Wi-LADL: A Wireless-Based Lightweight Attention Deep Learning Method for Human–Vehicle Recognition

Song, Mingxin; Lou, Liangliang; Chen, Xinquan; Zhao, Xiaoming; Hong, Yuanyuan; Zhang, Shiqing; He, Wei

doi:10.1109/jsen.2022.3232243

Cited by 7 publications

(1 citation statement)

References 46 publications

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“…Consequently, there is an urgent need for research focused on lightweight neural network models applicable to modulation recognition tasks [16]. To deploy CNN models more effectively on edge devices, it is common practice to compress the model [17], so that the network carries fewer parameters and can simultaneously solve problems related to memory and computation speed [18]. Various methods have been proposed for model compression, such as the hybrid pruning method combining weight and convolution kernel pruning [19].…”

Section: Introductionmentioning

confidence: 99%

CSCNN: Lightweight Modulation Recognition Model for Mobile Multimedia Intelligent Information Processing

Chen,

Huang,

Zhang

et al. 2024

Mobile Netw Appl

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With the advancement of the Internet of Things, the importance of multimedia intelligent information processing technology is increasing. Faced with massive electromagnetic data and limited terminal equipment computing resources, previous technologies cannot meet the real-time decisionmaking requirements for processing short-term observations or burst signals in deployable systems. In this paper, our approach proposes the Deep Complex Separable Convolution (DCSC) operation by combining separable convolution operation and complex convolution operation. At the same time, to better preserve coupling information between channels and minimize the model size, we propose the Multilevel Separable Convolutional Residual Block (MSCRB). Based on the above two methods, we constructed the Complex Separable Convolutional Neural Network (CSCNN). This neural network significantly reduces the complexity of the deep learning model. The smallest network we constructed, CSCNN-Tiny, has a model size of 0.760M, which is only 6% of the size of MobileNet. With 0.815M Flops, it is 3.8% of MobileNet. However, it achieves a recognition accuracy of 50.97%, only 0.97% lower than MobileNet.

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