使用深度学习的情境感知认知无线电

2017 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN) Pub Date : 2017-03-06 DOI:10.1109/DySPAN.2017.7920784

Francisco Paisana, Ahmed A. S. Seleim, Maicon Kist, Pedro Alvarez, J. Tallon, Christian Blümm, André Puschmann, L. Dasilva

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引用次数: 29

摘要

本文介绍了用于IEEE DySPAN挑战赛2017的辅助用户(SU)链路的设计、实验评估和软件定义无线电(SDR)实现。目标是成功地识别主用户(PU)的行为并与之共存，主用户的通道访问模式随时间而变化。为此，我们利用传感、深度学习和动态优化。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Context-aware cognitive radio using deep learning

This paper describes the design, experimental assessmant and Software Defined Radio (SDR) implementation of a Secondary User (SU) link for the IEEE DySPAN Challenge 2017. The objective is to successfully discern the behavior of and coexist with a Primary User (PU), whose channel access patterns vary over time. For that end, we utilize sensing, deep learning and dynamic optimization.

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2017 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN)

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