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Asynchronous iterative water filling for cognitive smart grid communications

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dc.citedby1
dc.contributor.authorHiew Y.-K.en_US
dc.contributor.authorAripin N.M.en_US
dc.contributor.authorDIn N.M.en_US
dc.contributor.authorid56495343900en_US
dc.contributor.authorid35092180800en_US
dc.contributor.authorid9335429400en_US
dc.date.accessioned2023-05-29T05:59:43Z
dc.date.available2023-05-29T05:59:43Z
dc.date.issued2015
dc.descriptionAdvanced metering infrastructures; Communication; Electric power transmission networks; Filling; Game theory; Industrial electronics; Iterative methods; Radio communication; Smart power grids; Telecommunication networks; Throughput; Asynchronous iterative; Control packets; Neighboring nodes; Smart grid; Smart Grid Communications; Throughput performance; Unlicensed spectrum; Wireless channel; Cognitive radioen_US
dc.description.abstractAsynchronous iterative water filling (AIWF), which is based on Shannon Theory, distributes resources fairly among users. Unlike Game Theory that requires users to exchange packets, AIWF does not require users to exchange packets for achieving Nash equilibrium. Conventionally, AIWF computes optimal transmit power for multiple users in Gaussian Wireless Channel. In this paper, AIWF was implemented upon cognitive radio for smart grid communications. Smart grid data in Advanced Metering Infrastructure (AMI) can be as large as 1000 kbps or 500 kbps for backhaul. Locating a block of spectrum which is available at all smart gird areas for AMI communication is infeasible. Although cognitive radio allows users to detect and utilize idle channels in licensed and unlicensed spectrum bands, the ability of cognitive radio to support high traffic load in AMI is a concern. Hence, AIWF was implemented to maximize the throughput performance in to order to cope with high traffic load environments. AIWF perform repetitive calculations to determine the transmit power of each user. Too low a transmit power causes unsuccessful transmission; while too high a transmit power increases delivery ratio, but it also causes interference to neighboring users. Using AIWF, interference from neighboring nodes are treated as noise, thus, no control packet exchange is required. AIWF coding was implemented in NS-2 simulator. An AMI communication scenario was simulated. The results show that AIWF improves the throughput performance by 20.35%. � 2015 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo7298358
dc.identifier.doi10.1109/ISCAIE.2015.7298358
dc.identifier.epage216
dc.identifier.scopus2-s2.0-84959052792
dc.identifier.spage211
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84959052792&doi=10.1109%2fISCAIE.2015.7298358&partnerID=40&md5=4ba7e8c4ffbf58094b7f93a1216fed07
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/22229
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitleISCAIE 2015 - 2015 IEEE Symposium on Computer Applications and Industrial Electronics
dc.titleAsynchronous iterative water filling for cognitive smart grid communicationsen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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