Sparse Vector Coding-Based Multi-Carrier NOMA for In-Home Health Networks

Zhang, Xuewan; Yang, Liuqing; Ding, Zhiguo; Song, Jian; Zhai, Yunkai; Zhang, Di

doi:10.1109/jsac.2020.3020679

Cited by 54 publications

(31 citation statements)

References 40 publications

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“…This article has been cited as suggested, please see [10] in the updated manuscript. Please note that the power is selected in [10] such that the constellations for both users do not overlap without specifying the possible power ranges. Also, the system considers only the two-user scenario.…”

Section: Department Of Electrical and Computer Engineering Western University London On Canadamentioning

confidence: 99%

Power Allocation for Reliable SIC Detection of Rectangular QAM-based NOMA Systems

Al-Dweik¹,

Iraqi²

2021

Preprint

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This paper considers the power allocation for non-orthogonal multiple access (NOMA) users to enable using the successive interference cancellation (SIC) while providing reliable error performance. The derived closed-form expressions are applicable for arbitrary number of NOMA users each of which has a square or rectangular quadrature amplitude modulation (QAM) constellation with arbitrary order. The obtained numerical results show that power assignment process at the transmitter for the superposition process and at the receiver for the SIC process should be performed meticulously because the power difference between the weakest and strongest users can be tremendous when the number of users or the modulation orders increase. Moreover, the derived expressions can be used to reduce the computational complexity that is required to obtain the optimal power coefficients using brute force methods by significantly reducing the search space.

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Section: Department Of Electrical and Computer Engineering Western University London On Canadamentioning

confidence: 99%

Power Allocation for Reliable SIC Detection of Rectangular QAM-based NOMA Systems

Al-Dweik¹,

Iraqi²

2021

Preprint

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“…HE past decade has witnessed the great progress in artificial intelligence and communication networks. Predictably, cyberspace of the Internet of Things (IoT) will become an important living space for human beings in the 5G era [1]. Accordingly, cyberspace security will be of great importance to the economic prosperity and social stability, such as the spammer detection.…”

Section: Introductionmentioning

confidence: 99%

Robust Spammer Detection Using Collaborative Neural Network in Internet-of-Things Applications

Guo

Shen

Bashir

et al. 2021

IEEE Internet Things J.

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158

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Spamming is emerging as a key threat to Internet of Things (IoT)-based social media applications. It will pose serious security threats to the IoT cyberspace. To this end, artificial intelligence-based detection and identification techniques have been widely investigated. The literature works on IoT cyberspace can be categorized into two categories: 1) behavior pattern-based approaches; and 2) semantic pattern-based approaches. However, they are unable to effectively handle concealed, complicated, and changing spamming activities, especially in the highly uncertain environment of the IoT. To address this challenge, in this paper, we exploit the collaborative awareness of both patterns, and propose a Collaborative neural network-based Spammer detection mechanism (Co-Spam) in social media applications. In particular, it introduces multi-source information fusion by collaboratively encoding long-term behavioral and semantic patterns. Hence, a more comprehensive representation of the feature space can be captured for further spammer detection. Empirically, we implement a series of experiments on two real-world datasets under different scenario and parameter settings. The efficiency of the proposed Co-Spam is compared with five baselines with respect to several evaluation metrics. The experimental results indicate that the Co-Spam has an average performance improvement of approximately 5% compared to the baselines.

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“…At present, telemedicine technology has realized the real-time communication of voice and high-definition image using high-speed network, which provides a broader development space for modern medicine [ 1 – 3 ]. And national health authorities are focusing on E-health services such as E-health cards, health portals, health information networks, electronic medical records, telemedicine services, and personal wear and portable communication systems for patient monitoring and support [ 4 – 6 ]. Among them, independently managed biomedical/medical stakeholders (such as hospitals, suppliers, and patients) will adopt blockchain technology for applications that want to collaborate without ceded control to centrally managed intermediaries [ 7 – 11 ].…”

Section: Introductionmentioning

confidence: 99%

A Displacement Estimated Method for Real Time Tissue Ultrasound Elastography

Zhang

et al. 2021

Mobile Netw Appl

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As an important means of medical imaging, elastic imaging is an indispensable part of mobile telemedicine. Ultrasound elastography has become a research hotspot because it can accurately measure soft tissue lesions. Displacement estimation is the most important step in ultrasound elastography. At present, the phase zero search method is an accurate and fast displacement estimation method. However, when the displacement exceeds 1/4 wavelength, it is invalid. The accuracy of block matching method is not high, but it is suitable for large displacement, so it can overcome this shortcoming. It is worth noting that the quality-guided block matching method has good robustness under complex mutation conditions. It can provide prior knowledge to increase the robustness of the phase-zero search under large displacement conditions. So we propose a novel displacement estimation method for real time tissue ultrasound elastography, which combines the quality-guided block matching method and the phase-zero search method. The experimental results show that this method is more accurate, faster and robust than other displacement estimation methods.

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Sparse Vector Coding-Based Multi-Carrier NOMA for In-Home Health Networks

Cited by 54 publications

References 40 publications

Power Allocation for Reliable SIC Detection of Rectangular QAM-based NOMA Systems

Power Allocation for Reliable SIC Detection of Rectangular QAM-based NOMA Systems

Robust Spammer Detection Using Collaborative Neural Network in Internet-of-Things Applications

A Displacement Estimated Method for Real Time Tissue Ultrasound Elastography

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