User Interference Effect on Routing of Cognitive Radio

User Interference Effect on Routing of Cognitive Radio Ad-Hoc Networks Tauqeer Safdar Lecturer IT-Networking Higher College of Technology (HCT)

Presentation Outline Introduction Problem Formulation Cognitive radio systems Applications Challenges References

1 Introduction

Why cognitive radio? Current status: the frequency bands are statically assigned to specific wireless operators/services Problems: The static frequency allocation policy results in a low utilization of the licensed frequency spectrum. For example, in most of the time only 6% of the frequency spectrum is active [FCC/American Foundation Broadband Forum, June 2003]. Current spectrum policy needs to be re-explored

Cognitive Radio Ad-Hoc Network (CRAHN) Fixed Spectrum Assignment policy spectrum white spaces Inefficient spectrum utilization Cognitive radio ad-hoc network (CRAHN): ◦ A new paradigm that provides the capability to share or use the spectrum through Dynamic Spectrum Access (DSA).

Dynamic Spectrum Access The concept of dynamic spectrum access (DSA) has been proposed by the Federal Communication Committee (FCC) as a solution to the potential spectrum scarcity and spectrum underutilizations problems Basic principle: secondary users can “borrow” spectrum from primary users, but always respect primary users’ priority Secondary Zzzzzz rights Zzzzzz Ideally, primary users do not perceive the existence of secondary users Zzzzzz Primary Users Hey, don’t waist the spectrum Users

3 Problem Formulation

Routing Effected in CRAHN Due to User Interference

Problem Statement To provide the user interference-aware routing so that the end-to-end delay and packet collision can be minimized and Average Data Rate is improved under Dynamic Spectrum Access & Spectrum Mobility environment of CRAHN.

4 Proposed Solution & Methodology

Network-Layered Architecture of CRAHN

Proposed Solution Analyze the impact of user interference on Qo. S in CRAHN routing. Implementation of channel selection through the learning and decision mechanism on Network layer for route calculation during routing. The channel information is accessed through learning agent from MAC layer using the spectrum mobility manager. Minimize the end-to-end delay and user interference in terms of packet collision. Simulate the result using the CRCN based on NS-2 simulator.

Methodology

Methodology Routing table ◦ the channel information such as, transmission rate, modulation, channel switching delay etc. Decision block ◦ path information and Qo. S performance ◦ Qo. S evaluation block influences the decision block by measuring. how close the current performance of the routing algorithm fares with the requirements specified by the application layer. Learning Block ◦ tunes the working of the routing layer over time ◦ helps the decision block to make progressively better channel and path switching decisions.

Implementation of Learning Agent through Learning Block

5 Findings & Calculations

Simulations CRCN simulator is a software based network simulator for network-level simulations. [20] Based on open-source NS-2. [21]

Findings Impact of transmission rate on packet delivery ratio

Impact of transmission rate on average endto-end delay

End-to-End Delay.

6 Applications, Obstacles & Future Work

Cognitive Radio and Military Networks How is the military planning on using cognitive radio?

Drivers in Commercial and Military Networks Many of the same commercial applications also apply to military networks ◦ Opportunistic spectrum utilization ◦ Improved link reliability ◦ Automated interoperability ◦ Cheaper radios ◦ Collaborative networks Military has much greater need for advanced networking techniques ◦ MANETs and infrastructure-less networks ◦ Disruption tolerant ◦ Dynamic distribution of services ◦ Energy constrained devices Goal is to intelligently adapt device, link, and network parameters to help achieve mission objectives

Typical Cognitive Radio Applications What does cognitive radio enable? � Cognitive Radio Technologies, 2007 25

Challenges Interference avoidance Qo. S awareness Seamless communication Requires a cross layer design

Conclusion The overall end-to-end delay has been minimized for CRAHN routing. Interference-Aware routing for the efficient transmission in CRAHN is proposed in a cross layer fashion of network architectural stack. Reinforcement learning is implemented on network layer for proper interference handling by secondary users.