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author:

Wang, Jun (Wang, Jun.) [1] | Jiang, Weibin (Jiang, Weibin.) [2] | Wang, Hongjun (Wang, Hongjun.) [3] | Huang, Yanwei (Huang, Yanwei.) [4] | Chen, Riqing (Chen, Riqing.) [5] | Lin, Ruiquan (Lin, Ruiquan.) [6]

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EI

Abstract:

As part of an Internet of Things (IoT) framework, the Smart Grid (SG) relies on advanced communication technologies for efficient energy management and utilization. Cognitive Radio (CR), which allows Secondary Users (SUs) to opportunistically access and use the spectrum bands owned by Primary Users (PUs), is regarded as the key technology of the next-generation wireless communication. With the assistance of CR technology, the quality of communication in the SG could be improved. In this paper, based on a hybrid CR-enabled SG communication network, a new system architecture for multiband-CR-enabled SG communication is proposed. Then, some optimization mathematical models are also proposed to jointly find the optimal sensing time and the optimal power allocation strategy. By using convex optimization techniques, several optimal methods are proposed to maximize the data rate of multiband-CR-enabled SG while considering the minimum detection probabilities to the active PUs. Finally, simulations are presented to show the validity of the proposed methods. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.

Keyword:

Cognitive radio Convex optimization Electric power transmission networks Energy efficiency Internet of things Smart power grids

Community:

  • [ 1 ] [Wang, Jun]College of Electrical Engineering and Automation, Fuzhou University, Fuzhou; 350116, China
  • [ 2 ] [Jiang, Weibin]College of Electrical Engineering and Automation, Fuzhou University, Fuzhou; 350116, China
  • [ 3 ] [Wang, Hongjun]College of Electronic Engineering, National University of Defense Technology, Hefei; 230037, China
  • [ 4 ] [Huang, Yanwei]College of Electrical Engineering and Automation, Fuzhou University, Fuzhou; 350116, China
  • [ 5 ] [Chen, Riqing]College of Computer and Information Sciences, Fujian Agriculture and Forestry University, Fuzhou; 350002, China
  • [ 6 ] [Lin, Ruiquan]College of Electrical Engineering and Automation, Fuzhou University, Fuzhou; 350116, China

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Source :

Sensors

ISSN: 1424-8220

Year: 2021

Issue: 24

Volume: 21

3 . 8 4 7

JCR@2021

3 . 4 0 0

JCR@2023

ESI HC Threshold:117

JCR Journal Grade:2

CAS Journal Grade:3

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 2

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