Uncoordinated Massive Wireless Networks: Spatiotemporal Models and Multiaccess Strategies

Giovanni Chisci, Hesham Elsawy, Andrea Conti, Mohamed-Slim Alouini, Moe Z. Win

Research output: Contribution to journalArticlepeer-review

37 Scopus citations


The massive wireless networks (MWNs) enable surging applications for the Internet of Things and cyber physical systems. In these applications, nodes typically exhibit stringent power constraints, limited computing capabilities, and sporadic traffic patterns. This paper develops a spatiotemporal model to characterize and design uncoordinated multiple access (UMA) strategies for MWNs. By combining stochastic geometry and queueing theory, the paper quantifies the scalability of UMA via the maximum spatiotemporal traffic density that can be accommodated in the network, while satisfying the target operational constraints (e.g., stability) for a given percentile of the nodes. The developed framework is then used to design UMA strategies that stabilize the node data buffers and achieve desirable latency, buffer size, and data rate.
Original languageEnglish (US)
Pages (from-to)918-931
Number of pages14
JournalIEEE/ACM Transactions on Networking
Issue number3
StatePublished - Jun 2019

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): OSR-2015-SENSORS-2700
Acknowledgements: This work was supported in part by FAR and the “5x1000” Young Researcher Mobility Project, University of Ferrara, Italy, in part by the KAUST Sensor Research Initiative under Award OSR-2015-SENSORS-2700, and in part by the National Science Foundation under Grant
CCF-1525705. This paper was presented in part at the 2017 IEEE International Symposium on Wireless Communication Systems.


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