Signal classification based on self-attention mechanism in unlicensed bands

Shuojia Liu; Rui Zhao

doi:10.1117/12.2684658

19 October 2023 Signal classification based on self-attention mechanism in unlicensed bands

Shuojia Liu, Rui Zhao

Proceedings Volume 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023); 127090V (2023) https://doi.org/10.1117/12.2684658
Event: Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 2023, Nanjing, China

Abstract

With the rapid development of the Internet era, the demand for data traffic has exploded, and this dramatic growth has brought higher requirements for spectrum resources. Unlicensed long term evolution (LTE-U) technology is considered one of promising solutions to address the problem. However, the unlicensed band is rich resource, which is mainly occupied by wireless fidelity (WiFi). The biggest challenge for the LTE-U technology is how to achieve friendly coexistentence with WiFi. Many Researchers in academia and industry have proposed various solutions to deal with this problem. In this paper, we propose a classification method based on self-attention mechanism algorithm in order to distinguish unlicensed LTE and WiFi to further realize the friendly coexistence of the two signals. The proposed algorithm can enhance the feature extraction capability of the model by fusing the features extracted by the convolutional neural network through the self-attentive mechanism. Simulations show that our proposed algorithm has higher accuracy and stability compared to other algorithms at the same signal-to-noise ratio. When the signal-to-noise ratio is higher than 0 dB, the recognition accuracy can reach above 90%. Especially when SNR id -5 dB, the accuracy of self-attention network is 12% higher than CNN’s.

Citation Download Citation

Shuojia Liu and Rui Zhao "Signal classification based on self-attention mechanism in unlicensed bands", Proc. SPIE 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 127090V (19 October 2023); https://doi.org/10.1117/12.2684658

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