distributed spectrum sensing in unlicensed bands using the vesna platform student: zoltan padrah...
TRANSCRIPT
Distributed spectrum sensing in unlicensed bands using the
VESNA platform
Student: Zoltan PadrahMentor: doc. dr. Mihael Mohorčič
Seminar II
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Agenda
• Motivation• Theoretical aspects• Practical aspects• Stand-alone spectrum sensing• Distributed spectrum sensing• Spectrum sensing testbed• Experimental results• Conclusions07.12.2012
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MOTIVATION
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Motivation
• Introduction• Radio spectrum
– Regulation– Usage
• Using the radio spectrum more efficiently– Approach
• Reusing radio frequency bands– Licensed– Unlicensed
• Motivation• Theoretical aspects• Practical aspects• Stand-alone spectrum
sensing• Distributed spectrum
sensing• Spectrum sensing
testbed• Experimental results• Conclusions07.12.2012
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Introduction• Radio spectrum1
– Many systems use it: AM, FM, TV broadcast, GSM, UMTS, WiFi, GPS, satellite
– Systems need to coexist– Avoid disturbance (interference)
• Radio spectrum regulation– Frequency band allocation– Each system has its own frequency band
1 image credit: Roke Manor reseach, 200407.12.2012
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Frequency band allocation
image credit: Roke Manor reseach, 200407.12.2012
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Usage of radio spectrum
• Studies about radio spectrum utilizationLeft: Cabric et al: Implemenation issues
In spectrum sensing
Bottom: Valenta et al: Survey in spectrum
utilization in Europe
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Usage of radio spectrum
• Studies about radio spectrum utilizationLeft: Cabric et al: Implemenation issues
In spectrum sensing
Bottom: Valenta et al: Survey in spectrum
utilization in Europe
Terminal 1Terminal 2 Terminal 3
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Usage of radio spectrum
• Studies about radio spectrum utilizationLeft: Cabric et al: Implemenation issues
In spectrum sensing
Bottom: Valenta et al: Survey in spectrum
utilization in Europe
Terminal 1Terminal 2 Terminal 3
Terminal 4
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Approach
Get information about radio spectrum
Take decision on the used frequency band
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Approach
Get information about radio spectrum
Take decision on the used frequency band
Perform database lookup
Perform sensing with a radio
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Reusing radio spectrum
In licensed bands
• Examples: TV VHF, UHF, GSM bands
• Primary user(s)• Secondary user(s)
• Dynamic spectrum access (DSA)
In unlicensed bands
• Examples: ISM bands (868 MHz; 2.4 GHz)
• Multiple equally threated users
• Spectrum Sharing (SP)
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Reusing radio spectrum
In licensed bands
• Examples: TV VHF, UHF, GSM bands
• Primary user(s)• Secondary user(s)
• Dynamic spectrum access (DSA)
In unlicensed bands
• Examples: ISM bands (868 MHz; 2.4 GHz)
• Multiple equally threated users
• Spectrum Sharing (SP)
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THEORETICAL ASPECTS
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Theoretical aspects
• Problem formulation• Goals• Hidden terminal and
exposed terminal situations
• Spectrum sensing• Energy detection
• Motivation• Theoretical aspects• Practical aspects• Stand-alone spectrum
sensing• Distributed spectrum
sensing• Spectrum sensing
testbed• Experimental results• Conclusions07.12.2012
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Problem formulation
Testbed is needed
For solving the artificial spectrum scarcity problem, it is necessary:• Experimental-driven research• Experimental validation and improvement of sensing
algorithms
We assume that either:
a) a radio communication experiment is prepared in an ISM radio frequency band
b) the radio activity in an ISM band is of interest at a given location
In both cases external interference might be observed.07.12.2012
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Goals
• Defining the system architecture for a testbed • Developing software that allows performing spectrum
sensing with the VESNA platform• Spectrum sensing:
– Calibration of multiple VESNA devices– Evaluation of their performance– Performing experiments with them
• Implementation of the functionalities needed for – Integrating multiple VESNA devices in a testbed– Communication system of the testbed, supporting
experiments• Experimental evaluation of the performance of a
VESNA-based spectrum sensing testbed.07.12.2012
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Hidden terminal and exposed terminal situations• Idea: use multiple radios for
observation– Each radio performs partial
detection– Results are centralized
• Resolves the problems:– Hidden transceiver– Hidden receiver
• Relies on other methods for partial detection
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Spectrum sensing
• Detecting other radios• Spectrum sensing methods
– Energy detection– Eigenvalue based detection– Cyclostationary feature detection– Matched filter detection– Collaborative sensing
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Energy detection
• Idea: measure the energy in frequency band and compare it to a threshold
• Simple to implement• Needs correct threshold value: noise floor• Does not work well with spread spectrum signals07.12.2012
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PRACTICAL ASPECTS
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Practical aspects
• Used devices• VESNA platform• Spectrum sensing
framework
• Motivation• Theoretical aspects• Practical aspects• Stand-alone spectrum
sensing• Distributed spectrum
sensing• Spectrum sensing
testbed• Experimental results• Conclusions07.12.2012
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Used devices
• Sensor network based testbed• VESNA platform
– Low-cost, low-complexity• CC1101 radio – 868 MHz ISM band• CC2500 radio – 2.4 GHz ISM band
• The radios can only provide RSSI values– Only energy detection is possible
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VESNA platform• Developed at Jozef Stefan Institute• ST ARM Cortex-M3, 64 MHz• JTAG, USB, USART PC interface• I2C, SPI, PWM, ADC, DAC, USART
sensor and actuator interfaces– Code library: C/C++ (GCC)
• 300-900 MHz, 2.4 GHz radio interface (all ISM bands); – TI CC1101, TI CC2500
• Software tools: Open Source• Eclipse IDE• Tool-chain: GNU Compiler Collection• Cygwin, Linux environment for
Windows• JTAG server: OpenOCD• JTAG hardware interface: Olimex
ARM-USB-OCD07.12.2012
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VESNA platform• Developed at Jozef Stefan Institute• ST ARM Cortex-M3, 64 MHz• JTAG, USB, USART PC interface• I2C, SPI, PWM, ADC, DAC, USART
sensor and actuator interfaces– Code library: C/C++ (GCC)
• 300-900 MHz, 2.4 GHz radio interface (all ISM bands); – TI CC1101, TI CC2500
• Software tools: Open Source• Eclipse IDE• Tool-chain: GNU Compiler Collection• Cygwin, Linux environment for
Windows• JTAG server: OpenOCD• JTAG hardware interface: Olimex
ARM-USB-OCD
Performance:- Comparable to other sensor node platforms,
like TelosB or Sensinode- Lot less processing power than a PC
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Spectrum sensing framework
Radio VESNA Communication and control
Communication
interface Data storage
On-line processing
Off-line processin
g
Control system
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STANDALONE SPECTRUM SENSING
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Standalone spectrum sensing
• Goals• Experimental
setup• Calibration results
– CC2500– CC1101
• Motivation• Theoretical aspects• Practical aspects• Stand-alone
spectrum sensing• Distributed spectrum
sensing• Spectrum sensing
testbed• Experimental results• Conclusions07.12.2012
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Goals
• Implementation of spectrum sensing functionality
• Calibration of the prototype
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Experimental setup
Signal generator
Coaxial Cable
VESNA
Measured signal level
Offset value
Generated signal level
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Calibration – CC2500
• Absolute error: < 6 dB• Nonlinearity: < 2 dB
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Calibration – CC1101
• Absolute error: < 8 dB• Nonlinearity: < 0.5 dB
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Calibration – CC1101
Malfunction07.12.2012
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DISTRIBUTED SPECTRUM SENSING
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Distributed spectrum sensing
• Goals• Demonstration
– Devices– Environment– Representative
results• Device comparison
– Introduction– Environment– Results
• Motivation• Theoretical aspects• Practical aspects• Stand-alone spectrum
sensing• Distributed spectrum
sensing• Spectrum sensing
testbed• Experimental results• Conclusions07.12.2012
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Goals
• Demonstrate the functioning of heterogeneous sensing system
• Benchmark– Devices– Combinations of devices
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Demonstration – devices
• eZ430-RF2500• Texas Instruments wireless
development tool• MSP430 CPU• CC2500 radio
• USRP2• Universal Software Radio Peripheral• SBX daugthterboard• Software defined radio device• GNU radio software
• VESNA• CC2500 radio
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Demonstration – environment
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Representative results
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Device comparison
Path loss model with parameters
Measurement results from
devices
Fitting
Parameter values
Error relative to the model
For each
device
Comparison
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Device comparison
Path loss model with parameters
Measurement results from
devices
Fitting
Parameter values
Error relative to the model
For each
device
Comparison
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Device comparison
Path loss model with parameters
Measurement results from
devices
Fitting
Parameter values
Error relative to the model
For each
device
Comparison
• One static continuous transmission
• Multiple measurement locations
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Device comparison
Path loss model with parameters
Measurement results from
devices
Fitting
Parameter values
Error relative to the model
For each
device
Comparison
• One static continuous transmission
• Multiple measurement locations
Mean Squared Error (MSE): average of squared error values for each data point
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Environment
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Results - plotted
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Results - numerical
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SPECTRUM SENSING TESTBED
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Spectrum sensing testbed
• Architecture• Goals• Requirements• Constraints• Measurements
– Setup– Representative
results
• Motivation• Theoretical aspects• Practical aspects• Stand-alone spectrum
sensing• Distributed spectrum
sensing• Spectrum sensing
testbed• Experimental results• Conclusions07.12.2012
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Architecture
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Architecture• Functionality abstracted
in resources• RESTful design: GET
and POST requests• All nodes addressable• Requests initiated by
management and control part
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Architecture
• Custom application layer protocol
• Similar to HTTP
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Architecture
• Management and control part
• Access control• HTTP interface• Scriptable
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Goals
• Everything configurable remotely– No physical access
• Unified control interface– Simple design and usage
• Centralized control and data collection– Simplicity, reliability
• Possibility of easily adding functionality in the future
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Requirements
• Spectrum sensing data collection– Performance level– Nodes Control system
• Reprogramming functionality– firmware image transmission performance
level– Control system Nodes
• Reliability
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Constraints
• Availability of Internet access– for the gateway node
• Location of light poles• Power connections to the light poles• Radio connectivity• Possibilities for experiments
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Measurements – setup
• Goal: measuring radio propagation– For the control network
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Measurements – representative results
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EXPERIMENTAL RESULTS
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Experimental results
• Scenario• Radio wave
propagation in the testbed– Link quality
categories• Experiment
scenario• Results
• Motivation• Theoretical aspects• Practical aspects• Stand-alone spectrum
sensing• Distributed spectrum
sensing• Spectrum sensing
testbed• Experimental results• Conclusions07.12.2012
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Scenario
• In the industrial zone• 2.4 GHz ISM band• Emulated behavior
– Scripted• Observed by multiple nodes
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Radio wave propagation
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Link quality categories
1) Good link quality
2) Medium link quality
3) Bad link quality
1) 2)
3)
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Experiment scenario
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Node roles in the experiment
• Node 17: terminal with cognitive radio capabilities (c)
• Node 2: terminal without cognitive radio capabilities (n)
• Rest of the nodes: observers
(c)
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Results – Node 25
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Results – Node 6
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Results – Node 13
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CONCLUSIONS
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Conclusions (1)
• Spectrum sensing: energy detection is suitable for low-complexity platform
• Stand-alone spectrum sensing prototype– Developed– Calibrated– Integrated in a heterogeneous system– Accuracy has been determined
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Conclusions (2)
• Spectrum sensing testbed– Architecture defined– Network planning performed– Developed, set up
• Including HTTP like protocol
• Spectrum sensing experiment– Prepared– Performed
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THANK YOU FOR YOUR ATTENTION!
Questions?
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