SpecNet

SpecNet : Spectrum Sensing Sans Frontieres

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 IndiaCommercial 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.

UnusedT.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 UserSpectrum 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

SpecNetUser

SpecNetServer

import xmlrpclib;apiServer = xmlrpclib.ServerProxy(“https://research.microsoft.com/specnet/api”);devices = apiServer.getDevice();

User CodeSpectrum 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

spanscan fRBWct )/1(1

RBWcN 2)/1(3 Nctscan or

Log(RBW)N

f

102 106

-80

-110

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.microsoft.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

Occ

upan

cy

1

0

r

[lat,lng]d

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

P

d

)log(100 dPPd

• SpecNet server chooses a resolution bandwidth such that noise floor is Pd - 5dB

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

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

S1

S2

S3

d1

d2

d3

d

Strategy III : A Hybrid Partitioning

• Find an optimal combination of area and frequency partitioning

kdtscan

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

)log(100 dPPd

Localizing Bangalore T.V TowerHow 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 50-150 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

We request your participation!Thank You!

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.