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
Whitespaces in the Heart of Bangalore FM TV GSM CDMA 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.
Unused T.V. Bands Options for Spectrum Usage in India 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 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!
Mapping Spectrum Usage How can we construct and maintain spatio- temporal spectrum usage maps? A collaborative measurement platform is the key! A network of spectrum sensing devices. 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 2000 Km
SpecNet SpecNet : A platform that enables development of collaborative spectrum measurement based applications using networked spectrum analyzers Remote User Spectrum Analyzer
The Power of SpecNet 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 Enable remote measurements Help cognitive researchers to access real data from across the world to validate their models Remote User Spectrum Analyzer Real-Time Distributed Applications that Utilizes Spectrum Measurements Researchers can implement and test their ideas using real-time sensing data
SpecNet Operation XML RPC SpecNet User SpecNet User SpecNet Server SpecNet Server import xmlrpclib; apiServer = xmlrpclib.ServerProxy(“ht tps://research.microsoft. com/specnet/api”); devices = apiServer.getDevice(); import xmlrpclib; apiServer = xmlrpclib.ServerProxy(“ht tps://research.microsoft. com/specnet/api”); devices = apiServer.getDevice(); User Code Spectrum Analyzers Volunteering spectrum analyzer (SA) owners register and connect to SpecNet SA owners specify times of public usage Connect to SpecNet server Users Use SpecNet API to write applications SpecNet API provides an easy to use abstraction layer implemented as XML- RPC for flexibility SpecNet Server Interprets the API commands to task individual spectrum analyzers Schedules task intelligently to optimize resource utilization
Fundamental Tradeoffs Time versus Resolution Bandwidth Sort of like the Heisenberg’s uncertainty principle The finer frequencies you wish to resolve, the longer it takes Time versus Noise Floor A lower resolution bandwidth implies lower noise Can detect weaker signals Also means it takes longer to detect weaker signals Resolution Bandwidth Ability to distinguish between two nearby parts of the spectrum or Log(RBW) Nf
A Simple First Example (Lat,Lng) r Fc 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 import xmlrpclib; apiServer = xmlrpclib.ServerProxy(“https://research.micr osoft.com/specnet/api”); for d in devices: val = apiServer.getPowerSpectrum(‘NOW’,d,Fc,BW,Nf); devices = apiServer.getDevices([lat,lng,r]);
Example II: Occupancy Detection Occupancy 1 0 r [lat,lng] d import xmlrpclib; apiServer = xmlrpclib.ServerProxy(“https://research.microsoft.co m/specnet/api”); oc = apiServer.getOccupancy(NOW,[lat,lng,r], Fc,BW,P) Must detect a transmitter with power P anywhere within the circle P d SpecNet server chooses a resolution bandwidth such that noise floor is P d - 5dB
Scheduling Multiple Spectrum Analyzers Goal : To minimize scan time e.g Mhz Strategy I : Partition the frequency space S1 scans MHz, S2 scans MHz, S3 scans MHz Time taken reduces linearly i.e. by a factor of 3 Strategy II : Partition the geographical space All spectrum analyzers scan Mhz Scan only a part of the geographical area Scan time = max( k 1 d 1 , k 2 d 2 , k 3 d 3 ) Scan time decreases super-linearly S1 S2 S3 d1d1 d2d2 d3d3 d Strategy III : A Hybrid Partitioning Find an optimal combination of area and frequency partitioning
Example III : Estimating Transmitter’s Footprint 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 Typically use a path loss model Log Distance Path Loss Model - Longley Rice Model
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
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
A Real-Time Demo App Find the strongest FM Station! 1.Scan from Mhz at a high resolution 2.Find the strongest point in the spectrum 3.Scan ± 500Khz around the strongest point at a finer resolution bandwidth
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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 Before 1985 Wasteland for emissions due to Industrial, Scientific and Medical equipment ISM Band Today Tremendous innovation WiFi, Bluetooth, Zigbee, WiBree, Cordless phones, etc.