Cognitive Engine Development for IEEE 802.22

Lizdabel Morales

April 16th, 2007

lizdabel@vt.edu

Presentation Outline

• Introduction

• IEEE 802.22

• Cognitive Radio

• CE Development Approach

• Simulation and Results

• Future Work

What is a cognitive radio?

An enhancement on the traditional software radio concept wherein the radio is aware of its environment and its capabilities, is able to independently alter its physical layer behavior, and is capable of following complex adaptation strategies.

Cognitive radio Cognition cycle

Cognitive Radio is a promising tool for…

• Access to spectrum– finding an open frequency and using it

• Interoperability– talking to legacy radios using a variety of incompatible

waveforms

Cognitive Radio Platform

Motivation for using “cognition” in IEEE 802.22 Systems

• Using previous experience to predict:– Channel reputation– Incumbent detection– Other patterns

• Protect incumbent users by being aware of the environment

• Co-existence and self co-existence• Spectrum utilization improvements• Future proofing for other CR

technologies

“It is not known whether a CR network can offer satisfactory performance despite the injection of many new incumbent handling mechanisms…”

[Cordeiro, et. al. 2005]

MPRG’s Development of an IEEE 802.22 Cognitive Engine

• Objective was to create a Cognitive Engine for IEEE 802.22 systems

• Phases I and II completed• Main Accomplishments:

– Development of a solid and generic architecture for the IEEE 802.22 CE

– Development of a flexible framework that allows for future design, development and testing of more sophisticated modules

WRAN Considered Scenario

CPE 1CPE 2

CPE 3

CPE 4

CH1CH1

CH2

CH3

WRAN Base Station

TV Station (Primary User)

• System Description– Single WRAN BS– CPE’s with different application requests– Incumbent users – TV only and Part 74 devices

• Events that trigger change in the system:– New CPE service request in the WRAN– Incumbent detected in TV channel

Cognitive Engine Architecture

WRAN Cognitive Engine

Main Controller

REM Multi-objectiveOptimizer

Utility

Spectrum Manager

Channel Modeler and

Predictor

Sensing Module

Case and Knowledge Reasoner

Cognitive Engine Modules

• Sensing Module – provides radio environment sensing results

• REM – provides a snapshot of the radio scenario through time

• Main Controller – decides which algorithm to use

• Case and Knowledge Reasoner – provides coarse solution, starting point for the Multi-objective Optimizer

• Multi-objective Optimizer – further refines the solution obtained by the CBR

Utility function & Performance metrics

Utility function used in CE should reflect the performance metrics of cognitive WRAN systems, and weight of each performance metrics may vary with radio scenarios:

• U1 = QoS satisfaction of each (uplink and downlink) connection … for adding new CPE connections

• U2 = Incumbent PU protection (fast adaptation and evacuation) … more important for relocating CPEs in case PU reappears

• U3 = Spectral efficiency … more important for multi-cell or large number of CPEs

• U4 = Power efficiency and interference temperature reduction …more important for mobile UE and large-scale cognitive networks

U = w1*U1 + w2*U2 + w3*U3 + w4*U4

Testing scenarios for WRAN BS CE Performance evaluation

Scenario index

Number of existing

CPEs

Number of CPEs to add to

network

Number of initial active

channels

Number of initial

candidate channels

1 2 3 1 9

2 10 5 2 8

3 10 10 2 8

4 10 20 3 7

5 10 40 3 7

REM-CKL vs. GA

0 5 10 15 20 25 30 35 40 4510

0

101

102

103

Number of CPEs Added

Avera

ge A

dapta

tion T

ime [

ms]

GA

CKL

CKL runs much faster than GA, especially under complicated situations.

802.22 Specification

Detect incumbent user

Update policy

Deactivate uncompliantnodes

Allocate resources

Scenario

Current framework picks up after incumbent user is detected

Questions