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Minimum-Latency Broadcast Scheduling for Cognitive Radio Networks

Minimum-Latency Broadcast Scheduling for Cognitive Radio Networks. Shouling Ji and Raheem Beyah CAP group, School of Electrical and Computer Engineering Georgia Institute of Technology Zhipeng Cai Department of Computer Science Georgia State University. OUTLINE. 1. Introduction.

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Minimum-Latency Broadcast Scheduling for Cognitive Radio Networks

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  1. Minimum-Latency Broadcast Scheduling for Cognitive Radio Networks ShoulingJi and RaheemBeyah CAP group, School of Electrical and Computer Engineering Georgia Institute of Technology ZhipengCai Department of Computer Science Georgia State University

  2. OUTLINE 1 Introduction System Model and Problem Definition 2 Broadcasting Tree and Coloring 3 Broadcast Scheduling 4 5 Simulation 6 Conclusion and Future Work

  3. Introduction

  4. Cognitive Radio Networks (CRNs) • Cognitive Radio Networks (CRNs) • The utilization of spectrum assigned to licensed users varies from 15% to 85% temporally and geographically (FCC report) • Unlicensed users (Secondary Users, SUs) can sense and learn the communication environment, and opportunistically access the spectrum without causing any unacceptable interference to licensed users (Primary Users, PUs)

  5. Broadcast Scheduling in CRNs • Broadcast Scheduling in CRNs • Task and goal • Broadcast a data packet from the source to all the other nodes • Minimum-latency and minimum-redundancy • Motivation • NP-hard even in traditional wireless networks under the simple UDG model • It is not straightforward to move traditional broadcast algorithms to CRNs • Existing solutions are either heuristic solutions without performance guarantee or with performance far from the optimal solution • Our contributions • A Mixed Broadcast Scheduling (MBS) algorithm for CRNs under both the Unit Disk Graph (UDG) model and the Protocol Interference Model (PrIM) • Comprehensive latency and redundancy analysis

  6. System Model and Problem Definition

  7. Network Model • Primary Network • N Primary Users (PUs): • Transmission/interference radius: • Network time is slotted: • Primary transmitters are Poisson distributed with density • Secondary Network • A source and n randomly distributed Secondary Users (SUs) • Transmission/interference radius: • Topology graph:

  8. Interference Model and Problem Definition • Interference Model • Unit Disk Graph (Model): • Protocol Interference Model (PrIM): • Problem definition • To seek a broadcast scheduling strategy of minimum latency • Low broadcast redundancy • the maximum possible transmission times of the broadcast packet by a SU during the scheduling

  9. Broadcasting Tree and Coloring

  10. CDS-based Broadcasting Tree • Connected Dominating Set (CDS) • Dominators (black), Connectors (blue), and Dominatees (white) • CDS-based broadcasting tree

  11. Tessellation and Coloring • Tesselation • A tessellation of a plane is to cover this plane with a pattern of flat shapes so that there are no overlaps or gaps • A regular tessellation is a pattern made by repeating a regular polygon, e.g. hexagon

  12. Broadcast Scheduling

  13. Broadcast Scheduling under UDG • MBS-UDG: Idea • Phase I: broadcast to all the dominators • by Unicast • Phase II: broadcast to all the dominatees • by mixed Unicast and Broadcast • Depending on how many dominateechildren are waiting for receiving the broadcast packet

  14. Broadcast Scheduling under UDG • Latency and redundancy performance analysis • The expected time consumption of MBS-UDG is upper bounded by and (Theorem 3). • The broadcast redundancy of MBS-UDG is at most and (Theorem 4).

  15. Broadcast Scheduling under PrIM • MBS-PrIM • No significant difference with MBS-UDG • Performance analysis • Let . The expected number of time slots consumed by MBS-PrIM is upper bounded by if and if (Theorem 7). • The broadcast redundancy of MBS-PrIM is upper bounded by if , and if (Theorem 8).

  16. Simulation

  17. Simulation Results and Analysis • Latency performance

  18. Simulation Results and Analysis • Redundancy performance

  19. Conclusion and Future Work • A Mixed Broadcast Scheduling (MBS) algorithm is proposed • Comprehensive latency and redundancy performance analysis • Simulations are conducted • Future Research Directions • Considering more accurate dynamic spectrum model and access model • Distributed broadcasting algorithm with performance guarantee

  20. Minimum-Latency Broadcast Scheduling for Cognitive Radio Networks ShoulingJi and RaheemBeyah CAP Group, Georgia Institute of Technology sji@gatech.edu http://www.ece.gatech.edu/cap/ ZhipengCai Georgia State University Thank you!

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