distributed spectrum sensing in unlicensed bands using the...
TRANSCRIPT
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Distributed spectrum sensing in unlicensed bands using the
VESNA platform
Student: Zoltan Padrah
Mentor: doc. dr. Mihael Mohorčič
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Agenda
• Motivation
• Theoretical aspects
• Practical aspects
• Stand-alone spectrum sensing
• Distributed spectrum sensing
• Spectrum sensing testbed
• Experimental results
• Conclusions
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MOTIVATION
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• Introduction • Radio spectrum
– Regulation – Usage
• Using the radio spectrum more efficiently – Approach
• Reusing radio frequency bands – Licensed – Unlicensed
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• Motivation
• Theoretical aspects
• Practical aspects
• Stand-alone spectrum sensing
• Distributed spectrum sensing
• Spectrum sensing testbed
• Experimental results
• Conclusions
Motivation
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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, 2004
1
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Frequency band allocation
image credit: Roke Manor reseach, 2004
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Usage of radio spectrum
• Studies about radio spectrum utilization Left: 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 utilization Left: Cabric et al: Implemenation issues
In spectrum sensing
Bottom: Valenta et al: Survey in spectrum
utilization in Europe
Terminal 1 Terminal 2
Terminal 3
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Usage of radio spectrum
• Studies about radio spectrum utilization Left: Cabric et al: Implemenation issues
In spectrum sensing
Bottom: Valenta et al: Survey in spectrum
utilization in Europe
Terminal 1 Terminal 2
Terminal 3
Terminal 4
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Get information about radio
spectrum
Take decision on the used
frequency band
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Approach
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Get information about radio
spectrum
Take decision on the used
frequency band
Perform database
lookup
Perform sensing with
a radio
Approach
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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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5
Reusing radio spectrum
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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
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THEORETICAL ASPECTS
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• Problem formulation
• Goals
• Hidden terminal and exposed terminal situations
• Spectrum sensing
• Energy detection
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• Motivation
• Theoretical aspects
• Practical aspects
• Stand-alone spectrum sensing
• Distributed spectrum sensing
• Spectrum sensing testbed
• Experimental results
• Conclusions
Theoretical aspects
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Testbed is needed
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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.
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Problem formulation
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• 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.
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Goals
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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 signals
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PRACTICAL ASPECTS
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Practical aspects
• Used devices
• VESNA platform
• Spectrum sensing framework
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• Motivation
• Theoretical aspects
• Practical aspects
• Stand-alone spectrum sensing
• Distributed spectrum sensing
• Spectrum sensing testbed
• Experimental results
• Conclusions
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• 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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Used devices
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• 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
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VESNA platform
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• 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
VESNA platform
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Radio VESNA Communication
and control
Communication
interface Data
storage
On-line
processing
Off-line
processing
Control system
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Spectrum sensing framework
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STANDALONE SPECTRUM SENSING
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• Goals
• Experimental setup
• Calibration results
– CC2500
– CC1101
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• Motivation
• Theoretical aspects
• Practical aspects
• Stand-alone spectrum sensing
• Distributed spectrum sensing
• Spectrum sensing testbed
• Experimental results
• Conclusions
Standalone spectrum sensing
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• Implementation of spectrum sensing functionality
• Calibration of the prototype
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VESNA
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Signal generator
Coaxial Cable
VESNA
Measured signal level
Offset value
Generated signal level
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Experimental setup
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• Absolute error: < 6 dB
• Nonlinearity: < 2 dB
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Calibration CC2500
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• Absolute error: < 8 dB
• Nonlinearity: < 0.5 dB
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Calibration CC1101
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Malfunction
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Calibration CC1101
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DISTRIBUTED SPECTRUM SENSING
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• Goals
• Demonstration – Devices
– Environment
– Representative results
• Device comparison – Introduction
– Environment
– Results
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• Motivation
• Theoretical aspects
• Practical aspects
• Stand-alone spectrum sensing
• Distributed spectrum sensing
• Spectrum sensing testbed
• Experimental results
• Conclusions
Distributed spectrum sensing
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• Demonstrate the functioning of heterogeneous sensing system
• Benchmark
– Devices
– Combinations of devices
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Goals
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• 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 - devices
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Demonstration - environment
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Representative results
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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
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Path loss model
with parameters
Measurement
results from
devices
Fitting
Parameter
values
Error relative
to the model
For each
device
Comparison
Device comparison
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Device comparison
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Seminar II
43
TODO intro
More text,
because work
has been done
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
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Seminar II
44
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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• Architecture
• Goals
• Requirements
• Constraints
• Measurements
– Setup
– Representative results
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• Motivation
• Theoretical aspects
• Practical aspects
• Stand-alone spectrum sensing
• Distributed spectrum sensing
• Spectrum sensing testbed
• Experimental results
• Conclusions
Spectrum sensing testbed
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Architecture
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• Functionality abstracted
in resources
• RESTful design: GET
and POST requests
• All nodes addressable
• Requests initiated by
management and
control part
Architecture
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• Custom application layer
protocol
• Similar to HTTP
Architecture
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• Management and control
part
• Access control
• HTTP interface
• Scriptable
Architecture
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• 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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28
Goals
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• Spectrum sensing data collection
– Performance level
– Nodes Control system
• Reprogramming functionality
– firmware image transmission performance level
– Control system Nodes
• Reliability
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29
Requirements
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• 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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30
Constraints
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• Goal: measuring radio propagation
– For the control network
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31
Measurements - setup
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32 Measurements –
representative results
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EXPERIMENTAL RESULTS
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• Scenario
• Radio wave propagation in the testbed – Link quality
categories
• Experiment scenario
• Results
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• Motivation
• Theoretical aspects
• Practical aspects
• Stand-alone spectrum sensing
• Distributed spectrum sensing
• Spectrum sensing testbed
• Experimental results
• Conclusions
Experimental results
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• In the industrial zone
• 2.4 GHz ISM band
• Emulated behavior
– Scripted
• Observed by multiple nodes
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33
Scenario
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34
Radiowave propagation
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1) Good link quality 2) Medium link quality 3) Bad link quality
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1) 2)
3)
35
Link quality categories
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Experimental scenario
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• Node 17: terminal with cognitive radio capabilities (c)
• Node 2: terminal without cognitive radio capabilities (n)
• Rest of the nodes: observers
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(c)
(n)
37
Node roles in the experiment
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38
Results – Node 25
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39
Results – Node 6
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40
Results – Node 13
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CONCLUSIONS
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• 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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41
Conclusions (1)
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• Spectrum sensing testbed
– Architecture defined
– Network planning performed
– Developed, set up
• Including HTTP like protocol
• Spectrum sensing experiment
– Prepared
– Performed
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Conclusions (2)
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THANK YOU FOR YOUR ATTENTION!
Questions?
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