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Study of admission and control system in a Centralized Cognitive Radio Network
1. Study of admission and control
system in a Centralized Cognitive
Radio Network
Albert Torró Vilert
Master Thesis MASTEAM
2011
2. Introduction
Project based on work of Nicolas Bolivar from the BCDS 1
group of the Universitat de Girona.
Proposes a new Centralized Cognitive Radio Network
(CCRN) with distributed control system.
Study the Cognitive Radio (CR) technology, specifically, the
admission and control system of CCRN proposed.
Develop a simulator program to study and test the behavior
and performance of the model proposed.
2
1
BCDS: Broadband Communications and Distributed Systems
3. Content
1. Cognitive Radio technology
2. Centralized Cognitive Radio Network
3. CCBS – Control System
4. Matlab Program
5. Simulations and results
6. Conclusions and future work
3
4. 1. Cognitive Radio technology
Term initially propose by Joseph Mittola in 1998.
“The related networks are sufficiently computationally intelligent
about radio resources and related computer-to-computer
communications to detect user communications needs...”
Provide the maximum efficiency of the spectrum to improve
its utilization using dynamic spectrum access techniques.
Share the wireless channel with licensed users in
opportunistic manner using spectrum holes.
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5. 1. Cognitive Radio technology
General scheme of CRN with existing network
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6. 1. Cognitive Radio Technology
CRU in a CRN must
Spectrum Sensing → Determine which portions
of the spectrum are available.
Spectrum Decision → Select the best available
channel
Spectrum Sharing → Coordinate access to this
channel with other users
Spectrum Mobility → Vacate the channel when
license user (Primary User) is detected.
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7. Content
1. Cognitive Radio technology
2. Centralized Cognitive Radio Network
3. CCBS – Control System
4. Matlab Program
5. Simulations and results
6. Conclusions and future work
7
10. Content
1. Cognitive Radio technology
2. Centralized Cognitive Radio Network
3. CCBS – Control System
4. Matlab Program
5. Simulations and results
6. Conclusions and future work
10
11. 3. CCBS – CRU Control System
CCBS Control Algorithm
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12. 3. CCBS – CRU Control System
CCBS Control Algorithm
Frequency distribution
Control Broadcast Transmission → Frequency beacon.
Bit 1/Bit 2 Process
00 CCBS and CRU coordination for using a channel
01 CRU request to use a channel
10 CCBS announcing availability 12
11 Frequency slot occupied.
13. 3. CCBS – CRU Control System
CCBS – CRU Control Algorithm
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14. 3. CCBS – CRU Control System
CCBS Control Algorithm
Time-based approach: Identify the PU presence.
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15. Content
1. Cognitive Radio technology
2. Centralized Cognitive Radio Network
3. CCBS – Control System
4. Matlab Program
5. Simulations and results
6. Conclusions and future work
15
16. 4. Matlab Program
Core definition
Study and analyze the behavior of the model.
Simulator that models the proposed control
algorithm.
Input variables are PUs and CRUs, with a traffic
distribution.
The results: Graphical behavior, % of CRUs
request completed and spectrum efficiency.
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17. 4. Matlab Program
Global variables
Number of frequency slots.
Number of CRU.
Time duration.
Input variables → Traffic generation
PU definition → PU table
CRU time definition → CRU time table
CRU frequency definition → CRU frequency table
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18. 4. Matlab Program
Traffic generation
Traffic models
Assign frequency slots and time slots to PUs.
Assign CRU requests along the time slots.
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PU table CRU time table
19. 4. Matlab Program
Traffic generation
Traffic models
Random → Random parameter
Custom → User table specification
Distribution
Mean arrival time → Poisson
Mean duration → Negative Exp.
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20. 4. Matlab Program
Traffic generation
CRU frequency models
Models to assign the available frequencies to
CRUs
CRU frequency table
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21. 4. Matlab Program
Traffic generation
CRU frequency models
Random → Random parameter.
Custom → User table specification.
Band model
Number of bands.
Random parameter.
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22. 4. Matlab Program
Control Algorithm
Independent function
Uses PU table, CRU time table and CRU
frequency table.
Assign white spaces with specific strategy.
Result is the spectrum after control algorithm
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25. Content
1. Cognitive Radio technology
2. Centralized Cognitive Radio Network
3. CCBS – Control System
4. Matlab Program
5. Simulations and results
6. Conclusions and future work
25
26. 5. Simulations and results
Random model simulation results
Random model behavior results
10 PU, 10 CRUs, 10 time slots 128 PU, 128 CRUs, 100 time slots
PU and CRU Random Parameter 0.5 26
CRU random frequency parameter 0.5
27. 5. Simulations and results
Random model simulation results
Evolution of % CRU request completed results
changing random parameters
128 PU, 128 CRUs, 100 time slots 128 PU, 128 CRUs, 100 time slots
Changing CRU random parameter 27
Changing PU random parameter
Fix CRU random parameter to 0.5 Fix PU random parameter to 0.5
28. 5. Simulations and results
Random model simulation results
Evolution of % CRU request completed and
spectrum efficiency changing the number of CRU.
Percentage of CRU request completed Spectrum efficiency
PU and CRU Random Parameter 0.5 28
CRU random frequency parameter 0.5
29. 5. Simulations and results
Distribution model simulation results
Distribution model behavior results
16 PUs, 16 CRUs, 20 time slots 128 PUs, 128 CRUs, 100 time slots
PU & CRU mean arrival time 0.1 CRU Band model with 8 bands 29
PU & CRU mean duration 8. CRU Random Band 0.3
30. 5. Simulations and results
Distribution model simulation results
Comparison between CRU band frequency model
VS CRU random frequency model.
CRU Band frequency model CRU Random frequency model
PU & CRU mean arrival time 0.1 30
PU & CRU mean duration 8
31. Content
1. Cognitive Radio technology
2. Centralized Cognitive Radio Network
3. CCBS – Control System
4. Matlab Program
5. Simulations and results
6. Conclusions and future work
31
32. 6. Conclusions and future work
The basics of Cognitive Radio Networks, which is an
actual under study telecommunications technology
are studied.
Using a simple strategy to share the spectrum
contributes to increase the spectrum efficiency.
95 % of the CRU requests are completed when the
relation of CRU requests respect to the number PUs
or frequency slots is about 75%.
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33. 6. Conclusions and future work
The cooperation and collaboration between Nicolas
Bolivar PhD thesis work and my project was
amazing.
Very good experience to work in a research BCDS
group.
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34. 6. Conclusions and future work
For a complete analysis model, we need to integrate
the modules that are not contemplated in this project
to the simulator.
The study of the behavior of new types of traffic and
frequency distributions that fits the different
standards currently available.
Introduce new algorithms that allow to queue in
memory the requests and serve these petitions in
different time instants.
Method to sense the spectrum is also needed. 34
35. THANK YOU!
QUESTIONS?
Contact:
Albert Torró Vilert
albert.torro@gmail.com
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37. 2. Centralized Cognitive Radio Network
Information and processing module
Sense the frequency spectrum. Considered perfectly
and continuously done.
Digitalize the analog signal from the sensing module.
Store an array of frequency slots and other information
in to the database.
Process the data information stored in the database in
the control channel module and communicate with
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transceiver module.
38. 2. Centralized Cognitive Radio Network
Database module
Store information used for control and
communication module.
Main tables:
Sensing table.
Communication table.
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39. 2. Centralized Cognitive Radio Network
Control channel module
Control the communication between CCBS and
CRUs.
Frequency division and time division multiplexing
techniques.
Cognitive Pilot Channels.
Time slot definition. 39
40. 5. Simulations and results
Distribution model simulation results
Evolution of % CRU request completed changing
the mean arrival time parameter
PU mean arrival time CRU mean arrival time
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