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Conference Paper: How Local Information Improves Rendezvous in Cognitive Radio Networks

TitleHow Local Information Improves Rendezvous in Cognitive Radio Networks
Authors
Issue Date2018
PublisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1001051
Citation
Proceedings of the 2018 15th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON), Hong Kong, China,11-13 June 2018 How to Cite?
AbstractCognitive Radio Network (CRN) is a promising technique for solving the wireless spectrum scarcity problem. Rendezvous is the fundamental process of CRNs. We aim at designing faster rendezvous algorithms for CRNs. We find that local information such as user's ID and the label of an available channel is very useful for designing faster rendezvous algorithms. First, we propose the Sequence-Rotating Rendezvous (SRR) algorithm. The SRR algorithm can guarantee rendezvous for any two users i and j in (2P 2 + 2P) timeslots, where P is the least prime not less than the total number of channels in the network. Second, we utilize the user's identifier (ID) to design an ID-based Rendezvous (IDR) algorithm. The IDR algorithm can guarantee rendezvous for any two users i and j in (l + 1)(P_i + 2)(P_j + 2) timeslots, where Pi and Pj are the smallest primes which are not less than the numbers of available channels of users i and j respectively. Third, we propose a Channel-Label- based Rendezvous (CLR) algorithm which can guarantee rendezvous for any two usersin ((P_i+2) (P_j+2)+PN)(⌈log_2N⌉+1) timeslots, where N is the total number of channels in the network and P_N is the least prime which is not less than N. The theoretical Maximum Time To Rendezvous (MTTRs) of the three algorithms we propose are less than those of the state-of-the-art algorithms in the corresponding categories respectively in certain scenarios. All of our algorithms can be used in multi-user scenarios. We conduct a number of experiments to compare our algorithms with state- of-the-art rendezvous algorithms in different scenarios, the results of which confirm our theoretical analysis.
Persistent Identifierhttp://hdl.handle.net/10722/260636
ISBN

 

DC FieldValueLanguage
dc.contributor.authorFu, YQ-
dc.contributor.authorWang, Y-
dc.contributor.authorGu, Z-
dc.contributor.authorZheng, XL-
dc.contributor.authorWei, TH-
dc.contributor.authorCao, Z-
dc.contributor.authorCui, H-
dc.contributor.authorLau, FCM-
dc.date.accessioned2018-09-14T08:44:50Z-
dc.date.available2018-09-14T08:44:50Z-
dc.date.issued2018-
dc.identifier.citationProceedings of the 2018 15th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON), Hong Kong, China,11-13 June 2018-
dc.identifier.isbn978-1-5386-4281-8-
dc.identifier.urihttp://hdl.handle.net/10722/260636-
dc.description.abstractCognitive Radio Network (CRN) is a promising technique for solving the wireless spectrum scarcity problem. Rendezvous is the fundamental process of CRNs. We aim at designing faster rendezvous algorithms for CRNs. We find that local information such as user's ID and the label of an available channel is very useful for designing faster rendezvous algorithms. First, we propose the Sequence-Rotating Rendezvous (SRR) algorithm. The SRR algorithm can guarantee rendezvous for any two users i and j in (2P 2 + 2P) timeslots, where P is the least prime not less than the total number of channels in the network. Second, we utilize the user's identifier (ID) to design an ID-based Rendezvous (IDR) algorithm. The IDR algorithm can guarantee rendezvous for any two users i and j in (l + 1)(P_i + 2)(P_j + 2) timeslots, where Pi and Pj are the smallest primes which are not less than the numbers of available channels of users i and j respectively. Third, we propose a Channel-Label- based Rendezvous (CLR) algorithm which can guarantee rendezvous for any two usersin ((P_i+2) (P_j+2)+PN)(⌈log_2N⌉+1) timeslots, where N is the total number of channels in the network and P_N is the least prime which is not less than N. The theoretical Maximum Time To Rendezvous (MTTRs) of the three algorithms we propose are less than those of the state-of-the-art algorithms in the corresponding categories respectively in certain scenarios. All of our algorithms can be used in multi-user scenarios. We conduct a number of experiments to compare our algorithms with state- of-the-art rendezvous algorithms in different scenarios, the results of which confirm our theoretical analysis.-
dc.languageeng-
dc.publisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1001051-
dc.relation.ispartofIEEE International Conference on Sensing, Communication, and Networking (SECON)-
dc.rightsIEEE International Conference on Sensing, Communication, and Networking (SECON). Copyright © IEEE.-
dc.rights©2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.-
dc.titleHow Local Information Improves Rendezvous in Cognitive Radio Networks-
dc.typeConference_Paper-
dc.identifier.emailWang, Y: amywang@hku.hk-
dc.identifier.emailCui, H: heming@hku.hk-
dc.identifier.emailLau, FCM: fcmlau@cs.hku.hk-
dc.identifier.authorityCui, H=rp02008-
dc.identifier.authorityLau, FCM=rp00221-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/SAHCN.2018.8397135-
dc.identifier.hkuros291701-
dc.publisher.placeUnited States-

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