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  5. Channel-Adaptive Location-Assisted Wake-up Signal Detection Approach Based on LFM Over Underwater Acoustic Channels

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Article
English
2019

Channel-Adaptive Location-Assisted Wake-up Signal Detection Approach Based on LFM Over Underwater Acoustic Channels

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English
2019
IEEE Access
Vol 7
DOI: 10.1109/access.2019.2926531

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Deqing Wang
Deqing Wang

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Deqing Wang
Haiyu Li
Yongjun Xie
+2 more

Abstract

This paper focuses on the wake-up signal detection design for underwater acoustic communication (UAC) terminals. A wake-up signal detection unit can considerably reduce the power consumption of the terminals. Compared with terrestrial wireless counterparts, the wake-up signal detection design for UAC terminals is challenged by the severe underwater acoustic channels, which is characterized as doubly selective fading and low signal-to-noise ratio (SNR). This paper proposes a wake-up signal detection approach called channel-adaptive detection and location-assisted joint decision (ChAD-LaJD), for UAC terminals. ChAD-LaJD applies a group of linear frequency modulation (LFM) signals as a wake-up signal. In order to increase the detection probability while keeping a low false alarm rate, ChAD-LaJD consists of two procedures: channel-adaptive detection (ChAD) and location-assisted joint decision (LaJD). Besides a pre-determined threshold, ChAD procedure defines two special parameters which reflect instantaneous channel states to detect wake-up signals adaptively. LaJD procedure further exploits the location relationships of LFM signals detected by ChAD to achieve a joint decision. The simulations and field experiments are conducted to evaluate the performance of ChAD-LaJD. The results show that ChAD-LaJD outperforms the conventional methods that consider a fixed threshold (FixTh) and/or constant false alarm rate (CFAR).

How to cite this publication

Deqing Wang, Haiyu Li, Yongjun Xie, Xiaoyi Hu, Liqun Fu (2019). Channel-Adaptive Location-Assisted Wake-up Signal Detection Approach Based on LFM Over Underwater Acoustic Channels. IEEE Access, 7, pp. 93806-93819, DOI: 10.1109/access.2019.2926531.

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Publication Details

Type

Article

Year

2019

Authors

5

Datasets

0

Total Files

0

Language

English

Journal

IEEE Access

DOI

10.1109/access.2019.2926531

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