Minimum latency broadcast scheduling for cognitive radio networks
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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

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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


Minimum latency broadcast scheduling for cognitive radio networks

OUTLINE

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


Cognitive radio networks crns

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)


Broadcast scheduling in crns

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


System model and problem definition

System Model and Problem Definition


Network model

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:


Interference model and problem definition

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


Broadcasting tree and coloring

Broadcasting Tree and Coloring


Cds based broadcasting tree

CDS-based Broadcasting Tree

  • Connected Dominating Set (CDS)

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

  • CDS-based broadcasting tree


Tessellation and coloring

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


Broadcast scheduling

Broadcast Scheduling


Broadcast scheduling under udg

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


Broadcast scheduling under udg1

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).


Broadcast scheduling under prim

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).


Simulation

Simulation


Simulation results and analysis

Simulation Results and Analysis

  • Latency performance


Simulation results and analysis1

Simulation Results and Analysis

  • Redundancy performance


Conclusion and future work

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


Minimum latency broadcast scheduling for cognitive radio networks

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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