Prospect of TV White Space
Deployment in Africa
By
Nasir Faruk
Department of Telecommunication Science,
University of Ilor...
2
Outline
 Introduction
 Scarcity of radio spectrum
 Mechanisms to solve the scarcity problems
 What is TV white space...
3
Introduction
Cisco Visual Networking Index (VNI) Global Mobile
Data Traffic Forecast
 Monthly global mobile data traffi...
Scarcity of Radio Spectrum
 Radio spectrum is limited
30 Hz (ELF) to 300 GHz (EHF)
 Measurements conducted in so many
c...
Intro (cont…): Mechanisms to solve
the scarcity problems
5
o Market Mechanism: Spectrum
Auction/Trading
o Joint design of ...
6
Un-used portion of 54-862 MHz : TVWS f(Space, time )
1. Temporal: off periods of TV transmitter
2. Spatial
What is TV W...
Mobile broadband Internet subscriptions in 2012 as a
percentage of a country's population
Fig 4. Source: International Tel...
Fixed broadband Internet subscriptions in 2012 as a
percentage of a country's population
Fig 5. Source: International Tele...
Other problems peculiar to
Africa and the likes
 Over 60% of Africa populace resides in the rural
communities
 These com...
Some benefits of providing telecommunication
access using TVWS solution particularly in the
rural Africa
 It is expected ...
Techno-Economic Benefits of
using White Spaces
1108/04/14
Secondary Spectrum Markets and Revenue Generation
Rural and ur...
Challenges
 Regulatory difficulties: The critical
issue is the development of spectrum
access rule that would allow the
s...
Why Unlicensed TVWS and Geo-
location Database for Africa ?
 World Summit on Information Society (WSIS):- By 
2015:
 All...
Deployment Scenarios in TVWS
14
08/04/14
Fig 6. Spatial reuse of spectrum between PU and SU  Fig 7. Coverage of rural area...
 UMTS and LTE extension over TVWS
 WiFi-2 in TVWS
 WiMAX in TVWS
 Public safety and disaster relief networks
 PMSE
15...
The TVWS drivers in Nigeria
 Government
 Academia
 WaveTek Nig Ltd : The main company
responsible for the deployment an...
Who are WaveTek?
 WaveTek is one of 16 founding members of the
“Dynamic Spectrum Alliance”, which includes
Microsoft and ...
Efforts so far!
 Research efforts to unveil this resource
 Deployment prototype in South Africa and
Kenya
 Preliminary ...
Other efforts
 We have to assess the efficacy of 12 empirical
models in predicting TV signal
 We have developed a model ...
Spatial Interpolation of Available TV
channels in Nigeria
20
Fig 8.
Acknowledgements
• WaveTek Nig Ltd for travel grants and
research supports
• Extensia ltd for giving us the opportunity to...
Authors Bio-data
Is a lecturer in the department of Telecommunication Science,
University of Ilorin, Ilorin, Nigeria. He r...
23
REFERENCES FOR FURTHER READING
1. -------- “Cisco Visual Networking Index: Global Mobile Data Traffic Forecast Update, ...
2408/04/14
16. R. Dahama, K. W. Sowerby, and G.B. Rowe, “Protection regions for dynamic spectrum sharing,” , IET Electron....
2508/04/14
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Nasir faruk tv white spaces presentation iad2014

Presentation created for IAD 2014. TV White Spaces - Nasir Faruk - University of Illorin Nigeria
Published on: Mar 3, 2016
Source: www.slideshare.net


Transcripts - Nasir faruk tv white spaces presentation iad2014

  • 1. Prospect of TV White Space Deployment in Africa By Nasir Faruk Department of Telecommunication Science, University of Ilorin, Nigeria Head of TV white space research, WaveTek Nig LTD Seminar presentation at Innovation Africa Digital Summit, Kairaba, The Gambia, 25th -27th March 2014.
  • 2. 2 Outline  Introduction  Scarcity of radio spectrum  Mechanisms to solve the scarcity problems  What is TV white space (TVWS) ?  Challenges  Problems peculiar to Africa and the likes  Why Unlicensed TVWS and Geo-location Database for Nigeria ?  Benefits of Using TVWS  Our Research Efforts so far !  Research efforts  Bio-Data of Author  References
  • 3. 3 Introduction Cisco Visual Networking Index (VNI) Global Mobile Data Traffic Forecast  Monthly global mobile data traffic will surpass 15 exabytes by 2018. The number of mobile-connected devices will exceed the world’s population by 2014. The average mobile connection speed will surpass 2 Mbps by 2016. Due to increased usage on smartphones, smartphones will reach 66 percent of mobile data traffic by 2018. Monthly mobile tablet traffic will surpass 2.5 exabyte per month by 2018. Tablets will exceed 15 percent of global mobile data traffic by 2016. 4G traffic will be more than half of the total mobile traffic by 2018. There will be more traffic offloaded from cellular networks (on to Wi-Fi) than remain on cellular networks by 2018.
  • 4. Scarcity of Radio Spectrum  Radio spectrum is limited 30 Hz (ELF) to 300 GHz (EHF)  Measurements conducted in so many countries show the spectrum is under utilized  So the questions are: What is responsible for this scarcity?  How do we meet with the demand for wireless data service? 408/04/14
  • 5. Intro (cont…): Mechanisms to solve the scarcity problems 5 o Market Mechanism: Spectrum Auction/Trading o Joint design of primary and secondary waveforms: Not good for legacy primaries o Opportunistic spectrum use by SUs: Temporal or spatial reuse WRAN: Wireless regional Area Network SCC 41: Standardization Coordinating Committee 41
  • 6. 6 Un-used portion of 54-862 MHz : TVWS f(Space, time ) 1. Temporal: off periods of TV transmitter 2. Spatial What is TV White space (TVWS) Fig 1 : Temporal White space Fig 2. Spatial TV white space Grade B contour Fig 3 TV white space [2] x x x x x x xx x x x x x x x x x White spaces for CH 10
  • 7. Mobile broadband Internet subscriptions in 2012 as a percentage of a country's population Fig 4. Source: International Telecommunications Union AFRICA IS STILL LAGGING BEHIND
  • 8. Fixed broadband Internet subscriptions in 2012 as a percentage of a country's population Fig 5. Source: International Telecommunications Union.
  • 9. Other problems peculiar to Africa and the likes  Over 60% of Africa populace resides in the rural communities  These communities are characterized by poor infrastructure, low income, adversely scattered buildings, low literacy level and etc  Recurrent cost for bandwidth very high in African region  Broadband penetration: USA >75%, Nigeria <10 %  Digital dividend: Global and National: The gap is widening on daily basis in developing countries 908/04/14
  • 10. Some benefits of providing telecommunication access using TVWS solution particularly in the rural Africa  It is expected to contribute to many social and economic development  Local businesses will experience lower communication costs  Improved access to information about markets and commodity prices  Potential for growth in entrepreneurship and tourism  Establishment of new services such as Internet cafes will be maximised  Rural communities will gain easier access to information on health, agriculture, security, distance education services, disaster warning, access to job information, and closer contact with distant family members. 1008/04/14
  • 11. Techno-Economic Benefits of using White Spaces 1108/04/14 Secondary Spectrum Markets and Revenue Generation Rural and urban Broadband Deployment  The highly favorable propagation characteristics of the TV broadcast spectrum (as compared to unlicensed 2.4 or 5 GHz  bands) allow for wireless broadband deployment with greater range of operation  Public Safety Communications  Public agencies can have access better spectrum in the TV band; this would improve the capacity and quality of their  networks, as well as facilitate their expanded use for e-government and consumer services.  Education and Enterprise Video Conferencing  The TV white spaces could be used to give local high schools and middle-schools the same multimedia capabilities  available to major university campuses: Personal Consumer Applications  White space could be used to provide new services and applications to consumers by taking advantage of the improved  signal reliability, capacity, and range of the TV broadcast spectrum.  Mesh and Ad-Hoc Networks  The TV white spaces could be used to enhance mesh networking. Self-configuring, ad-hoc mesh wireless networks avoid  disruption or failure by re-routing around node failures or congestion areas, thereby enabling more robust and reliable  communications.    Security Applications The favorable propagation and bandwidth characteristics of the TV broadcast spectrum could enable enhanced video security  applications for commercial, residential, and government purposes.  
  • 12. Challenges  Regulatory difficulties: The critical issue is the development of spectrum access rule that would allow the spectrum efficiently utilized without causing interference to primary users (TV broadcast system) 12
  • 13. Why Unlicensed TVWS and Geo- location Database for Africa ?  World Summit on Information Society (WSIS):- By  2015:  All persons, schools, health centres, governments, institutions and  businesses should be connected in a global digital network  National ICT Target Indices: By 2015,   The number of internet users should 70 million  Mobile penetration 80%  Internet penetration 34% and  Broadband penetration 12% . 1308/04/14
  • 14. Deployment Scenarios in TVWS 14 08/04/14 Fig 6. Spatial reuse of spectrum between PU and SU  Fig 7. Coverage of rural area using CR technologies   CH 21 CH 41 CH 61 CH 21 CH 21 CH 41 CH 41 CH 41
  • 15.  UMTS and LTE extension over TVWS  WiFi-2 in TVWS  WiMAX in TVWS  Public safety and disaster relief networks  PMSE 15 Potential Application Scenarios UMTS: Universal Mobile Telecommunication System LTE: Long Term Evolution WiFi: Wireless Fidelity WiMAX: Worldwide Interoperability Microwave Access PMSE: Programmable Making and Special Events
  • 16. The TVWS drivers in Nigeria  Government  Academia  WaveTek Nig Ltd : The main company responsible for the deployment and research in TVWS 1608/04/14
  • 17. Who are WaveTek?  WaveTek is one of 16 founding members of the “Dynamic Spectrum Alliance”, which includes Microsoft and Google.  The Dynamic Spectrum Alliance will promote regulatory policies that will pave the way for innovative new wireless technologies that address growing wireless data and digital divide challenges.  WaveTek Nigeria, partners; BridgeWave Communications and Carlson Wireless 1708/04/14
  • 18. Efforts so far!  Research efforts to unveil this resource  Deployment prototype in South Africa and Kenya  Preliminary surveys conducted in University of Ilorin, Ilorin, and Kano University of Science and Technology, Wudil, Kano state, Nigeria by WaveTek for the deployment of TVWS solution 1808/04/14
  • 19. Other efforts  We have to assess the efficacy of 12 empirical models in predicting TV signal  We have developed a model that gives better prediction of TV signal in our region  We have also developed TV spectrum sharing model that allows simultaneous transmission of primary and secondary users  We have also developed a prototype of spatial interpolation of available TV channels in Nigeria 1908/04/14
  • 20. Spatial Interpolation of Available TV channels in Nigeria 20 Fig 8.
  • 21. Acknowledgements • WaveTek Nig Ltd for travel grants and research supports • Extensia ltd for giving us the opportunity to disseminates this information • University of Ilorin for research grants and purchased of equipments used in this study • Government of Gambia 21
  • 22. Authors Bio-data Is a lecturer in the department of Telecommunication Science, University of Ilorin, Ilorin, Nigeria. He received his B.sc (Hons) in Physics with first class honours from KUST Wudil, Kano State, Nigeria and M.sc in Mobile and High Speed Telecommunication Networks with distinction from Oxford Brookes University, Oxford, UK. He is rounding his PhD research in Electrical and Electronics Engineering at University of Ilorin, Ilorin, Nigeria. Where he researched on development of Model for maximizing spatial TV white space. He has over 19 published journal and conference papers. His current research interest includes design, analysis and optimization of Wireless communication networks, Wireless mesh and sensor networks, spectrum management, channel modeling, disaster and public safety networks and multiservice networks. Mobile: +234 (8)0324 281 41, 805 454 9807 Email: faruk.n@unilorin.edu.ng, nasirfaruk@gmail.com, nasirfaruk@ieee.org 22
  • 23. 23 REFERENCES FOR FURTHER READING 1. -------- “Cisco Visual Networking Index: Global Mobile Data Traffic Forecast Update, 2013–2018” Accessed on: http://www.cisco.com/c/en/us/solutions/collateral/service-provider/visual-networking-index-vni/white_paper_c11-520862.html visited 20/02/2014 2. K. Arshad, R. MacKenzie, U. Celentano, A. Drozdy, S. Leveil, G. Mange, J. Rico, A. Medela and C. Rosik, “Resource Management for QoS Support in Cognitive Radio Networks” IEEE Communications Magazine, pp 119, March 2014. 3. Achtzehn, A., Riihij¨arvi,J., Martinez, G., Petrova, M., and Mahonen,P., “Improving coverage prediction for primary multi- transmitter networks operating in the TV white spaces,” 9th Annual IEEE Communication Society conference on sensor, mesh and Adhoc communication and networks (SECON), pp. 547-555, 2012. 4. Kim, H., Sunahara,H., and Kato, A.,”Study on environmental improvement for DTV White space utilization with narrow band system”, International journal of Network and Communications,Vol 2. No. 4, pp 38-46, 2012. 5. S. Deb, V. Srinivasan, R. Maheshawi., ”Dynamic spectrum access in DTV white space: Design rules, architectural and algorithms” MobiCom ’09, September 20-25, 2009. 6. Kang, K.M., Park. J.C., Chio, S.I., and Jeong, B.J.,” Deployment and coverage of cognitive radio networks in TV white space” IEEE Communications magazine, Vol. 50, No.12, pp 88-94, December 2012. 7. T. Oyama, T. Shimomura, and H. Seki, “TV White Space Availability in Japan Estimated Using D/U-based and I/N-based Protection Rules” in Proc. IEEE Globecome 2012 Cognitive radio and networks symposium pp 1320-1325, 2012. 8. M. Vu, N. Devroye, and V. Tarokh. “On the primary exclusive regions in cognitive networks. IEEE Transactions on Wireless Communications, vol. 8, no. 7, pp. 3380-3385, Jul. 2009. 9. J. Van de Beek, J. Riihijarvi, A. Achtzehn, and P. Mahonen, “TV White Space in Europe, ”Mobile Computing, IEEE Transactions on communication, vol. 11, no. 2, pp. 178–188, Feb. 2012. 10. R. Dahama, K. W. Sowerby, and G.B. Rowe, “Protection regions for dynamic spectrum sharing,” , IET Electron. Letters, vol. 46, no. 29, pp. 1407–1408, Sep. 2010. 11. R. Dahama , K.W. Sowerby, and G.B Rowe, “Estimating protection distances in spectrum sharing systems”, IEEE Transactions on signal processing, vol. 61, no. 17, September 1, 2013. 12. C. Park, S.I.Chio, and B.J. Jeong ,”Deployment and coverage of cognitive radio networks in TV white space” IEEE Communications magazine, Vol. 50, No.12, pp 88-94, December 2012. 13. N. Faruk, A.A. Ayeni, A.A, and Y.A. Adediran, ”DTV coverage and protection contour estimation for spatial white space” in Proc IEEE NIGERCON conference, pp 96-99, July, 2013. 14. T. Oyama, T. Shimomura, and H. Seki, “TV White Space Availability in Japan Estimated Using D/U-based and I/N-based Protection Rules” in Proc. IEEE Globecome 2012 Cognitive radio and networks symposium pp 1320-1325, 2012. 15. M. Vu, N. Devroye, and V. Tarokh. “On the primary exclusive regions in cognitive networks. IEEE Transactions on Wireless Communications, vol. 8, no. 7, pp. 3380-3385, Jul. 2009.
  • 24. 2408/04/14 16. R. Dahama, K. W. Sowerby, and G.B. Rowe, “Protection regions for dynamic spectrum sharing,” , IET Electron. Letters, vol. 46, no. 29, pp. 1407– 1408, Sep. 2010. 17. R. Dahama , K.W. Sowerby, and G.B Rowe, “Estimating protection distances in spectrum sharing systems”, IEEE Transactions on signal processing, vol. 61, no. 17, September 1, 2013. 18. R. Menon, R. M. Buehrer, and J.H.Reed, “Impact of exclusion region and spreading in spectrum -sharing ad hoc networks,” in Proc. 1st Int. Workshop Technol. Policy Access. Boston, MA, USA , Aug. 2006. 19. A. Hasan and J.H.Reed,“The guard zone in wireless adho c networks,” IEEE Transaction on wireless communication., v ol. 6, no. 3, pp. 8 97–906, Mar. 2007. 20. R. Menon, R. M. Buehrer, and J. H. Reed, “On the impact of dynamic spectrum sharing techniques on legacy radio systems,” IEEE Transaction on wireless communication, vol. 7, n o. 11 , pp. 4198–4207, Nov. 2 008. 21. Kim, H, H. Sunahara, and A. Kato,”Study on environmental improvement for DTV White space utilization with narrow band system”, International journal of Network and Communications, Vol 2. No. 4, pp 38-46, 2012. 22. K. Harrison, S. Mishra, and A. Sahai, “How Much White-Space Capacity Is There?” in Proceedings of IEEE DySPAN 2010, , pp. 1–10, April 2010. 23. J. Van de Beek, J. Riihijarvi, A. Achtzehn, and P. Mahonen, “TV White Space in Europe, ”Mobile Computing, IEEE Transactions on communication, vol. 11, no. 2, pp. 178–188, Feb. 2012. 24. P. Kumarz, N. Rakhejay, A.Sarswaty, H. Varshneyy, P. Bhatiay, S. R. Goliy, V. J. Ribeiroy, M. Sharmax, “White Space Detection and Spectrum Characterization in Urban and Rural India,” in Proc. of IEEE 14th Intl. Symp. and Workshops on a World of Wireless, Mobile and Multimedia Networks, pp. 1-6, June, 2013. 25. F. Hessar and S.Roy” Capacity Considerations for Secondary Networks in TV White Space”, arXiv:1304.1785v1 [cs.NI] 28 Mar 2013, http://arxiv.org/abs/1304.1785 [Accessed on 02/01/2014]. 26. G. Naik, S. Singhal, A. Kumar, and A. Karandikar, “Quantitative Assessment of TV White Space in India”, arXiv:1310.8540v1 [cs.IT] 31 Oct, 2013 available on http://www.diva-portal.org/smash/get/diva2:680724/FULLTEXT01.pdf [Accessed on 02/01/2014]. 27. R. A O’Connor, “Understanding Television’s Grade A and Grade B Service Contours” IEEE Transactions on Broadcasting, vol. 47, NO. 3,pp 309- 314, September 2001. 28. TV Service Contour Data Points http://transition.fcc.gov/Bureaus/MB/Databases/tv_service_contour_data/readme.html visited 12/06/2013. 29. D. Gurney, G. Buchwald, L. Ecklund, S. Kuffner, and J. Grosspietsch ” Geo-location Database Techniques for Incumbent Protection in the TV White Space “, 3rd IEEE Symposium on New Frontiers in Dynamic Spectrum Access Networks, DySPAN, pp 1-9, Oct, 2008, Chicago, Illinois, USA. 30. FCC, “Second report and order and memorandum opinion and order," ET Docket No. 08-260, Nov. 2008. 31. K. Ruttik , K. Koufos , and R. Jäntti,” Spectrum Reuse at the Border of a Primary User Cell” IEEE Transact ions on Communications, vol. 57, no. 12, Dec 2009. 32. K.A. Anang, P.B. Rapajic, R. Wu, L. Bello, and T.I. Eneh, “Cellular system information capacity change at higher frequencies due to propagation loss and system parameters”, Progress In Electromagnetics Research B, vol. 44, 191-221, 2012. 33. C. Phillips, D. Sicker, and D. Grunwald, “Bounding the practical error of path loss models” International Journal of Antennas and Propagation Vol. 2012 (2012), pp 1-21, doi:10.1155/2012/754158 34. ---“Federal Register: Rules and Regulations” vol 74, No. 30, pp 7317, Feb 17, 2009. Available on : http://alaskafisheries.noaa.gov/frules/74fr7359.pdf
  • 25. 2508/04/14

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