همسان‌سازی کور کانال و کدبرداری توأم در سیستم OFDM روی کانال فرکانس گزین با استفاده از فیلتر ذره‌ای
محورهای موضوعی : مهندسی برق و کامپیوترنگین قاسمی 1 , محمدفرزان صباحي 2 , اميررضا فروزان 3
1 - دانشگاه اصفهان
2 - دانشگاه اصفهان
3 - دانشگاه اصفهان
کلید واژه: همسان‌سازی کور کانال سیستم OFDM فیلترهای ذره‌ای همسان‌سازی و کدبرداری توأم,
چکیده مقاله :
در این مقاله روشی جدید برای کدبرداری توأم با همسان‌سازی کور کانال در سیستم OFDM روی یک کانال فرکانس گزین ارائه شده است. الگوریتم پیشنهادی روشی بازگشتی برای محاسبه ترتیبی احتمال پسین به منظور آشکارسازی با معیار MAP ارائه می‌کند. این محاسبات ترتیبی در راستای اندیس‌ها در یک سمبل OFDM و به کمک فیلتر ذره‌ای صورت می‌گیرد. با استفاده از کدگذاری، معرفی شکل مناسبی از تابع اهمیتی، در نظر گرفتن یک مدل احتمال پیشین برای کانال مورد استفاده و انتگرال‌گیری روی آن، روشی برای انجام توأم کدبرداری و همسان‌سازی کانال بیان شده است. در این روش تنها به توزیع پیشین کانال و مرتبه آن نیاز است و داده ارسال‌شده بدون به دست آوردن ضرایب کانال تخمین زده می‌شود. عملکرد آشکارساز با روش کدبرداری توأم با همسان‌سازی کور کانال پیشنهادی به وسیله شبیه‌سازی کامپیوتری بررسی شده و نرخ خطای آن با آشکارساز توربو مقایسه شده که نتایج، کارایی بیشتر روش پیشنهادی را نشان می‌دهد.
In this paper a sequential algorithm is proposed for joint blind channel equalization and decoding for orthogonal frequency-division multiplexing (OFDM) in frequency selective channels. This algorithm offers a recursive method to sequentially calculate the posterior probability for maximum a posteriori (MAP) detection. Recursive calculations are done along the indexes in each OFDM symbol using a particle filter. By defining an appropriate importance function, and a proper prior probability distribution function for the channel tap coefficients (and marginalizing it), an efficient method is presented for joint equalization and channel decoding in OFDM based systems. Performance of the proposed detector is evaluated using computer simulations and its bit error rate is compared with the trained turbo equalizer and a conventional particle filter-based method. The results show that the proposed method outperforms the previously presented particle filter-based method without a need for training data.
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