Backscatter technology and intelligent reflecting technology surface technology in the Internet of Things

Zhu, Zhengyu; Chu, Zheng; Li, Xingwang

doi:10.1016/b978-0-32-385655-3.00007-2

Cited by 2 publications

(4 citation statements)

References 44 publications

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“…Based on above discussion in Section III-A and Section III-B, this subsection summarizes the overall algorithm in Algorithm 1. Since the LDT and QT are employed to reformulate the sum rate maximization problem by two types of auxiliary vectors, Algorithm 1 provides the similar procedures to [11], [22], solving problem (2). Hence, the convergence analysis in [11], [22] can be applied to our work, and is omitted here to conserve space.…”

Section: Overall Algorithmmentioning

confidence: 99%

“…The vision for 6G is to achieve even higher data rates, reduced latency, and support more diverse use cases [1]. It aims to establish an ultra-reliable and ubiquitous connectivity fabric that interconnects not only humans but also devices, machines, and intelligent systems, ushering in the Internet of Everything (IoE) era [2].…”

Section: Introductionmentioning

confidence: 99%

“…Then, for the given active beamforming, we present an ADMM algorithm to iteratively design the semi-closedform solution of IRS/ITS phase shifts. 2 Moreover, the computational complexity of the proposed algorithm is characterized.…”

Section: Introductionmentioning

confidence: 99%

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Joint ITS- and IRS-Assisted Cell-Free Networks

Chu,

Xiao,

Foh

et al. 2024

IEEE Wireless Commun. Lett.

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This paper investigates a joint intelligent transmissive surface (ITS) and intelligent reflecting surface (IRS)-assisted cell-free network. Specifically, various ITS-assisted base stations (BSs), handled via a central processing unit (CPU), broadcast information signals to multiple IoT devices, carried by active transmit beamforming and transmissive reflecting phase shifts. Meanwhile, an IRS passively reflect signals from the ITS-assisted BS to the IoT devices. To examine this network performance, a sum rate is maximized among all users to jointly optimize the active beamforming, ITS and IRS passive beampatterns. These coupled variables leads to the non-convexity of this formulated optimization problem, which cannot be directly solved. To deal with this issue, we begin with applying the Lagrange dual transformation (LDT) and quadratic transformation (QT) to recast the sum of multiple logarithmically fractional objectives to the subtractive form, and further to quadratic form. Next, an alternating optimization (AO) algorithm is presented to separately the active beamforming, ITS and IRS passive beampatterns in an iterative fashion. Each sub-optimal solution of these variables can be iteratively derived, in a closed-form, by solving the quadratic objective function with a convex constraint or a unitmodulus constraint via the dual method with bisection search or the Alternating Direction Method of Multipliers (ADMM) algorithm. Finally, simulation results are provided to confirm the performance of the proposed algorithm compared to several benchmark schemes. IndexTerms-Cell-free networks, intelligent reflecting/transmissive surface (IRS/ITS), Lagrange dual transformation (LDT), quadratic transformation (QT), and alternating direction method of multipliers (ADMM).

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Section: Overall Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Joint ITS- and IRS-Assisted Cell-Free Networks

Chu,

Xiao,

Foh

et al. 2024

IEEE Wireless Commun. Lett.

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“…The intelligent perception terminal of the Internet of Things includes a four-layer architecture [3] of intelligent perception, computing fusion and collaboration, local application and communication integration, and interacts with the cloud application layer. Figure 1 shows the system diagram of the intelligent sensing terminal of the Internet of Things.…”

Section: Collect and Process Fault Characteristicsmentioning

confidence: 99%

Condition Monitoring of IGBT Devices in Internet of Things Based on Random Forest Model

Zhou,

Yao,

Wang

et al. 2024

Advances in Transdisciplinary Engineering

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Aiming at the problem of frequent aging faults of insulated gate bipolar transistor modules, this paper takes IGBT aging fault characteristic parameter data collected from the intelligent sensing layer of the Internet of Things terminal as samples. In the computing fusion cooperation layer, multi-terminal cooperation training and establishment of an optimized random forest model is applied to the IGBT aging fault diagnosis system to realize the condition monitoring of IGBT devices. Firstly, the characteristic parameters of aging fault are selected from the data samples and preprocessed to establish the aging fault diagnosis data set. Secondly, the traditional random forest model is built and optimized by parameter optimization of base evaluator, parameter optimization of model frame and bagging method. The optimization model training was completed on the basis of cross-validation. Finally, the prediction effect of the model in this paper and other models on IGBT aging fault diagnosis data set was evaluated by various evaluation indexes. Finally, the optimized model fit well, the error between the training set curve and the test set curve was 1.19%, and the prediction accuracy on the test set could reach 98.81%. The feasibility and accuracy of the optimized random forest model applied to IGBT condition monitoring system in the Internet of Things environment are verified.

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Backscatter technology and intelligent reflecting technology surface technology in the Internet of Things

Cited by 2 publications

References 44 publications

Joint ITS- and IRS-Assisted Cell-Free Networks

Joint ITS- and IRS-Assisted Cell-Free Networks

Condition Monitoring of IGBT Devices in Internet of Things Based on Random Forest Model

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