6,329 research outputs found

    Non-Linear Digital Self-Interference Cancellation for In-Band Full-Duplex Radios Using Neural Networks

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    Full-duplex systems require very strong self-interference cancellation in order to operate correctly and a significant part of the self-interference signal is due to non-linear effects created by various transceiver impairments. As such, linear cancellation alone is usually not sufficient and sophisticated non-linear cancellation algorithms have been proposed in the literature. In this work, we investigate the use of a neural network as an alternative to the traditional non-linear cancellation method that is based on polynomial basis functions. Measurement results from a full-duplex testbed demonstrate that a small and simple feed-forward neural network canceler works exceptionally well, as it can match the performance of the polynomial non-linear canceler with significantly lower computational complexity.Comment: Presented at the IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) 201

    Performance Evaluation of Channel Decoding With Deep Neural Networks

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    With the demand of high data rate and low latency in fifth generation (5G), deep neural network decoder (NND) has become a promising candidate due to its capability of one-shot decoding and parallel computing. In this paper, three types of NND, i.e., multi-layer perceptron (MLP), convolution neural network (CNN) and recurrent neural network (RNN), are proposed with the same parameter magnitude. The performance of these deep neural networks are evaluated through extensive simulation. Numerical results show that RNN has the best decoding performance, yet at the price of the highest computational overhead. Moreover, we find there exists a saturation length for each type of neural network, which is caused by their restricted learning abilities.Comment: 6 pages, 11 figures, Latex; typos corrected; IEEE ICC 2018 to appea

    Impaired Auditory Temporal Selectivity in the Inferior Colliculus of Aged Mongolian Gerbils

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    Aged humans show severe difficulties in temporal auditory processing tasks (e.g., speech recognition in noise, low-frequency sound localization, gap detection). A degradation of auditory function with age is also evident in experimental animals. To investigate age-related changes in temporal processing, we compared extracellular responses to temporally variable pulse trains and human speech in the inferior colliculus of young adult (3 month) and aged (3 years) Mongolian gerbils. We observed a significant decrease of selectivity to the pulse trains in neuronal responses from aged animals. This decrease in selectivity led, on the population level, to an increase in signal correlations and therefore a decrease in heterogeneity of temporal receptive fields and a decreased efficiency in encoding of speech signals. A decrease in selectivity to temporal modulations is consistent with a downregulation of the inhibitory transmitter system in aged animals. These alterations in temporal processing could underlie declines in the aging auditory system, which are unrelated to peripheral hearing loss. These declines cannot be compensated by traditional hearing aids (that rely on amplification of sound) but may rather require pharmacological treatment

    DivGraphPointer: A Graph Pointer Network for Extracting Diverse Keyphrases

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    Keyphrase extraction from documents is useful to a variety of applications such as information retrieval and document summarization. This paper presents an end-to-end method called DivGraphPointer for extracting a set of diversified keyphrases from a document. DivGraphPointer combines the advantages of traditional graph-based ranking methods and recent neural network-based approaches. Specifically, given a document, a word graph is constructed from the document based on word proximity and is encoded with graph convolutional networks, which effectively capture document-level word salience by modeling long-range dependency between words in the document and aggregating multiple appearances of identical words into one node. Furthermore, we propose a diversified point network to generate a set of diverse keyphrases out of the word graph in the decoding process. Experimental results on five benchmark data sets show that our proposed method significantly outperforms the existing state-of-the-art approaches.Comment: Accepted to SIGIR 201
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