Recurrent Neural Networks for Transmission Opportunity Forecasting
2016 IEEE 13th International Conference on Mobile Ad Hoc and Sensor Systems (MASS)(2016)
摘要
One of the major challenges in opportunistic networks is the correct identification of a transmission opportunity and its corresponding duration. In this work, recurrent neural network structures are investigated for transmission opportunity forecast. The proposed method is based on in-channel spectrum sensing and the use of Elman recurrent neural network to model the occupation of the channel. The results, based on real experiment measurements, using a Software Defined Radio for monitoring of a Wi-Fi channel shows that, in the evaluated scenarios even a small neural network is able to achieves 99.9% of correct transmission opportunity identification rate.
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关键词
Opportunity forecasting,recurrent neural networks,wifi 802.11
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