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Ieee tutorial wea 2012_cognitive_radio_sensor_networks_test_bed Presentation Transcript

  • 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
  • 3. Introduction COGNITIVE RADIO NETWORKS 3
  • 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
  • 8. Context • Diverse ways of signal handling 8
  • 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
  • 19. CRN main functions 19
  • 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
  • 21. Background FUNDAMENTAL FUNCTIONS 21
  • 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
  • 29. Spectrum Sensing 29
  • 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
  • 38. Physical Layer - Sensing • Sensing – energy detection Channel 2 - 2.410 GHz Channel 1 - 2.405 GHz 300 300 250 150 100 200 Frequency occurrence 200 Frequency occurrence 250 150 100 50 50 0 0 55 60 65 70 75 80 46 50 54 58 62 66 70 74 78 82 Energy [-dBm] 85 Energy [-dBm] Channel 4 - 2.420 GHz 350 Channel 3 - 2.415 GHz 300 300 150 100 250 Frequency occurrence 200 Frequency occurrence 250 200 150 100 38 50 50 0 55 0 46 50 54 58 62 Energy [-dBm] 66 70 74 energy level [dBm] 78 82 60 65 70 75 Energy [-dBm] energy level [dBm] 80 85
  • 39. Physical Layer - Sensing 80 energy detection (dBm) Energy detection (XBee Pro) 70 0 2 60 4 50 channel C hannel 6 8 40 10 30 12 14 20 16 18 10 5 10 15 20 25 Scan scan 30 35 40 45 50 39 0
  • 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
  • 44. Description TESTBED 44
  • 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
  • 46. Physical Layer - Sensing occurrence occurrence • Sensing – two slaves coordination and tuning - 01 10 Detection options 00 11 01 10 Detection options 11 occurrence occurrence 00 00 01 10 11 Detection options 00 00 01 01 10 10 11 11 Detection options Detection options 46
  • 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
  • 50. Distributed vs. Centralized Centralized or infrastructure Ad-hoc Distributed 50
  • 51. More challenges Hidden terminal Far and near terminal John Schiller, Mobile Communications, Addison Wesley, 2a Ed., 2003 51
  • 52. MAC 52
  • 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
  • 56. MAC - DECISION MAKING 56
  • 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
  • 68. SHARING & COLLABORATION 68
  • 69. Distributed vs. Centralized Centralized or infrastructure Ad-hoc Distributed 69
  • 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
  • 71. UPPER LAYERS 71
  • 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
  • 75. PROPOSALS 75
  • 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
  • 79. 79
  • 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
  • 82. XBee XBee XBee XBee PIC PIC PIC PIC XBee XBee XBee XBee PIC PIC PIC Smart detection in integrated platform PIC Signal BUS Control BUS BUS Master Microcontroller Spectrum sensing module *PIC is a family of modified Harvard architecture microcontrollers made by Microchip Technology 82
  • 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
  • 86. SOFTWARE DEFINED RADIO 86
  • 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
  • 88. Software defined radio (SDR)
  • 89. UPPER MAC PHYSICAL PROTOCOL STACK ( 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 89
  • 90. 90
  • 91. Software defined radio (SDR)
  • 92. Software defined radio (SDR) Se plantea la creación de un sistema que pueda operar con la tecnología existente 92
  • 93. “Sustainable design initiative’’ Sustainable Systems adaptation for efficient use of the RF spectrum Introduction Energy reduction for future communications Sustainable development 93
  • 94. Saving energy? 94 -“trend to reduce energy consumption”-
  • 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