VLSI Architecture for Cyclostationary Feature Detection Based Spectrum Sensing for Cognitive-Radio Wireless Networks and Its ASIC Implementation

Murty, Mahesh S.; Shrestha, Rahul

doi:10.1109/isvlsi.2016.12

Cited by 12 publications

(3 citation statements)

References 10 publications

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“…82 Moreover, feature detection decides if PU signal is present on a spectrum band by detecting a built-in periodicity in a modulated signal introduced by certain features. 83 These features include pilot signals, symbol rate, prefixes, spreading codes, and modulation type from local observations. 67 However, unlike energy detection and matched filter detection techniques feature detection is robust to uncertainty of noise power.…”

Section: Cyclostationary Feature Detectionmentioning

confidence: 99%

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A review of industrial wireless communications, challenges, and solutions: A cognitive radio approach

Oyewobi

Djouani

Kurien

2020

Trans Emerging Tel Tech

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Integral and crucial to performance of wireless sensor networks (WSNs), and specifically industrial wireless sensor network (IWSN) is stable, robust, reliable, and ubiquitous communications system. Though, wired communications system is suitable for industrial communications and is resilient to shadowing and multipath fading effects of industrial‐WSN environments, yet its wireless counterpart is a much preferred industrial communications technology due to reduced cost and high flexibility which it offers in comparison to wired communications. However, overcrowding of the industrial, scientific, and medical band, where IWSN is deployed together with other heterogeneous technologies, as well as resultant scarcity of usable frequency spectrum has restrained exclusive application of wireless technology for industrial communications. Nonetheless, cognitive radio (CR) has ability to increase spectrum utilization efficiency and channel capacity for industrial wireless communications (IWC) through opportunistic/dynamic spectrum access (DSA) technique. In this review article, we examine how DSA can benefit IWC through exploitation of new perspectives of white space definitions in the licensed bands as well as unlicensed bands. While discussing the potential of DSA for IWC, we have considered the unique characteristics of IWC as well as technical challenges and issues imposed by industrial‐WSN. Accordingly, we have suggested and proffered appropriate CR‐based solutions in mitigating some of the challenges where necessary.

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Section: Cyclostationary Feature Detectionmentioning

confidence: 99%

“…TA B L E 2 Summary of the different PU signal detection techniques 67,[72][73][74]77,78,81,83,84 50,51,59 Matched filter detection [72][73][74] • Less power consumption • Less sensing period.…”

Section: Suitability Of Different Pu Signal Detection Techniques For mentioning

confidence: 99%

A review of industrial wireless communications, challenges, and solutions: A cognitive radio approach

Oyewobi

Djouani

Kurien

2020

Trans Emerging Tel Tech

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“…Sutton et al [14] realized remote monitoring of construction projects based on WSN, and achieved functions of safety monitoring, quality monitoring and building energy saving. Murty and Shrestha [15] combined the CI system with the enhanced RFID positioning technology to realize multi-parameter monitoring of key parts of bridge structures and applied it to the risk warning of bridge structure damages. In the 2015 Annual Summit of China's Construction Industry, the concept of smart building and engineering based on AI, sensing technology and VR had been proposed for the first time [16].…”

Section: Introductionmentioning

confidence: 99%

Safety Monitoring and Evaluation of Construction Projects Based on Multi-sensor Fusion

Ni¹

2020

I2M

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For most construction projects, the complex engineering environment, the backward data collection technology, and the unreasonable monitoring network have resulted in many problems in monitoring data such as lots of noise and missing data items, therefore, it is of great significance to study the safety monitoring system of construction projects based on wireless sensor network (WSN). For this reason, this paper proposed a construction safety monitoring and evaluation (CSME) model based on multi-sensor fusion. First, the system structure and data flow model of the construction safety monitoring system were constructed; then, combining with a multi-sensor deep fusion system which was built on physical and information systems, this paper designed a spectrum sensing algorithm for sensor signals within the construction area. After that, tempo-spatial correlation analysis was conducted on the monitoring data, and a multi-sensor monitoring network joint sparse (MSMN-JS) model was constructed, which realized reconstruction of missing data items. At last, this paper used experimental results to prove the application value of the algorithm model to the safety monitoring and evaluation of construction projects.

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Design of Power Efficient and High-Performance Architecture to Spectrum Sensing Applications Using Cyclostationary Feature Detection

Aishwarya¹,

Swamy²

2020

Cognitive Informatics and Soft Computing

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VLSI Architecture for Cyclostationary Feature Detection Based Spectrum Sensing for Cognitive-Radio Wireless Networks and Its ASIC Implementation

Cited by 12 publications

References 10 publications

A review of industrial wireless communications, challenges, and solutions: A cognitive radio approach

A review of industrial wireless communications, challenges, and solutions: A cognitive radio approach

Safety Monitoring and Evaluation of Construction Projects Based on Multi-sensor Fusion

Design of Power Efficient and High-Performance Architecture to Spectrum Sensing Applications Using Cyclostationary Feature Detection

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