Simple and Robust Log-Likelihood Ratio Calculation of Coded MPSK Signals in Wireless Sensor Networks for Healthcare

Xie, Bo; Ma, Congfang; Li, Haiqiong; Zhang, Gaoyuan; Han, Congzheng

doi:10.3390/app12052330

Cited by 2 publications

(1 citation statement)

References 27 publications

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“…Wireless sensor networks (WSNs) is a multi-hop self-organizing network composed of a large number of sensor nodes used for monitoring physical events [1]. Due to their outstanding performance advantages, they play a crucial role in various fields such as environmental monitoring, healthcare, agriculture, and urban traffic [2][3][4][5], contributing significantly to the development and application of Internet of Things (IoT) technology.…”

Section: Introductionmentioning

confidence: 99%

Coverage Optimization of WSNs Based on Enhanced Multi-Objective Salp Swarm Algorithm

Yang,

Mei,

Zhu

et al. 2023

Applied Sciences

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In complex two-dimensional monitoring environments, how to enhance network efficiency and network lifespan while utilizing limited energy resources, and ensuring that wireless sensor networks achieve the required partial coverage of the monitoring area, are the challenges of optimizing coverage in wireless sensor networks.With the premise of ensuring connectivity in the target network area, an enhanced multi-objective salp swarm algorithm based on non-dominated sorting (EMSSA) is proposed in this paper, by jointly optimizing network coverage, node utilization, and network energy balance objectives. Firstly, the logistic chaotic mapping is used to maintain the diversity of the initial salp swarm population. Secondly, to balance global and local search capabilities, a new dynamic convergence factor is introduced. Finally, to escape local optima more effectively, a follower updating strategy is implemented to reduce the blind following of followers while retaining superior individual information. The effectiveness of the strategy is validated through comparative experiments on ZDT and DTLZ test functions, and the proposed algorithm is applied to coverage optimization in WSNs in complex environments. The results demonstrate that the algorithm can adjust coverage thresholds according to different application requirements, providing various effective coverage optimization configurations. With the same preset requirements for partial coverage achieved, both network efficiency and lifespan have been significantly improved.

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Section: Introductionmentioning

confidence: 99%

Coverage Optimization of WSNs Based on Enhanced Multi-Objective Salp Swarm Algorithm

Yang,

Mei,

Zhu

et al. 2023

Applied Sciences

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Automatic Modulation Recognition Based on Multimodal Information Processing: A New Approach and Application

Zhang,

Xue,

Yao

et al. 2024

Electronics

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Automatic modulation recognition (AMR) has wide applications in the fields of wireless communications, radar systems, and intelligent sensor networks. The existing deep learning-based modulation recognition models often focus on temporal features while overlooking the interrelations and spatio-temporal relationships among different types of signals. To overcome these limitations, a hybrid neural network based on a multimodal parallel structure, called the multimodal parallel hybrid neural network (MPHNN), is proposed to improve the recognition accuracy. The algorithm first preprocesses the data by parallelly processing the multimodal forms of the modulated signals before inputting them into the network. Subsequently, by combining Convolutional Neural Networks (CNN) and Bidirectional Gated Recurrent Unit (Bi-GRU) models, the CNN is used to extract spatial features of the received signals, while the Bi-GRU transmits previous state information of the time series to the current state to capture temporal features. Finally, the Convolutional Block Attention Module (CBAM) and Multi-Head Self-Attention (MHSA) are introduced as two attention mechanisms to handle the temporal and spatial correlations of the signals through an attention fusion mechanism, achieving the calibration of the signal feature maps. The effectiveness of this method is validated using various datasets, with the experimental results demonstrating that the proposed approach can fully utilize the information of multimodal signals. The experimental results show that the recognition accuracy of MPHNN on multiple datasets reaches 93.1%, and it has lower computational complexity and fewer parameters than other models.

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Simple and Robust Log-Likelihood Ratio Calculation of Coded MPSK Signals in Wireless Sensor Networks for Healthcare

Cited by 2 publications

References 27 publications

Coverage Optimization of WSNs Based on Enhanced Multi-Objective Salp Swarm Algorithm

Coverage Optimization of WSNs Based on Enhanced Multi-Objective Salp Swarm Algorithm

Automatic Modulation Recognition Based on Multimodal Information Processing: A New Approach and Application

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