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.
@article{arxiv.1811.09346,
title = {Deep Neural Network Aided Scenario Identification in Wireless Multi-path Fading Channels},
author = {Jun Liu and Kai Mei and Dongtang Ma and Jibo Wei},
journal= {arXiv preprint arXiv:1811.09346},
year = {2018}
}