Minimum-Latency Broadcast Scheduling for Cognitive Radio Networks

Minimum-Latency Broadcast Scheduling for Cognitive Radio Networks

Shouling Ji and Raheem BeyahCAP group, School of Electrical and Computer Engineering

Georgia Institute of Technology

Zhipeng Cai Department of Computer Science

Georgia State University

OUTLINE

4

Introduction1

2

3

5

Broadcasting Tree and Coloring

System Model and Problem Definition

Broadcast Scheduling

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

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

Broadcast Scheduling in CRNs

6

System Model and Problem Definition

7

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:

Network Model

8

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

Interference Model and Problem Definition

9

Broadcasting Tree and Coloring

10

Connected Dominating Set (CDS) Dominators (black), Connectors (blue), and Dominatees (white)

CDS-based broadcasting tree

CDS-based Broadcasting Tree

11

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

Tessellation and Coloring

12

Broadcast Scheduling

13

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 dominatee children are waiting for receiving the broadcast packet

Broadcast Scheduling under UDG

14

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 UDG

15

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).

Broadcast Scheduling under PrIM

16

Simulation

17

Latency performance

Simulation Results and Analysis

18

Redundancy performance

Simulation Results and Analysis

19

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

Conclusion and Future Work

THANK YOU!

Minimum-Latency Broadcast Scheduling for Cognitive Radio Networks

Shouling Ji and Raheem BeyahCAP Group, Georgia Institute of Technology

sji@gatech.eduhttp://www.ece.gatech.edu/cap/

Zhipeng CaiGeorgia State University