یک روش کارآمد برای تشخیص مدولاسیون سیگنال‌های MPSK در کانال‌های محو
محورهای موضوعی : مهندسی برق و کامپیوتر
1 - دانشگاه فردوسی مشهد
کلید واژه: الگوریتم زنبور تشخیص نوع مدولاسیون شبکه عصبی,
چکیده مقاله :
شناسایی خودکار نوع مدولاسیون سیگنال‌های دیجیتال برای سامانه‌های مخابرات هوشمند یک ضرورت است. اغلب روش‌های طبقه‌بندی خودکار نوع سیگنال‌های دیجیتال مسئله را در کانال‌های نویز سفید جمع‌شونده بررسی می‌کنند. با این وجود محیط‌های مخابره واقعی از جمله کانال‌های مخابرات بی‌سیم، با اثرات محوشوندگی روبه‌رو هستند. تعداد بسیار کمی از روش‌ها برای کانال‌های محوشدگی ارائه شده است. این مقاله یک روش بسیار کارامد برای شناسایی نوع سیگنال‌های دیجیتال کلیدزنی جابه‌جایی فاز Mتایی پیشنهاد می‌دهد. روش ارائه‌شده ترکیبی ابتکاری است متشکل از شبکه عصبی پرسپترون چندلایه به عنوان طبقه‌بند و الگوریتم زنبور به عنوان بهینه‌ساز و همچنین یک همسان‌ساز برای کاهش اثرات کانال استفاده شده است. یک ترکیب مناسب از آمارگان مرتبه بالا تا مرتبه هشت، به عنوان مشخصه‌های سیگنال‌ها در نظر گرفته شده است. نتایج شبیه‌سازی کارامدی بالای تکنیک ارائه‌شده را برای تشخیص نوع سیگنال‌های دیجیتال، حتی در نسبت‌های سیگنال به نویز پایین، تأیید می‌کنند.
Automatic modulation recognition of digital signals is an essential for intelligent communication systems. Most automatic classifications of digital signal types deal with recognizing signals formats in presence of additive white Gaussian noise (AWGN) in channels. However, real world communication environments, such as wireless communication channels, suffer from fading effects. There are few methods proposed to perform in fading channels. This paper presents a high efficient method for identification of M-array phase shift keying (MPSK) digital signal type. The proposed method is heuristic hybrid, formed by a multilayer perceptron (MLP) neural network as the classifier and the bees algorithm (BA) as the optimizer. An equalizer is also used to reduce channel effects. A suitable combination of higher order statistics, up to eighth, is considered as prominent characteristics of signals. Simulation results validate the high efficiency of the proposed technique in recognizing the types of digital signals even at low SNRs.
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