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Cognitive Radio with Radio Research Management

MQP Presentation
by

Matthew Kelley

on 4 October 2011

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Transcript of Cognitive Radio with Radio Research Management

Cognitive Radio with Radio Resource Management
For Medical Applications Diagnostic Request Node Discovery Link Assessment Many-to-one Return Routing Algorithm Parameter Assignment Implementation Advisors: Alexander Wyglinski Sponsors: Sean McGrath
Richard Vaz Michael Barry Path Limiting Algorithm Power Limiting Algorithm Cognitive radio is an external engine to the MAC and physical layers with the purpose of detecting system performance and providing feedback to RRM modules. Michael Ghizzoni, Matthew Kelley, Conor Rochford Medical Application Improved Mobility No need to rewire everything when moving between rooms.

Maintain connection to hospital records in transit.

Can be applied to location tracking in hospitals or nursing homes. Improved Signal Quality Shorter signal path Problem Statement To create a multi-user network capable of handling the demands of medical instrumentation with respect to: Data Accuracy/Reliability Mobility Power Efficiency Implement an energy spreading network using the ZigBee protocol with a strict QoS limit on RSSI and BER.
This must update its routes quickly and frequently.
It must promote a longer battery life of all nodes in the network
It must accomplish this without compromising signal quality. Proposed Solution Design Decisions Made Cognitive Parameters
RSSI, BER, Battery Life
RRM Techniques
Transmission Power
Source Routing with Energy Spreading
Hardware
UZBee Dongles
XBee Development Boards
Firmware
Transparent Operation
API Frames Cognitive Parameters Received Signal Strength Indicator (RSSI)
Pin 6 on the XBee Development Board outputs an RSSI encoded as a PWM.


Bit Error Rate (BER)
To find the BER the device compares a received message to an expected message with a bitwise XOR. The remaining 1s indicate mismatches and therefore bit-errors. Modulo-2 and right shifting is used to count them.

Battery Life
Set manually Radio Resource Management The RRM techniques were selected based on their relevance to the network requirements.

Transmission Power
Can affect RSSI and BER
Theoretically can affect battery life of a node

Routing to:
Spread the energy consumption across the network
Ensure strong signal quality Hardware UZBee Dongles
Concern about compatibility
Difficulty of implementation
XBee Development Boards
Series 1
802.15.4
Series 2
ZigBee Stack http://www.flexipanel.com http://www.digi.com Firmware Capabilities
AT Commands
Node Discovery
Power Level
Faults
Network Flooding
No Source Routing


Benefits
Can write a frame for all AT Commands
Capable of Source Routing Transparent Operation API Frames Diagnostic Request Node Discovery Link Assessment Many-to-One Return Routing Algorithm Parameter Setting After receiving the diagnostic request the command ATND is performed.

This returns data about neighbors.

Addresses are isolated and added to a table. All cognitive parameters of neighbors found in the node discovery must be sent to the coordinator.

RSSI found by PWM output of XBee.

BER found by comparing the test message to an expected packet.

A cognitive payload is sent to the coordinator. The coordinator then sends out a message to the routers, telling them what source routes to set and what power levels they should be at.
Source routing has its own API frame

Power level setting is accomplished with the ATPL command Results: Tx Power Limiting Algorithm Results: Path Limiting Algorithm Conclusions Implemented a cognitive multi-hop network in ZigBee capable of automatically adjusting its routes and transmission power levels to maximize the total battery life of the network. This paper has been submitted to the Vehicular Transport Conference in Ottowa Canada 2010 After 40 trials it was calculated that the full diagnostic was successful 87.5% of the time.
The routing algorithms behaved as expected 100% of the time.
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