Cognitive Radio and Smart Grid

Dr. Robert C. QiuProfessor

Wireless Networking Systems LaboratoryDepartment of Electrical and Computer Engineering

Tennessee Technological UniversityFebruary 18, 2010

Presented at IEEE Chapter, Huntsville, ALEmail: rqiu@tntech.edu http://iweb.tntech.edu/rqiu

Cognitive RadioCognitive Radio

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Outline

■ Cognitive Radio■ Cognitive Radio @ Tennessee Tech University□ Spectrum Sensing and Wideband Spectrum Sensing□ Cognitive Radio Networks and Testbeds□ Cognitive Radio Networks and Testbeds□ Wideband Beamforming Testbeds

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Window of Opportunity

■ Existing spectrum policy forces spectrum to behave like a fragmented disk ■ Bandwidth is expensive and good frequencies are taken■ Unlicensed bands – biggest innovations in spectrum efficiency■ Recent measurements by the FCC in the US show 70% of the allocated

spectrum is not utilized ■ Time scale of the spectrum occupancy varies from msecs to hours■ Time scale of the spectrum occupancy varies from msecs to hours

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Spectrum Holes

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Spectrum Sharing

■ Existing techniques for spectrum sharing:□ Unlicensed bands (WiFi 802.11 a/b/g)□ Underlay licensed bands (UWB)□ Opportunistic sharing□ Recycling (exploit the SINR margin of legacy systems) □ Spatial Multiplexing and Beamforming□ Spatial Multiplexing and Beamforming

■ Drawbacks of existing techniques:□ No knowledge or sense of spectrum availability□ Limited adaptability to spectral environment□ Fixed parameters: BW, Fc, packet lengths, synchronization,

coding, protocols, …■ New radio design philosophy: all parameters are adaptive

□ Cognitive Radio Technology

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Cognitive Radio

■ Term coined by Mitola in 1999■ There is no such definition accepted by all researchers■ Mitola’s definition:

□ Software radio that is aware of its environment and its capabilities□ Alters its physical layer behavior□ Capable of following complex adaptation strategies□ Capable of following complex adaptation strategies

■ “A radio or system that senses, and is aware of, its operational environment and can dynamically and autonomously adjust its radio operating parameters accordingly”

■ Learns from previous experiences■ Deals with situations not planned at the initial time of design

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What is a Cognitive Radio?

■ Fixed radios□ are set by their operators

■ Adaptive radios□ can adjust themselves to accommodate anticipated events

■ Cognitive radios□ can sense their environment and learn how to adapt□ can sense their environment and learn how to adapt

■ Beyond adaptive radios, cognitive radios can handle unanticipated channels and events.

■ Cognitive radios require:□ Sensing□ Adaptation□ Learning

■ Cognitive radios intelligently optimize their own performance in response to user requests and in conformity with FCC rules.

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What is a Cognitive Radio?

■ Cognitive radios are machines that sense their environment (the radio spectrum) and respond intelligently to it.

■ Like animals and people they□ seek their own kind (other radios with which they want to

communicate)□ avoid or outwit enemies (interfering radios)□ find a place to live (usable spectrum)□ conform to the etiquette of their society (the Federal

Communications Commission)□ make a living (deliver the services that their user wants)□ deal with entirely new situations and learn from experience

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What is a Cognitive Radio?

■ Cognitive radio requirements□ co-exists with legacy wireless systems□ uses their spectrum resources □ does not interfere with them

■ Cognitive radio properties■ Cognitive radio properties□ RF technology that "listens" to huge swaths of spectrum □ Knowledge of primary users’ spectrum usage as a function of

location and time□ Rules of sharing the available resources (time, frequency, space)□ Embedded intelligence to determine optimal transmission

(bandwidth, latency, QoS) based on primary users’ behavior

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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 capabilities, is able to independently alter its physical layer behavior, and is capable of following complex adaptation strategies.

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Cognitive Radio Advantages

■ All of the benefits of software defined radio■ Improved link performance

□ Adapt away from bad channels□ Increase data rate on good channels

■ Improved spectrum utilization□ Fill in unused spectrum□ Fill in unused spectrum□ Move away from over occupied spectrum

■ New business propositions□ High speed internet in rural areas□ High data rate application networks (e.g., Video-conferencing)

■ Significant interest from FCC, DoD□ Possible use in TV band refarming

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Cognitive radios are a powerful tool for solving two major problems:

■ 1) Access to spectrum (finding an open frequency and using it)

■ 2) Interoperability (talking to legacy radios using a variety of incompatible waveforms)

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Cognitive Radio Platform

Application Scenarios

Licensed network Third party access in licensed networks

Cellular, PCS band

Improved spectrum efficiency

Improved capacity

TV bands (400-800 MHz)

Non-voluntary third party access

Licensee sets a protection

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Unlicensed network

Improved capacity Licensee sets a protection threshold

Automatic frequency coordination

Interoperability

Co-existence

ISM, UNII, Ad-hoc

… …

Secondary markets

Public safety band

Voluntary agreements between licensees and third party

Limited QoS

Outline

■ Cognitive Radio■ Cognitive Radio @ Tennessee Tech University□ Spectrum Sensing and Wideband Spectrum Sensing□ Cognitive Radio Networks and Testbeds□ Cognitive Radio Networks and Testbeds□ Wideband Beamforming Testbeds

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Wideband (Multi-GHz) Cognitive Radio

• 2008 NSF MRI Award

• List price of $400,000

•Testbed for Cognitive Radio, Cognitive Radar, Anti-Jamming

• Information Security

• Latest Technology

Spectrum Sensing

■ Key part of Cognitive Radio□ Sense the channel and decide whether primary user exists

■ Example□ IEEE 802.22

▪ Sense digital TV (DTV) signal□ Sensitivity is very high□ Sensitivity is very high

▪ Capable of detecting DTV signal under -20 dB SNR!▪ Detection rate >= 90%▪ False alarm rate <= 10%▪ Sensing time <= 2 sec

■ Two major methods we will implement:□ Fourier transform based□ Covariance matrix based

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Spectrum Sensing

■ Fourier transform based pilot energy and location detection□ There is a strong pilot tone in DTV signal

▪ Spectrum sensing reduced to pilot tone sensing▪ The frequency location of pilot tone is known▪ Use FFT to find pilot tone energy and its location

□ Detecting in the Fourier domain is much easier!□ Detecting in the Fourier domain is much easier!▪ Simply find the maximum value in the Fourier domain

▫ C. Cordeiro, M. Ghosh, D. Cavalcanti, and K. Challapali, “Spectrum sensing for dynamic spectrum access of TV bands,” in Second International Conference on Cognitive Radio Oriented Wireless Networks and Communications, Orlando, Florida, Aug. 2007.

DTV Spectrum Measured by IEEE 802.22

DTV Pilot is approx. 25 dB stronger “locally”

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Spectrum Sensing

■ Covariance matrix based signal detection□ The statistics of signal is different from that of noise□ The difference is characterized by the non-diagonal elements of

the covariance matrix▪ Y. H. Zeng and Y.-C. Liang, “Covariance based signal detections for cognitive radio,” in

Proceedings of the 2nd IEEE (DySPAN ’07), pp.202–207, April 2007.Proceedings of the 2 IEEE (DySPAN ’07), pp.202–207, April 2007.

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Covariance Matrix of Signal Covariance Matrix of Gaussian White Noise

Non-diagonal elements are ZERONon-diagonal elements are NON-ZERO19 2/23/2010

Spectrum Sensing

■ Flow-chart of the covariance matrix based detection

Transform the covariance matrix

Choose the matrix size and the threshold r

Sample and filter the signals

Compute the covariance matrix

Decision: if T1 >r*T2,signal exists;Otherwise, signal not exists.

Compute the absolute sum of the matrix, T1, and the absolute sum ofdiagonal elements, T2

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▪ Y. H. Zeng and Y.-C. Liang, “Covariance based signal detections for cognitive radio,” in Proceedings of the 2nd IEEE (DySPAN ’07), pp.202–207, April 2007.

Spectrum Sensing

■ Simulation Results▪ EG-xdB: Traditional energy detection with xdB noise uncertainty▪ CAV: Covariance absolute value ▪ CFN: Covariance Frobenius norm

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▪ Y. H. Zeng and Y.-C. Liang, “Covariance based signal detections for cognitive radio,” in Proceedings of the 2nd IEEE (DySPAN ’07), pp.202–207, April 2007.

Spectrum Sensing

■ Implementing a REAL-TIME spectrum sensing prototype

RF Transceiver

Module

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ADC/DAC Module

Spectrum Sensing

Algorithm using

FPGA/DSPLyrtech SDR development platform

Wideband Spectrum Sensing

Outline

■ Cognitive Radio■ Cognitive Radio @ Tennessee Tech University□ Spectrum Sensing and Wideband Spectrum Sensing□ Cognitive Radio Networks and Testbeds□ Cognitive Radio Networks and Testbeds□ Wideband Beamforming Testbeds

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Cognitive Radio Networks

Internet or othernetworks

SU3SU4

SU1

Statistical learningof network behavior

Robust control for

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Internet or othernetworks

Primary user(PU)

Secondary user(SU)

SU5

SU2

PU1

Robust control forcomplex cognitive

network

Experimentaltestbeds

Lyrtech SFF SDR Development Platform (1)

■ RF frequency range□ TX: 200 MHz to 930 MHz□ RX: 30 MHz to 928 MHz

■ Full-duplex transceiver■ Selectable bandwidth: 5 MHz/20 MHz■ RF input

□ Gain: 22 dB (RX selectable filter: 20 MHz)□ Gain: 22 dB (RX selectable filter: 20 MHz)□ Phase noise at 100 kHz from carrier: –101 dBc (RF: 425 MHz)□ Phase noise at 10 kHz from carrier: –73 dBc (RF: 425 MHz)□ Sensitivity: –105 dBm (BW: 300 kHz, SNR: 0 dB)

■ RF output□ Carrier suppression: –35 dBc□ Sideband suppression: –37 dBc□ Phase noise at 100 kHz from carrier: –109 dBc (RF: 425 MHz)□ Phase noise at 10 kHz from carrier: –83 dBc (RF: 425 MHz)

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Lyrtech SFF SDR Development Platform (2)

■ Digital Processing Module□ Texas Instruments TMS320DM6446 DSP□ Xilinx Virtex-4 SX35 FPGA□ 128-MB DDR2 SDRAM and NAND flash memory□ Texas Instruments Stereo Audio codec (8 kHz to 48 kHz)□ 10/100-Mbps Ethernet□ 10/100-Mbps Ethernet□ High-speed USB (USB 2.0)

■ Data Conversion Module□ Two 14-bit, 125-MSPS

input channels (TI ADS5500)□ Dual-channel 16-bit,

500-MSPS output channels (TI DAC5687)

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Cognitive Radio Testbeds in the Wireless Lab of Tennessee Tech University

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Building Cognitive Radio Networks Using Lyrtech Platforms

■ All of the nodes and super nodes are connected using Ethernet cable through an Ethernet switch to computers.

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Available Equipments and Testbeds for Cognitive Radio

■ Advanced equipments□ DPO - Tektronix DPO72004: 20 GHz bandwidth, 50 GS/s

sampling rate□ AWG - Tektronix AWG7122B: 12 GS/s per channel, dual

channel□ Funded by NSF MRI

■ Narrowband Cognitive Radio Testbed □ Texas Instruments (TI) Software Defined Radio (SDR)

Development Platform (x2)▪ TMS320DM6446 DSP

▫ 594 MHz TMS320C64x+™ DSP core▫ 297 MHz ARM926 core

▪ Xilinx Virtex-4 SX35 FPGA▪ RF: 360 MHz – 960 MHz, Bandwidth: 5 MHz or 20 MHz

□ Used for testing concepts and algorithms

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Available Equipments and Testbeds for Cognitive Radio

■ Multi-GHz Wideband Cognitive Radio Testbed□ Evolution of the UWB Time Reversal Testbed

▪ 1.5 GHz 8-bit ADC, 1 GHz 14-bit DAC▪ 3.3 GHz – 4.4 GHz Local Oscillator▪ 500 MHz bandwidth

□ To our knowledge, existing cognitive radio testbeds only have a maximum bandwidth of 100 MHz

□ Wideband cognitive radio can take full advantage of cognitive radio - large □ Wideband cognitive radio can take full advantage of cognitive radio - large spectrum coverage

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Outline

■ Cognitive Radio■ Cognitive Radio @ Tennessee Tech University□ Spectrum Sensing and Wideband Spectrum Sensing□ Cognitive Radio Networks and Testbeds□ Cognitive Radio Networks and Testbeds□ Wideband Beamforming Testbeds

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Sundance SMT702

• Two or more SMT702 can be installed in the NI PXIe-1082 Chassis• Synchronization of both boards can be make using external clock

3333

Smart GridSmart Grid

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Outline

■ Traditional Power Grid■ Smart Grid□ What is Smart Grid?□ Key Technologies□ Challenges□ Current Example applications

■ Smart Grid @ Tennessee Tech University■ Conclusion

The Traditional Power Grid■ The grid we are using

□ Many implementation decisions were made 120 years ago

Generation, transmission, distribution

http://oncor.com/images/content/grid.jpg

The Traditional Power Grid

■ Problems with current Power Grid□ It is not efficient

▪ Transmission losses = 20%▪ Only 30% of the energy consumed is transmitted to consumers

□ It has not kept pace with modern challenges▪ Security threats from energy suppliers or cyber attack▪ Security threats from energy suppliers or cyber attack▪ Limited alternative power generation sources▪ No solutions for conservative use of energy▪ Un-interruptible electricity supply▪ Poor situation awareness▪ Poor control and management of distribution network

■ A “SMARTER” grid is needed!

Outline

■ Traditional Power Grid■ Smart Grid□ What is Smart Grid?□ Key Technologies□ Challenges□ Current Example applications

■ Smart Grid @ Tennessee Tech University■ Conclusion

What Is Smart Grid?

■ Smart Grid is an application of digital information technology to optimize electrical power generation, delivery and use□ Optimize power delivery and generation□ Self-healing□ Consumer participation□ Resist attack□ High quality power□ Accommodate generation options

What Is Smart Grid?

• Optimize power delivery and generation□ Advanced efficient power generation□ Low loss delivery power lines

■ Self-healing□ Real-time awareness and reaction of system problems□ Real-time awareness and reaction of system problems

■ Consumer participation□ Consumer can monitor and control “smart appliances” to

manage energy use and reduce energy cost

What Is Smart Grid?

Consumer participation [1]

Added green power sources

High-speed, networked

What Is Smart Grid?

Plug-in hybrid electric cars

Smart thermostats, appliances and in-home control devices

Real-time and green pricing signals

networked connections

Customer interaction with utility

http://www.worldchanging.com/smarthouse.jpg

What Is Smart Grid?

■ Resist attack□ Real time monitoring of power grids□ Identify and respond to man-made or natural disruptions□ Isolate affected areas and redirect power flows around damaged

facilities

■ High quality power□ Reduce high losses due to outages and power quality issues□ Those issues cost US more than $100 billion each year!

What is Smart Grid?

Smart Grid would save 46~117 billion dollars over the next 20 years!

[2]

Key Technologies

■ Integrated communications□ Fast and reliable communications for the grid□ Allowing the grid for real-time control, information and data

exchange to optimize system reliability, asset utilization and security

□ Can be wireless, powerline or fiber-optics□ Can be wireless, powerline or fiber-optics□ For wireless

▪ Zigbee▪ WiMAX▪ WiFi

Generating Plant

Transmission Line

Substation

� Broadband over Powerlines — Provide for two-way

communications� Monitors and smart relays at

substations

Key Technologies

End UserDistribution System

substations� Monitors at transformers,

circuit breakers and reclosers

� Bi-directional meters with two-way communication

[1]

Key Technologies

■ Sensing and measurement□ Smart meter technology, real time metering of:

▪ Congestion and grid stability▪ Equipment health▪ Energy theft▪ Real time thermal rating▪ Real time thermal rating▪ Electromagnetic signature measurement/analysis▪ Real time pricing

□ Phasor measurement units (PMU)▪ Real time monitor of power quality▪ Use GPS as a reference for precise measurement

Key Technologies

Example: FNET-Frequency monitoring NETworkTTU is involved!

http://www.cesr.tntech.edu/about%20fnet/default.html

Key Technologies

■ Advanced components□ Flexible AC transmission system devices□ High voltage direct current□ Superconducting wire□ High temperature superconducting cable□ High temperature superconducting cable□ Distributed energy generation and storage devices□ Composite conductors□ “Intelligent” appliances

Key Technologies

■ Power system automation□ Rapid diagnosis and precise solutions to specific grid disruptions

or outages□ Distributed intelligent agents□ Analytical tools involving software algorithms and high-speed

computerscomputers□ Operational applications

Key Technologies

Fujian power grid, China:

Wide area protection system:AI programming techniques to calculate control strategies

Voltage Stability Monitoring & Control Voltage Stability Monitoring & Control (VSMC) software:Sensitivity-based successive linear programming method to determine optimal control solution

[3]

Challenges

■ IEEE P2030 project defined three task forces:□ TF1: Power Engineering Technology□ TF2: Information Technology□ TF3: Communications Technology

Challenges

■ TF1: Power Engineering Technology□ Energy sources□ Transmission □ Substation□ Distribution□ Distribution□ Consumer premise□ Cyber security□ Safety

Challenges

■ TF2: Information Technology□ Cyber security□ Management protocols□ Coordination with TF1

▪ Provide data storage requirements▪ Data retrieval performance requirements▪ Define data interfaces

□ Coordination with TF3▪ Communication link▪ Topology control▪ Protocol

Challenges

■ TF3: Communications Technology□ Define communication requirements between devices□ Identify existing communication standards and definitions for use

in Smart Grid

Current Example applications

■ Austin, Texas, 1st Smart Grid city in US

http://www.inhabitat.com/wp-content/uploads/15-grid-537x324.jpg

Current Example applications

■ Xcel Smart City in Boulder

http://smartgridcity.xcelenergy.com

Current Example applications

■ Energy Smart Miami

http://tinycomb.com/wp-content/uploads/2009/05/smart-grid.jpg

Current Example applications

■ GE “Plug into the Smart Grid”

http://ge.ecomagination.com/smartgrid

Outline

■ Traditional Power Grid■ Smart Grid□ What is Smart Grid?□ Key Technologies□ Challenges□ Current Example applications

■ Smart Grid @ Tennessee Tech University■ Conclusion

Wind Energy

Solar Energy

(Donated by TVA)

A Real-Time Smart Grid Testbed in Tennessee Technological University

Dorms

Apartments

Water Energy

Offices

Long RangeLong Range

Wireless Network:

Cognitive Radio, WiMAX

Short Range

Wireless Network:

ZigBee, WiFi, UWB, etc.

Electric Grid

(Donated by TVA)

Overlaid by 2-way highly secured wireless network

Including 40 Cognitive Radio nodes

(Sponsored by DoD 2010 Defense Earmark Project)

Cookeville Utilities

Smart Automation& Control

Smart Power Grid

Outline

■ Traditional Power Grid■ Smart Grid□ What is Smart Grid?□ Key Technologies□ Challenges□ Current Example applications

■ Smart Grid @ Tennessee Tech University■ Conclusion

Conclusion

■ Smart Grid is the next generation Power Grid■ Involve many new technologies■ Cooperation within multiple areas of research■ $4.5 billions investment by government

It’s a big deal!!!

Thank you!

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