Deep Neural Network Aided Scenario Identification in Wireless Multi-path Fading Channels
This work addresses scenario identification for wireless communication systems, but it appears incremental as it applies an existing DNN method to a specific domain with new data.
The paper tackled the problem of identifying wireless channel scenarios in multi-path fading conditions using a deep neural network, achieving 100% accuracy at SNR over 20dB and 88.4% average accuracy across a 0-40dB SNR range.
This letter illustrates our preliminary works in deep nerual network (DNN) for wireless communication scenario identification in wireless multi-path fading channels. In this letter, six kinds of channel scenarios referring to COST 207 channel model have been performed. 100% identification accuracy has been observed given signal-to-noise (SNR) over 20dB whereas a 88.4% average accuracy has been obtained where SNR ranged from 0dB to 40dB. The proposed method has tested under fast time-varying conditions, which were similar with real world wireless multi-path fading channels, enabling it to work feasibly in practical scenario identification.