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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

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


OUTLINE Networks

1

Introduction

System Model and Problem Definition

2

Broadcasting Tree and Coloring

3

Broadcast Scheduling

4

5

Simulation

6

Conclusion and Future Work


Introduction
Introduction Networks


Cognitive radio networks crns
Cognitive Radio Networks (CRNs) Networks

  • 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)


Broadcast scheduling in crns
Broadcast Scheduling in CRNs Networks

  • 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



Network model
Network Model Networks

  • 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:


Interference model and problem definition
Interference Model and Problem Definition Networks

  • 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



Cds based broadcasting tree
CDS-based Broadcasting Tree Networks

  • Connected Dominating Set (CDS)

    • Dominators (black), Connectors (blue), and Dominatees (white)

  • CDS-based broadcasting tree


Tessellation and coloring
Tessellation and Coloring Networks

  • 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



Broadcast scheduling under udg
Broadcast Scheduling under UDG Networks

  • 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


Broadcast scheduling under udg1
Broadcast Scheduling under UDG Networks

  • 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).


Broadcast scheduling under prim
Broadcast Scheduling under NetworksPrIM

  • 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).


Simulation
Simulation Networks


Simulation results and analysis
Simulation Results and Analysis Networks

  • Latency performance


Simulation results and analysis1
Simulation Results and Analysis Networks

  • Redundancy performance


Conclusion and future work
Conclusion and Future Work Networks

  • 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


Minimum-Latency Broadcast Scheduling for Cognitive Radio Networks

ShoulingJi and RaheemBeyah

CAP Group, Georgia Institute of Technology

[email protected]

http://www.ece.gatech.edu/cap/

ZhipengCai

Georgia State University

Thank you!


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