آشکارسازی عمیق MIMO در حضور خطای تخمین کانال

نوع مقاله : مقاله پژوهشی

نویسندگان

مجتمع دانشگاهی برق و کامپیوتر - دانشگاه صنعتی مالک اشتر - تهران - ایران

چکیده

ظرفیت سیستم­های مخابرات بی­سیم را می­توان با بکار بردن چند آنتن در فرستنده و چند آنتن در گیرنده به نحو قابل‌ملاحظه‌ای افزایش داد. در چنین سیستم­هایی، که MIMO نام دارند، گیرنده با دانشی که از کانال دارد، به آشکارسازی سیگنال ارسالی می­پردازد. روش­های مختلفی برای آشکارسازی بهینه و زیربهینه سمبل­های ارسالی پیشنهاد شده است. اخیراً مفاهیم یادگیری عمیق و استفاده از شبکه­های عصبی به‌منظور آشکارسازی در حد بهینه و حجم محاسبات کمتر در فرآیند تست، در مقایسه با سایر روش­های سنتی پیشین، بکار گرفته شده است. درصورتی‌که اطلاعات کانال در گیرنده با خطا همراه باشد، کارایی این نوع آشکارساز کاهش یافته و درنتیجه نرخ خطای بیت افزایش می­یابد. با توجه به اینکه در عمل، گیرنده تخمینی از کانال میان فرستنده و گیرنده را در اختیار دارد و نه مقدار دقیق آن را، مقاله حاضر روشی بهبودیافته برای آشکارسازی مبتنی بر یادگیری عمیق و مقاوم در برابر خطای تخمین کانال، پیشنهاد می­کند. در این روش آشکارسازی با استفاده از ماتریس کوواریانس تخمین­گر کانال و استفاده از مفاهیم یادگیری عمیق، یک آشکارساز مقاوم در برابر خطای تخمین کانال پیشنهاد و به‌صورت تحلیلی مورد بررسی قرار گرفته است. نتایج شبیه‌سازی مبین کارایی روش پیشنهادی در سیستم‌های MIMO است.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Deep MIMO Detection with Imperfect CSI

نویسندگان [English]

  • Hossein Khaleghi Bizaki
  • Mehdi Tayyeb Masoud
Faculty of Electrical and Computer Engineering, Malek Ashtar University of Technology, Tehran, Iran
چکیده [English]

It is possible to noticeably increase the capacity of wireless communication systems through the use of multiple antennas both in the transmitter and in the receiver. In such systems, which are referred to in short as MIMO, the receiver uses its knowledge of the channel to detect the transmitted signal. Different methods have been proposed for optimal and sub-optimal detection of the transmitted signals. Recently, principles of deep learning and implementing neural networks have been employed as a near optimal approach for MIMO detection with fewer calculations during the testing process compared to traditional methods. In the event an error occurs in the receiver’s channel estimation process, this type of detector suffers a drop in performance and as a result, BER will increase. Given that in practice, the receiver only has an estimation of the CSI instead of the exact values, the current study presents an enhanced detection method based on deep learning, which is also robust against channel estimation error. In this detection method, by using the covariance matrix of the channel estimator and the principles of deep learning, a robust detector against channel estimation error is proposed and comprehensively evaluated. Numerical simulations confirm the performance of the proposed method.

کلیدواژه‌ها [English]

  • Multiple Input Multiple Output (MIMO)
  • Robust Detection
  • Deep learning
  • Channel Estimation Error
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