Cognitive Engine Development for IEEE Lizdabel Morales April 16 th, 2007

Presentation Outline Introduction IEEE 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 radioCognition 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 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 Cognitive Engine Objective was to create a Cognitive Engine for IEEE systems Phases I and II completed Main Accomplishments: –Development of a solid and generic architecture for the IEEE CE –Development of a flexible framework that allows for future design, development and testing of more sophisticated modules

WRAN Considered Scenario 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

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: U 1 = QoS satisfaction of each (uplink and downlink) connection … for adding new CPE connections U 2 = Incumbent PU protection (fast adaptation and evacuation) … more important for relocating CPEs in case PU reappears U 3 = Spectral efficiency… more important for multi-cell or large number of CPEs U 4 = Power efficiency and interference temperature reduction …more important for mobile UE and large-scale cognitive networks U = w 1 *U 1 + w 2 *U 2 + w 3 *U 3 + w 4 *U 4

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

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

Specification Detect incumbent user Update policy Deactivate uncompliant nodes Allocate resources Scenario Current framework picks up after incumbent user is detected

Questions