Nonlinear Interference Mitigation via Deep Neural Networks
نویسندگان
چکیده
A neural-network-based approach is presented to efficiently implement digital backpropagation (DBP). For a 32×100 km fiber-optic link, the resulting “learned” DBP significantly reduces the complexity compared to conventional DBP implementations. OCIS codes: (060.0060) Fiber optics and optical communications, (060.2330) Fiber optics communications.
منابع مشابه
Singing-Voice Separation from Monaural Recordings using Deep Recurrent Neural Networks
Monaural source separation is important for many real world applications. It is challenging since only single channel information is available. In this paper, we explore using deep recurrent neural networks for singing voice separation from monaural recordings in a supervised setting. Deep recurrent neural networks with different temporal connections are explored. We propose jointly optimizing ...
متن کاملSinging-voice Separation Using Deep Recurrent Neural Networks
In this paper, we explore using deep recurrent neural networks for singing voice separation from monaural recordings in a supervised setting. We propose jointly optimizing the networks for multiple source signals by including the separation step as a nonlinear operation in the last layer. Discriminative training objectives are further explored to enhance the source to interference ratio. The al...
متن کاملCystoscopy Image Classication Using Deep Convolutional Neural Networks
In the past three decades, the use of smart methods in medical diagnostic systems has attractedthe attention of many researchers. However, no smart activity has been provided in the eld ofmedical image processing for diagnosis of bladder cancer through cystoscopy images despite the highprevalence in the world. In this paper, two well-known convolutional neural networks (CNNs) ...
متن کاملDeep Canonically Correlated LSTMs
We examine Deep Canonically Correlated LSTMs as a way to learn nonlinear transformations of variable length sequences and embed them into a correlated, fixed dimensional space. We use LSTMs to transform multi-view time-series data non-linearly while learning temporal relationships within the data. We then perform correlation analysis on the outputs of these neural networks to find a correlated ...
متن کاملDetecting Overlapping Communities in Social Networks using Deep Learning
In network analysis, a community is typically considered of as a group of nodes with a great density of edges among themselves and a low density of edges relative to other network parts. Detecting a community structure is important in any network analysis task, especially for revealing patterns between specified nodes. There is a variety of approaches presented in the literature for overlapping...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید
ثبت ناماگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید
ورودعنوان ژورنال:
- CoRR
دوره abs/1710.06234 شماره
صفحات -
تاریخ انتشار 2017