1. IEEE TUTORIAL WEA 2012
“Cross Layer Analysis for a Dynamic
Cross
Spectrum Allocation System’’
System’’
- A Cognitive Sensor Network Testbed Approach Autonomous Metropolitan University - Iztapalapa
Electrical Engineering Department
Enrique Rodríguez de la Colina, PhD.
erod@xanum.uam.mx
May 2012
1
2. Outline
Introduction
Cognitive Radio Networks (CRN)
Dynamic Spectrum Allocation (DSA)
Main Functions of a Cognitive Radio Device
Sensing, decision making, sharing, mobility
DSA - Test Bed Design
CR - Sensor Networks Approach
Sensing and media access control (MAC)
Decision Making
Control & MAC
Communication Interface and Upper Layers Applications
Proposals
Software defined radio (SDR)
Sustainable design
Energy consumption and adapting power
Conclusions & Future Work
Challenges , design, technology and future applications
2
4. Cognitive Radio Networks (CRN)
• Wireless Communication model where
the devices adjust their parameters
transmission
and reception
4
5. Background
• Most of the RF spectrum is assigned to licensed
communications by governments
5
6. Context
Frequency bands are regulated by governments where fixed
frequency bands are assigned. Thus the policies of use depends on,
Geography
Population
Local use of the frequencies
6
7. Context
• Free frequency bands are saturated by the increase of wireless
technologies and applications
7
9. Context
• Inefficient use of the private regions of the spectrum
• Management in time, frequency, coding and space
9
1. Wellens, Matthias; Wu, Jin; Mahonen, Petri;, “Evaluation of Spectrum Occupancy in Indoor and
Outdoor Scenario in the Context of Cognitive Radio”, IEEE Cognitive Radio Oriented Wireless Networks
and Communications, 2007
10. Cognitive Radio Networks (CRN)
• CR devices can be classified as,
Full cognitive radio
• (Mitola’s radio)1
• All the parameters observed by a node are considered for adaptation
CR device for dynamic spectrum allocation (DSA)
• ‘Spectrum Sensing Cognitive Radio’
• This approach considers only the frequency spectrum changes
10
et. al.
1. J. Mitola III and G.Q Maguire, Jr., “Cognitive Radio: Making Software Radios More Personal”, IEEE
Personal Communications (Wireless Communications), vol.6, no. 4, pp. 13-18, August 1999.
11. Cognitive Radio Networks
• Operative parameters change based on monitoring several factors
• Changes are induced by external and internal parameters such as,
Communication characteristics, e.g. utilization
Power energy
Social behavior
Tx/Rx parameters
RF spectrum changes
Eb/No
Frequency
11
time
12. Dynamic Spectrum Allocation (DSA)
The “Dynamic Spectrum Allocation” (DSA) solves some issues
for the frequency spectrum,
the waste of frequency spectrum bands due to few use
the increase number of wireless systems in some frequency portions
the random use of the spectrum bands
QoS for wireless services
12
13. Cognitive Radio Networks
One premise is to avoid interference with licensed users
Licensed users (primary users)
No licensed users (secondary users)
Then it is required to locate devices fast and with accuracy to avoid
delays
Systems with different characteristics
heterogeneous
and homogeneous
Applications adaptation
13
14. Cognitive Radio Networks Fundamentals
Depending of the spectrum availability, the CR devices can be identified as,
Cognitive Radio Devices in licensed bands
which operates in coexistence with primary users, for example, in U.S.A. this
systems operate in digital TV bands
Cognitive Radio Devices away from licensed bands
this devices operate only out of the licensed bands of the frequency
spectrum,
•
most of experimental testsbed or
•
in free licensed bands
14
15. Cognitive Radio Networks Fundamentals
Using previous definitions the CRN can be classified as in [2],
Underlay:
The frequency section used by these CR devices is also used by
primary users where CR devices mainly use spread spectrum
techniques to avoid interference
Overlay:
The frequency section used by the CR devices is not occupied by
licensed users, so the interference to primary users is not considerable
15
2. A. M. Wyglinski, M. Nekovee, and Y. T. Hou, Cognitive Radio Communications and
Networks, Principles and Practice, vol. ISBN 978-0-12-374715-0 (alk. paper): Elsevier, 2010.
16. Cognitive Radio Networks Fundamentals
16
2. A. M. Wyglinski, M. Nekovee, and Y. T. Hou, Cognitive Radio Communications and
Networks, Principles and Practice, vol. ISBN 978-0-12-374715-0 (alk. paper): Elsevier, 2010.
17. Cognitive Radio Networks Challenges
Main challenges for the protocols implemented in CR devices
Spectrum random changes
Noise and interference
Communication collision between users
17
18. CR devices basic functions
Prof. Ian F. Akyildiz in “A Survey on Spectrum Management in Cognitive
Radio Networks” [3] among other authors explains main functions of
CR devices,
‘Spectrum Sensing’: sensing the spectrum holes. The spectrum sensing
function enables the cognitive radio to adapt to its environment by detecting
spectrum holes.
‘Spectrum decision’: a cognitive radio determines the data rate, the
transmission mode, and the bandwidth of the transmission. Then, the
appropriate spectrum band is chosen according to the spectrum
characteristics and user requirements.
‘Spectrum sharing’: coordination and collaboration with other devices
‘Spectrum mobility’ : mobility and connection management approaches to
reduce delay and loss during spectrum handoff
18
20. Spectrum management
Tasks
Cognitive Function
Determine white spaces
¿how?
Sensing
¿when?
Decision making
Coordination and collaborative tasks with others
¿who?
Coordination
Moving and hand off
¿where?
Mobility
Decision making and media access
Protocols and control of the CR device
20
22. Funciones principales
• Ian F. Akyildiz3 :
22
3. F. Akyildiz, W.Y. Lee, M.C. Buran, and S. Mohanty, “A survey on spectrum management in cognitive
radio networks”, IEEE Communications Magazine, vol. 46, no. 4, pp. 40-48, April 2008.
23. Functions
• Ian F. Akyildiz3 et al. :
‘Spectrum Sensing’
Spectrum monitoring
‘Spectrum decision’: decision
making, media
selecting
‘Spectrum
coordination
with others
‘Spectrum
access
and
sharing’:
and
collaboration
mobility’:
communications must continue
moving to another spectrum
portion
when
primary users
presence
23
3. F. Akyildiz, W.Y. Lee, M.C. Buran, and S. Mohanty, “A survey on spectrum management in cognitive
radio networks”, IEEE Communications Magazine, vol. 46, no. 4, pp. 40-48, April 2008.
24. Functions
• Ian F. Akyildiz3 et al. :
‘Spectrum Sensing’
Spectrum monitoring
‘Spectrum decision’: decision
making, media
selecting
‘Spectrum
coordination
with others
‘Spectrum
access
and
sharing’:
and
collaboration
mobility’:
communications must continue
moving to another spectrum
portion
when
primary users
presence
24
3. F. Akyildiz, W.Y. Lee, M.C. Buran, and S. Mohanty, “A survey on spectrum management in cognitive
radio networks”, IEEE Communications Magazine, vol. 46, no. 4, pp. 40-48, April 2008.
25. Funciones principales
• Ian F. Akyildiz3 et al. :
‘Spectrum Sensing’
Spectrum monitoring
‘Spectrum decision’: decision
making, media
selecting
‘Spectrum
coordination
with others
‘Spectrum
access
and
sharing’:
and
collaboration
mobility’:
communications must continue
moving to another spectrum
portion
when
primary users
presence
25
3. F. Akyildiz, W.Y. Lee, M.C. Buran, and S. Mohanty, “A survey on spectrum management in cognitive
radio networks”, IEEE Communications Magazine, vol. 46, no. 4, pp. 40-48, April 2008.
26. Funciones principales
• Ian F. Akyildiz3 et al. :
‘Spectrum Sensing’
Spectrum monitoring
‘Spectrum decision’: decision
making, media
selecting
‘Spectrum
coordination
with others
‘Spectrum
access
and
sharing’:
and
collaboration
mobility’:
communications must continue
moving to another spectrum
portion
when
primary users
presence
26
3. F. Akyildiz, W.Y. Lee, M.C. Buran, and S. Mohanty, “A survey on spectrum management in cognitive
radio networks”, IEEE Communications Magazine, vol. 46, no. 4, pp. 40-48, April 2008.
27. DSA proposal
UPPER
MAC
PHYSICAL
PROTOCOL STACK ( LAYERS )
• Cross-layer approach
Application
• Interfaces
• Services
Transport
• UDP
Routing
• Multi-hop
• TCP
• Point-to-Point
Protocol adaptation
Access Control
• Coordinate
• Sharing
Front-end
• Communication
• Sensing
Proactive
Decision
Module
Reactive
Spectrum
Analysis
Environment
Data
Control
Module
Information
27
28. DSA proposal
UPPER
MAC
PHYSICAL
PROTOCOL STACK ( LAYERS )
• Physical Layers
Application
• Interfaces
• Services
Transport
• UDP
Routing
• Multi-hop
• TCP
• Point-to-Point
Protocol adaptation
Access Control
• Coordinate
• Sharing
Front-end
• Communication
• Sensing
Proactive
Decision
Module
Reactive
Spectrum
Analysis
Environment
Data
Control
Module
Information
28
30. Spectrum Sensing
Shadowing
Reflection
Refractions
CR devices have the same difficulties that wireless networks,
Noise and Interference
Collisions between users
30
Fig. Reference: John Schiller, Mobile Communications, Addison Wesley, 2a Ed., 2003
31. Sensing
• Spectral analysis
• Opportunities detection
• Performing time
• Energy detection based on
levels (blind detection)
• Digitalizing the spectrum
management
• Availability vector
• binary
• Occupied or available
31
32. Sensing
• Test of the spectrum
occupancy:
• Metageek device
• Drawbacks
• Private software
• Input data precision
32
33. Sensing and monitoring
•
•
•
•
•
State Machine device implemented
Implementation using PICs
Developing a MAC system
Work on the communication interface
Proposed to use 2 interfaces to speed-up the system
• For detection
• For communication
Communications
interface
(Microcontroller)
Media Access Module
(MAC)
& detection
33
34. Challenges for the Physical Layer
• Parallelization
• Space diversity
• Improve resolution
•
•
•
•
•
Reception range
Bandwidth
Devices operation
New techniques integration
Embedded systems
34
35. Sensing for a Sensor Network
• Approach:
• Interface IEEE 802.15.4 (ZigBee)
• Detection procedure
• Sequential
• Parallel
• Monitoring time response
(200ms)
• Drawbacks
• Communication between hardware
• Technologies limitations per se
35
36. Single sensing module
• ‘XBee – PIC’ module for testing
XBee
Modul
e
Slave
Signal
output
Star
t bit
36
37. Single sensing module
• Sensing - delays in the detection and delays histogram
Processing delay
Processing delay histogram
900
300
850
750
700
650
600
550
0
50
100
150
200
250
Iteration
300
Iteration
350
400
450
500
200
Frequency
occurrence
800
D e la y [m s]
Delay [ms]
250
150
100
50
0
550
600
650
700
750
800
850
900
Delay [ms]
Delay [ms]
37
40. Cognitive characteristics for
wireless sensor networks
• Integration
• Time response and bandwidth restrictions
• Interfaces with other modules is a challenge
• Energy consumption limitations
40
41. ‘Diversity’ principles, example
• Model of communication with Additive White
Gaussian Noise (AWGN) channel and being time
variant
• the channel characteristic varies in average over
the time,
• acceptable quality detection 90 % of the time
• poor quality detection 10 % of the time
• bit error rate (BER) of 10-10 for the acceptable
quality detection,
• BER of 0.5 for the poor quality reception
41
42. Sensing – diversity principles
time %
0.9
BER
1.0E-10
time %
0.1
BER
0.5
5.00E-02
2.50E-02
2 antennas
Antena 1
signal 1
no signal 0
Antena2
signal 1
no signal 0
Ant1
Prob
0
0
1
1
0
1
0
1
0.1
0.1
0.9
0.9
Ant2
Prob
Probability
0.1
0.9
0.1
0.9
0.01
0.09
0.09
0.81
0.01
0.18
xBER
0.005
1.8E-11
0.81
8.10E-11
0.01
9.9E-01
0.005
9.9E-11
2 ANT BER
5.00E-03
0 reception probability
1 rx probability
5.00E-03
3 antennas
Antena 1
signal 1
no signal 0
1
2
3
4
5
6
7
8
Antena2
signal 2
no signal 0
Antena3
signal 3
no signal 0
Ant1
Prob
Ant2
Prob
Ant2
Prob
0
0
0
1
0
1
1
1
0
0
1
0
1
0
1
1
0
1
0
0
1
1
0
1
0.10
0.10
0.10
0.90
0.10
0.90
0.90
0.90
0.10
0.10
0.90
0.10
0.90
0.10
0.90
0.90
0.10
0.90
0.10
0.10
0.90
0.90
0.10
0.90
1.E-08
1.E+00
BER
5.E-09
1.E-10
8 antennas
Bad reception probability
Good reception probability
1.0E-03
9.0E-03
9.0E-03
9.0E-03
8.1E-02
8.1E-02
8.1E-02
7.3E-01
1.0E-03
2.7E-02
xBER
5.0E-04
2.7E-12
2.4E-01
2.4E-11
7.3E-01
7.3E-11
3 ANT BER
5.00E-04
42
5.E-09
43. Diversity principles, example
• The resulted BER in average for a single receiver is
about 0.05, which is quite erroneous
• However, for eight receptors, the BER would
be 5x10-10 which represents a much better approach
to have truthful detection
• another important factor to improve accuracy is the
quality of the components used
43
45. Sensor Network Testbed
• Physical Layer - Sensing multiple receivers
XBee
XBee
XBee
XBee
PIC
PIC
PIC
PIC
XBee
XBee
XBee
XBee
PIC
PIC
PIC
PIC
Signal
BUS
Control
BUS
BUS
Master
Microcontroller
Spectrum sensing module
*PIC is a family of modified Harvard architecture
microcontrollers made by Microchip Technology
Multiple receivers and one master coordinator
45
Dr. Enrique Rodríguez de la Colina
47. Physical Layer - Sensing
other combinations
11111111
other combinations
11111111
00000000
00000000
other combinations
11111111
occurrence
occurrence
00000000
occurrence
occurrence
• - eight slaves coordination and tuning
00000000
other combinations
11111111
47
48. CR MAC
CR MAC
Architecture
Centralized
Distributed
Spectrum Sharing
Behavior
Cooperative
NonCooperative
Spectrum Sharing
Mode
Overlay
Underlay
Access Mode
Conetention
Free
Conetention
based
48
Jie Xiang, Yan Zhang, Tor Skeie, Medium Access Control Protocols in Cognitive Radio Networks,
WCMC2010; 10:31-49 Wiley InterScience
49. CR MAC
49
Jie Xiang, Yan Zhang, Tor Skeie, Medium Access Control Protocols in Cognitive Radio Networks,
WCMC2010; 10:31-49 Wiley InterScience
53. Media Access Control (MAC)
• MAC Proposal,
• includes a literature review and based on
• criteria design which consist of,
Avoidance of a Control Common Channel (CCC)
Cooperative Overlay and Underlay scheme
Applicable to centralized and distributed systems
• Development of a customized simulator
53
54. Media Access Control (MAC)
• Tests using a WiFi platform
Cognitive 1
Cognitive 2
54
55. Media Access Control (MAC)
• Algoritmo preliminar basado en intercambio de mensajes
55
57. Media Access Control (MAC)
For the MAC module,
Decision making module
Attributes assignment
Numeric validation
user-centric system
Simulation with CRUAMAC*
Enrique Rodriguez-Colina, Carlos Ramirez-Perez y C. Ernesto Carrillo A:, “Multiple attribute dynamic decision
making for cognitive radio networks”, IEEE Wireless and Optical Communications Networks (WOCN), June 2011.
57
58. Decision Making Module (DMM)
58
Enrique Rodriguez-Colina, Carlos Ramirez-Perez y C. Ernesto Carrillo A:, “Multiple attribute dynamic decision
making for cognitive radio networks”, IEEE Wireless and Optical Communications Networks (WOCN), June 2011.
59. Multiple attribute dynamic decision
making for CRN
• We model the Spectrum Decision making
functionality with multiple attributes
• We propose a novel use of the Analytic
Hierarchy Process (AHP)
• to optimally select available bands from a finite set of
options
• Our approach classifies from the best to the
worst bands based on the requirements from
two different classes of service,
• Real Time and Best Effort
• The selection of the best available bands is done with a low
execution latency.
Enrique Rodriguez-Colina, Carlos Ramirez-Perez y C. Ernesto Carrillo A:, “Multiple attribute dynamic decision
making for cognitive radio networks”, IEEE Wireless and Optical Communications Networks (WOCN), June 2011.
59
60. Analytic Hierarchy Process (AHP)
60
Enrique Rodriguez-Colina, Carlos Ramirez-Perez y C. Ernesto Carrillo A:, “Multiple attribute dynamic decision
making for cognitive radio networks”, IEEE Wireless and Optical Communications Networks (WOCN), June 2011.
61. Outcome
61
Enrique Rodriguez-Colina, Carlos Ramirez-Perez y C. Ernesto Carrillo A:, “Multiple attribute dynamic decision
making for cognitive radio networks”, IEEE Wireless and Optical Communications Networks (WOCN), June 2011.
62. Example
62
Enrique Rodriguez-Colina, Carlos Ramirez-Perez y C. Ernesto Carrillo A:, “Multiple attribute dynamic decision
making for cognitive radio networks”, IEEE Wireless and Optical Communications Networks (WOCN), June 2011.
63. The proposed AHP delay response
63
Enrique Rodriguez-Colina, Carlos Ramirez-Perez y C. Ernesto Carrillo A:, “Multiple attribute dynamic decision
making for cognitive radio networks”, IEEE Wireless and Optical Communications Networks (WOCN), June 2011.
64. The proposed AHP conclusions
64
Enrique Rodriguez-Colina, Carlos Ramirez-Perez y C. Ernesto Carrillo A:, “Multiple attribute dynamic decision
making for cognitive radio networks”, IEEE Wireless and Optical Communications Networks (WOCN), June 2011.
65. Statistical - decision making
Statistical model for decision making
Spectrum
Sensing
Criteria, e.g. BW,
SINR, occupancy
decision
maker
Probability error of the
decision making
process
Ranking
Correlation
Database of
Knowledge
Band Histograms
Processed
Samples
Snapshot-measure ranks
No. times
the best
1 2 3 4 5 …….. bands
Best
bands
No. times
2nd place
1 23…
To CR
bands
No. times
the worst
1 2 3 4 5 …….. bands
Worst
bands
V1
V2
Vi
1
15
3
2
7
8
3
15
7
2
5
1
4
15
1
3
2
1
time
Dr. Enrique Rodríguez de la Colina
65
66. Reactive and Proactive Approach
System performance reactive
System performance proactive
66
Enrique Rodriguez-Colina, Víctor-M. Ramos-R., Gerardo Laguna-Sanchez, Cross Layer Analysis for a Dynamic
Spectrum Allocation System, -A Cognitive Sensor Network Testbed Design-, IEEE Workshop on Engineering
Applications (WEA) 2012
67. Media Access Techniques
Diverse techniques to access the media and its
combinations,
Coding
[Eb/No]
space
time
frequency
frequency
coding
time
This improves the spectrum management but there
are various restrictions
67
70. Collaboration
To share the spectrum monitoring
Decision making and channel selection with collaborations
Communications ‘ coordination
70
Jiaqi Duan, Yong Li, Performance Analysis of Cooperative Spectrum Sensing in Different
Fading Channels, IEEE 2010
72. Upper Layers
UPPER
MAC
PHYSICAL
PROTOCOL STACK ( LAYERS )
• Cross-layer approach
Application
• Interfaces
• Services
Transport
• UDP
Routing
• Multi-hop
• TCP
• Point-to-Point
Protocol adaptation
Access Control
• Coordinate
• Sharing
Front-end
• Communication
• Sensing
Proactive
Decision
Module
Reactive
Spectrum
Analysis
Environment
Data
Control
Module
Information
72
73. Upper Layers
• Routing is an issue to solve mainly when the system is multihop
• The Transport Layer must be more robust to changes at the
same time the
73
74. Upper Layers – backup channels
The application layer consists in a human interface to allocate the communication
and to set the parameters desired by the user
e.g., the size of the file to transmit
Cognitive device for a sensor network where information data is sent using free
channels only
to avoid interference to primary users, so the scalability can be increased
74
76. Propuesta coexistencia de PU con CR
• Control channel proposal,
•
•
MAC with time division and frequency division
Today´s technology and heterogeneous
76
Nicolás Bolívar, J. L. Marzo, E. Rodriguez-Colina, ‘Distributed Control using Cognitive Pilot Channels in a
Centralized Cognitive Radio Network’. AICT 2010 – IEEE Computer Society Conference proceedings, May 9 - 15,
2010
77. CR device model for centralized system
• Central system design
77
Nicolás Bolívar, J. L. Marzo, E. Rodriguez-Colina, ‘Distributed Control using Cognitive Pilot Channels in a
Centralized Cognitive Radio Network’. AICT 2010 – IEEE Computer Society Conference proceedings, May 9 - 15,
2010
78. 78
Nicolás Bolívar, J. L. Marzo, E. Rodriguez-Colina, ‘Distributed Control using Cognitive Pilot Channels in a
Centralized Cognitive Radio Network’. AICT 2010 – IEEE Computer Society Conference proceedings, May 9 - 15,
2010
80. Embedded system which covers the
spectrum
Longitud de
onda
Frecuencias
http://mynasadata.larc.nasa.gov/images/EM_Spectrum3-new.jpg
A device to be able to cover the whole spectrum
is the challenge
80
81. Small device which covers the spectrum
Control node
Control
Monitoring
FPGA
interface
Hardware
simplification
81
Device with full capacity
83. Communication and channelization controls
WLAN
RF
Control node
FPGA
interface
RF
WLAN
Spectrum analyser
FPGA
interface
RF
FPGA
interface
Signal
power (W)
WLAN
0 1 0 1 1 1 0 10 0 1 0 1
Frequency (Hz)
WLAN
FPGA
interface
RF
Canales de comunicación
WLAN
WLAN
RF
FPGA
interface
FPGA
interface
RF
83
84. Integrated hardware and software
Signal
power (W)
Bussy channel = 1
0 1 0 1 1 1 0 10 0 1 0 1
Frequency (Hz)
Control node
Emulador de analizador
FPGA
interface
Control interface with the use of FPGA’s
- PC emulates the frequency spectrum (busy = 1 y free= 0)
- PC sends vector with information (0101110100101)
84
85. Other CR devices competences
Real time performance
Power
adaptation
Diverse bands inter-connection
inter-
Flexibility for protocol adaptation
Error
control
New applications adjustments
85
Mobility prediction
87. Software defined radio (SDR)
• Conventional Radio
IF signal
RF signal
Amplifier
Mixer
Filter
Amplifier
Mixer
Filter
Base band
signal
• Software defined radio
RF signal
IF signal
Amplifier
Mixer
Filter
Digital /
Analog
Converter
Digital signal
processing
Rx
Tx
87
•Cognitive Software Defined Radio: Applications of Cognitive SDR using the GNU Radio and the
USRP, David Scaperoth, 2005
•Joseph Mitola III, Software Radio Architecture, John Wiley & Sons, 2000
95. Project development
Sustainable development
High technology communications
economy
Planned, cost-benefit,
sustainable, feasible, ubiquitous
Adaptable, ecological,
environment
social
life -friendly
Technology
changes
through,
policies and
scientific design
Dynamic, Feasible , usable,
ubiquitous
95
Dr. Enrique Rodríguez de la Colina
96. Project development
Sustainable development
High technology communications
economy
Planned, cost-benefit,
sustainable, feasible, ubiquitous
Adaptable, ecological,
environment
social
life -friendly
Technology
changes
through,
policies and
scientific design
Dynamic, Feasible , usable,
ubiquitous
96
Dr. Enrique Rodríguez de la Colina
97. Cognitive Radio techniques
used
• Analyze the spectrum behavior
• Power consumption
• e.g.: Adaptive Power
• Algorithms to optimize communication
97
98. Capabilities of the CRN
Coding
Power
adaptation
frequency
Error
control
98
time
UAM
99. 99
D. S. Peter Steenkiste, Gary Minden, Dipankar Raychaudhuri "Future Directions in Cognitive
Radio Network Research. Executive Summary " in NSF Workshop Report 2009.
100. Conclusions
More research is required to develop practical cognitive radio devices
Multidisciplinary work is essential for the development of a wireless
cognitive technology
The ideas used by CR devices can help to create a sustainable
development in wireless communications
New MAC design is also required
Monitoring tools and testbeds are a good approach to the CRN
100
101. Future work
We plan to,
analyze other hardware platforms to improve the spectrum
sensing function
investigate other applications with cognitive radio devices
The development of systems that can operate with current
technology
The use of GNU Radio over SDR platforms
101
102. Future work
Android programming
Definition of new functionalities for lower layers
Wireless technology integration
Routing evaluation for multi-hop networks
Design and implementation of new testbeds
Wireless sensor network applications
102
103. Bibliography
1. J. Mitola III and G.Q Maguire, Jr., “Cognitive Radio: Making Software Radios More
Personal,” IEEE Personal Communications (Wireless Communications), vol.6, no. 4, pp. 1318, August 1999.
2. I. F. Akyildiz, W.Y. Lee, M.C. Buran, and S. Mohanty, “A survey on spectrum management in
cognitive radio networks,” IEEE Communications Magazine, vol. 46, no. 4, pp. 40-48, April
2008.
3. F. Wang, M. Krunz, and S. Cui, “Spectrum Sharing in Cognitive Radio Networks,” in IEEE
27th Conference on Computer Communications, INFOCOM 2008, pp. 36-40, April 2008.
4. H. Wang, H. Qin, and L. Zhu, “A Survey on MAC Protocols for Opportunistic Spectrum
Access in Cognitive Radio Networks,” in IEEE International Conference on Computer
Science and Software Engeneering 2008, pp. 214-218, December 2008.
5. Jie Xiang, Yan Zhang, Tor Skeie, Medium Access Control Protocols in Cognitive Radio
Networks, WCMC2010; 10:31-49 Wiley InterScience.
6. Andreas F. Molish, Larry J. Greenstein and Manson Shafi, Propagation Issues for Cognitive
Radio, IEEE proceedings, 2009
7. John Schiller, Mobile Communications, Addison Wesley, 2a Edición, 2003
8. Nicolás Bolívar, J. L. Marzo, E. Rodriguez-Colina, “Distributed Control using Cognitive Pilot
Channels in a Centralized Cognitive Radio Network”. AICT 2010 – IEEE Computer Society
Conference proceedings, May 9 - 15, 2010
9. Joseph Mitola III, Software Radio Architecture, John Wiley & Sons, 2000
10. Hongjian Sun, DI Laurenson JS Thompson, Cheng-Xiang Wang, A novel Centralized
Network for Sensing Spectrum in Cognitive Radio
11. Jiaqi Duan, Yong Li, Performance Analysis of Cooperative Spectrum Sensing in Different
Fading Channels, IEEE 2010
103
104. Thank you,
questions?
Gracias a la Universidad Distrital, Bogotá
Colombia por la invitación
Dr. Enrique Rodríguez de la Colina
erod@xanum.uam.mx
Universidad Autónoma Metropolitana Iztapalapa
104
