accuracy and confusion matrix methods. Simulation results show that the proposed approach is capable to classify the modulation class in unknown variable noise environment at even low SNRs.

کلیدواژه ها

Automatic Modulation Classification; AMC; Tracking Performance Evaluation; Passive-Aggressive Classifier; Self Training; Semi-Supervised Learning

مقالات مرتبط جدید

اطلاعات بیشتر در مورد COI

COI مخفف عبارت CIVILICA Object Identifier به معنی شناسه سیویلیکا برای اسناد است. COI کدی است که مطابق محل انتشار، به مقالات کنفرانسها و ژورنالهای داخل کشور به هنگام نمایه سازی بر روی پایگاه استنادی سیویلیکا اختصاص می یابد.

کد COI به مفهوم کد ملی اسناد نمایه شده در سیویلیکا است و کدی یکتا و ثابت است و به همین دلیل همواره قابلیت استناد و پیگیری دارد.