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Enhanced frameless slotted ALOHA protocol with Markov chains analysis
KTH, School of Electrical Engineering and Computer Science (EECS), Information Science and Engineering.ORCID iD: 0000-0002-5407-0835
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2018 (English)In: Science China Information Sciences, ISSN 1674-733X, E-ISSN 1869-1919, Vol. 61, no 10, article id 102304Article in journal (Refereed) Published
Abstract [en]

In this paper, we propose a novel approach to enhance the performance of frameless slotted ALOHA (SA) protocol. We employ signature codes to help the receiver identify the packets contained in collisions, and use successive interference cancellation (SIC) for packet recovery. We model the proposed scheme as a two-state Markov model represented by a uni-partite graph. We evaluate the throughput, expected delay and average memory size of the proposed scheme, and optimize the proposed scheme to maximize the throughput. We show that the theoretical analysis matches well with simulation results. The throughput and expected delay of the proposed protocol outperform the conventional slotted ALOHA protocol significantly. 

Place, publisher, year, edition, pages
Science in China Press , 2018. Vol. 61, no 10, article id 102304
Keywords [en]
delay, Markov chain, SIC, slotted ALOHA, throughput, uni-partite graph, Chains, Expected delay, Markov model, Packet recovery, Partite graphs, Slotted ALOHA protocol, Successive interference cancellation(SIC), Markov processes
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-236659DOI: 10.1007/s11432-017-9296-6ISI: 000436195600002Scopus ID: 2-s2.0-85048213937OAI: oai:DiVA.org:kth-236659DiVA, id: diva2:1262796
Note

Export Date: 22 October 2018; Article; Correspondence Address: Fei, Z.; School of Information and Electronics, Beijing Institute of TechnologyChina; email: feizesong@bit.edu.cn; Funding details: D171100006317001; Funding details: 4152047, Natural Science Foundation of Beijing Municipality; Funding details: B14010; Funding details: 111 Project; Funding text: Acknowledgements This work was supported in part by Beijing Major Science and Technology Projects (Grant No. D171100006317001), Beijing Natural Science Foundation (Grant No. 4152047), and Programme of Introducing Talents of Discipline to Universities (111 Project) (Grant No. B14010). QC 20181113

Available from: 2018-11-13 Created: 2018-11-13 Last updated: 2018-11-13Bibliographically approved

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Xiao, Ming

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