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SpecNet

SpecNet : Spectrum Sensing Sans Frontieres. SpecNet. Anand Padmanabha Iyer (MSRI) Krishna Kant Chintalapudi (MSRI) Vishnu Navda (MSRI) Ramachandran Ramjee (MSRI) Venkat Padmanabhan (MSRI) Chandra Murthy ( IISc ). A Case for Sub-GHz in Rural India.

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SpecNet

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  1. SpecNet : Spectrum Sensing Sans Frontieres SpecNet Anand PadmanabhaIyer (MSRI) Krishna Kant Chintalapudi (MSRI) Vishnu Navda (MSRI) RamachandranRamjee (MSRI) VenkatPadmanabhan (MSRI) Chandra Murthy (IISc)

  2. A Case for Sub-GHz in Rural India • Commercial Broadband Connectivity in Rural India is uneconomical • 70% of Indian population • 500,000 villages 1-2SqKm in area • 80% villages under 1000 people • Low Income, Low user density • Long Distance Wi-Fi (in 2.4 GHz) • High gain directional wireless links for back-haul • Needs a tower roughly per village • Does not scale economically • Sub-gigahertz license free spectrum • Excellent range • about 10Km at 300 MHz 30 dBm • A single tower can provide for several tens of village • Has the potential to enable economically viable connectivity

  3. Whitespaces in the Heart of Bangalore • Over 90% of the spectrum remains unused in the sub-gigahertz spectrum • Only 16/566 MHz of TV spectrum is used • Prior studies in U.S.A, Spain, France, Singapore, China etc. GSM FM CDMA TV

  4. Options for Spectrum Usage in India • Unused T.V. Bands in India • In U.S almost all allocated T.V. bands are in use at one or more locations • A large number of T.V. bands are not used anywhere in India! Unused T.V. Bands • Three Options to Reclaim Unused Spectrum • Auction away to Commercial Providers • no commercial interest in rural deployments • Create a License Free Band Similar to ISM • can potentially spur tremendous growth • government loses the opportunity to monetize the band • Opportunistic Usage of Unused Spectrum (e.g. FCC in U.S) • - perhaps best of both worlds

  5. Mapping Spectrum Usage • The first step is to understand the nature spectrum usage • India is a large country • Information is not as readily available as in developed countries • - e.g. no online T.V. tower location database 2500 Km • How can we construct and maintain spatio-temporal spectrum usage maps? • A collaborative measurement platform is the key! • A network of spectrum sensing devices. 2000 Km

  6. SpecNet Spectrum Analyzer Remote User SpecNet : A platform that enables development of collaborative spectrum measurement based applications using networked spectrum analyzers

  7. The Power of SpecNet • Enable remote measurements • Help cognitive researchers to access real data from across the world to validate their models Remote User Spectrum Analyzer • Construction and Maintenance of Real-Time White Space Spatio-Temporal Usage Maps • Can help future white space service providers to plan their infrastructure deployments • Can aid the operation of white space devices • Real-Time Distributed Applications that Utilizes Spectrum Measurements • Researchers can implement and test their ideas using real-time sensing data

  8. SpecNet Operation User Code • Users • Use SpecNet API to write applications • SpecNet API provides an easy to use abstraction layer implemented as XML-RPC for flexibility • Spectrum Analyzers • Volunteering spectrum analyzer (SA) owners register and connect to SpecNet • SA owners specify times of public usage • Connect to SpecNet server • SpecNet Server • Interprets the API commands to task individual spectrum analyzers • Schedules task intelligently to optimize resource utilization import xmlrpclib; apiServer = xmlrpclib.ServerProxy(“https://research.microsoft.com/specnet/api”); devices = apiServer.getDevice(); SpecNet Server XML RPC SpecNet User

  9. Fundamental Tradeoffs • Resolution Bandwidth • Ability to distinguish between two nearby parts of the spectrum • Time versus Resolution Bandwidth • Sort of like the Heisenberg’s uncertainty principle • The finer frequencies you wish to resolve, the longer it takes -80 Nf • Time versus Noise Floor • A lower resolution bandwidth implies lower noise • Can detect weaker signals • Also means it takes longer to detect weaker signals -110 102 106 Log(RBW) or

  10. A Simple First Example import xmlrpclib; apiServer = xmlrpclib.ServerProxy(“https://research.microsoft.com/specnet/api”); (Lat,Lng) devices = apiServer.getDevices([lat,lng,r]); r for d in devices: val = apiServer.getPowerSpectrum(‘NOW’,d,Fc,BW,Nf); BW • Behind the Scenes • For each spectrum analyzer SpecNet maps the required noise floor Nf to its resolution bandwidth. • It then issues commands to each spectrum analyzer to scan. • Collects the results and sends them back to the user Fc

  11. Example II: Occupancy Detection import xmlrpclib; apiServer = xmlrpclib.ServerProxy(“https://research.microsoft.com/specnet/api”); oc = apiServer.getOccupancy(NOW,[lat,lng,r], Fc,BW,P) • Must detect a transmitter with power P anywhere within the circle 1 Occupancy 0 d P [lat,lng] r d • SpecNet server chooses a resolution bandwidth such that noise floor is Pd - 5dB

  12. Scheduling Multiple Spectrum Analyzers Goal : To minimize scan time e.g. 300-600 Mhz • Strategy I : Partition the frequency space • S1 scans 300-400 MHz, S2 scans 400-500 MHz, S3 scans 500-600 MHz • Time taken reduces linearly i.e. by a factor of 3 d1 S1 • Strategy II : Partition the geographical space • All spectrum analyzers scan 300-600Mhz • Scan only a part of the geographical area • Scan time = max( k1d1, k2d2 , k3d3 ) • Scan time decreases super-linearly S2 d2 S3 d d3 • Strategy III : A Hybrid Partitioning • Find an optimal combination of area and frequency partitioning

  13. Example III : Estimating Transmitter’s Footprint Typically use a path loss model Log Distance Path Loss Model - Longley Rice Model • Locating T.V Transmitter Towers in India • There is no readily available database that provide this information in India like in the U.S • We tried to obtain this information using RTI • Incomplete information (100/700+) • Erroneous information • Provided to the SpecNet users as an API

  14. Localizing Bangalore T.V Tower • How can one localize the T.V. transmitter? • Basic Idea :Use a path loss model and find the location that fits the data the best • [loc,P] = estimateTransmitterParams (pos, power, model) 6 Km error in the RTI Data

  15. Predicting T.V. Signal Strength • 125 Locations in Bangalore • 5 – 8 dB variation due to fading at various locations • 60 Test Locations spread across Bangalore • Performance is within the variation limits

  16. A Real-Time Demo App Find the strongest FM Station! Scan from 50-150 Mhz at a high resolution Find the strongest point in the spectrum Scan ± 500Khz around the strongest point at a finer resolution bandwidth

  17. We request your participation! Thank You!

  18. Opportunistic Spectrum Usage in U.S • FCC Ruling (2008) : Permits opportunistic usage of T.V whitespaces in the sub-gig Hz in US • Will lead to tremendous innovation and development in wireless communication Putting things in Perspective • ISM Band Today • Tremendous innovation • WiFi, Bluetooth, Zigbee, WiBree, Cordless phones, etc. • ISM Band Before 1985 • Wasteland for emissions due to Industrial, Scientific and Medical equipment

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