Low-Power Communication Signal Enhancement Method of Internet of Things Based on Nonlocal Mean Denoising

• Published in: Wireless communications and mobile computing Vol. 2022; pp. 1 - 9
• Main Authors: Tian, Mingchuan, Liu, Jizheng
• Format: Journal Article
• Language: English
• Published: Oxford Hindawi 30.07.2022; Hindawi Limited
• Subjects: Algorithms; Bit error rate; Communication; Deep learning; Feature recognition; Internet of Things; Methods; Neural networks; Noise reduction; Semantics; Signal to noise ratio
• ISSN: 1530-8669; 1530-8677
• DOI: 10.1155/2022/5167639

In order to improve the transmission effect of low-power communication signal of Internet of Things and compress the enhancement time of low-power communication signal, this paper designs a low-power communication signal enhancement method of Internet of Things based on nonlocal mean denoising. Firstly, the residual of one-dimensional communication layer is preprocessed by convolution core to obtain the residual of one-dimensional communication layer. Then, according to the two classification recognition methods, the noise reduction signal feature recognition of the low-power communication signal of the Internet of Things is realized, the nonlocal mean noise reduction algorithm is used to remove the low-power communication signal of the Internet of Things, and the weight value between similar blocks is calculated according to the European distance method. Finally, the low-power communication signal enhancement of the Internet of Things is realized by the nonlocal mean value denoising method. The experimental results show that the communication signal enhancement time overhead of this method is low, which is always less than 2.6 s. The lowest bit error rate after signal enhancement is about 1%, and the signal-to-noise ratio is up to 18 dB, which shows that this method can achieve signal enhancement.