Primary Social Behavior aware Routing and Scheduling for Cognitive Radio Networks

Primary Social Behavior aware Routing and Scheduling for Cognitive Radio Networks

Shouling Ji and Raheem Beyah Georgia Institute of Technology

Zhipeng CaiGeorgia State University

Jing Selena HeKennesaw State University

S. Ji, Z. Cai, J. S. He, and R. Beyah Routing and Scheduling for CRNs

Introduction

Primary Users (PUs)

Secondary Users (SUs)

S. Ji, Z. Cai, J. S. He, and R. Beyah Routing and Scheduling for CRNs

Introduction• Problem

– Routing

– Scheduling

• Motivation– Primary users’ activities follow some social patterns

– The primary network consisting of cell phone users is more active during [10am – 10pm] compared with other time slots

– The primary activity (cell phone users) is heavier during [9am – 5pm] in the business area while heavier during [6pm – 11pm] in the residential area

• Contribution– Primary social behavior-aware whitespace analysis

– ε-optimal joint routing and scheduling algorithm

– Distributed joint routing and scheduling framework

S. Ji, Z. Cai, J. S. He, and R. Beyah Routing and Scheduling for CRNs

Outline

• Introduction and Motivation• System Model• Social Behavior Analysis of PUs• Joint Routing and Time-domain Scheduling • Distributed Solution• Simulation• Conclusion

S. Ji, Z. Cai, J. S. He, and R. Beyah Routing and Scheduling for CRNs

System Model• Primary Network

– N Poisson distributed PUs with density of λ

– Transmission and interference radii: R and RI

– The network time is slotted with each time slot of length τ

• Secondary Network– n i.i.d. SUs

– Transmission and interference radii: r and rI

• Problem – Study how to route and schedule the data transmission for a set of communication

sessions within time T

S. Ji, Z. Cai, J. S. He, and R. Beyah Routing and Scheduling for CRNs

Outline

• Introduction and Motivation• System Model• Social Behavior Analysis of PUs• Joint Routing and Time-domain Scheduling • Distributed Solution• Simulation• Conclusion

S. Ji, Z. Cai, J. S. He, and R. Beyah Routing and Scheduling for CRNs

Social Behavior Analysis of PUs

The utilization of primary spectrum is inefficient!

Q1: how does the spectrum whitespace/opportunity distribute over time?Q2: how to utilize primary spectrum effectively and meanwhile minimizing harmful impacts on primary activities?

S. Ji, Z. Cai, J. S. He, and R. Beyah Routing and Scheduling for CRNs

Social Behavior Analysis of PUs• MIT Reality Trace

– 97 mobile device holders, 114046 Bluetooth contacts spanning 246 days

• UCSD Trace– 275 mobile device holders, 123335 WiFi contacts spanning 77 days

S. Ji, Z. Cai, J. S. He, and R. Beyah Routing and Scheduling for CRNs

Social Behavior Analysis of PUs

• The probability that a PU is active during the t-th time slot

• The available whitespace for a secondary link

S. Ji, Z. Cai, J. S. He, and R. Beyah Routing and Scheduling for CRNs

Outline

• Introduction and Motivation• System Model• Social Behavior Analysis of PUs• Joint Routing and Time-domain Scheduling • Distributed Solution• Simulation• Conclusion

S. Ji, Z. Cai, J. S. He, and R. Beyah Routing and Scheduling for CRNs

Joint Routing and SchedulingMaximize a scaling factor κ such that each session l can achieve a data transmission rate of at least κγ(l)

Scheduling

Routing

Flow Balance Constraint

Capacity Constraint

Mixed-Integer Non-Linear Program (MINLP)

S. Ji, Z. Cai, J. S. He, and R. Beyah Routing and Scheduling for CRNs

Joint Routing and Scheduling

MINLP P

Linearized version of P

Divide P into two subproblems

Solution

S. Ji, Z. Cai, J. S. He, and R. Beyah Routing and Scheduling for CRNs

Outline

• Introduction and Motivation• System Model• Social Behavior Analysis of PUs• Joint Routing and Time-domain Scheduling • Distributed Solution• Simulation• Conclusion

S. Ji, Z. Cai, J. S. He, and R. Beyah Routing and Scheduling for CRNs

Distributed Solution• Forwarding Sector

• Forwarding score

– Prefers the next-hop such that it is closer to the destination, more available bandwidth, less interference, and less traffic

S. Ji, Z. Cai, J. S. He, and R. Beyah Routing and Scheduling for CRNs

Distributed Solution

S. Ji, Z. Cai, J. S. He, and R. Beyah Routing and Scheduling for CRNs

Outline

• Introduction and Motivation• System Model• Social Behavior Analysis of PUs• Joint Routing and Time-domain Scheduling • Distributed Solution• Simulation• Conclusion

S. Ji, Z. Cai, J. S. He, and R. Beyah Routing and Scheduling for CRNs

Simulation• Settings

– Time slot: 1min

– One periodical size: 24 hours = 1440 time slots

– By default: W = 100, N = 100, n = 500, session# = 6, ……

• Comparison– Coolest (ICDCS’11): a spectrum mobility-aware routing algorithm for CRNs

S. Ji, Z. Cai, J. S. He, and R. Beyah Routing and Scheduling for CRNs

Simulation• Available bandwidth for SUs

• Successful delivery ratio

S. Ji, Z. Cai, J. S. He, and R. Beyah Routing and Scheduling for CRNs

Conclusion

– Primary social behavior-aware whitespace analysis for CRNs

– Both centralized and distributed joint routing and scheduling algorithms for CRNs

S. Ji, Z. Cai, J. S. He, and R. Beyah Routing and Scheduling for CRNs

Thank you!Shouling Ji

sji@gatech.edu

http://users.ece.gatech.edu/sji/