Presentation on theme: "Exploring Indoor White Spaces in Metropolises"— Presentation transcript:
1Exploring Indoor White Spaces in Metropolises Xuhang Ying, Jincheng Zhang, Lichao YanGuanglin Zhang, Minghua ChenRanveer Chandra
2Skyrocketing Wireless Data Demand Source: Cisco VNI Global Mobile Data Traffic Forecast,Our story starts with an alarming observation that the worldwide wireless data demand grows exponentially – according to CISCO, the demand triples almost every 2 years.CAGR: Compound Annual Growth RateHong Kong 2.5G-4G data demand: 23Gbps (double every year)
3A Vision: Improve Spectrum Utilization to Satisfy the Growing Demand 15%Spectrum OccupancyHow can we support the increasing demand? Well, it is observed in practice that although most spectrum are licensed, they are underutilized -– in a case study in Chicago, the average utilization is less than 15%.Thus one way to satisfy the growing demand is to improve the spectrum utilization, by allowing unlicensed users to access the license band when the band is not in use.Most spectrum are licensed but underutilized
4A Trend: Explore TV White Spaces “White Spaces” are unoccupied TV channelsFCC allows unlicensed devices to operate in white spaces (2008, 2010)TV “White Space”dbmFrequency-60-100“White spaces”470 MHz800 MHzPrimarily UHF ~ MHz (channel 21-62)A trend in DSA is to explore the unoccupied TV channels for wireless communications. These unoccupied TV channels are called white spaces.This trend is rather promising, esp since FCC passed the historic rulings and opened the door for unlicensed devices to operate in TV white spaces. (From the award ceremony this morning, we know victor is the person behind these moves).
5TV White Space Networking Scenario ISM (Wi-Fi)MHz54-9047070024002500518053007000MHzSignal StrengthVacant Spectrumup to 3x of gSignal StrengthConceptually, using TV white spaces for communication is similar to using the wifi band for communications. But the scenario also involves new challenges to be addressed. For example, how to identify the unused TV channels? How does two devices agree on which channels to communicate?FrequencyFrequency
6Prior Works and Our Observation MeasurementIdentificationMedium AccessNetwork DesignOutdoorChicago [1, 2], Singapore , Guangzhou ,UK , Europe , etc.Cabric , Kim [8, 9],Murty , etc.Yuan Borth Bahl , etc.Murty ,Borth ,Bahl ,Feng , etc.Indoor?802.11afThe trend is promising, and the scenario incurs new challenges. Of course, there are existing works and many of them are really nice. These works focus on various aspects of white space networking.…But outdoor is only one part of the story. We believe that indoor is another important part of the story, … and the indoor white space story is widely open for exploration.More than 70% of data demand comes from indoorsMost people are indoors 80% of the time
7WISER – White-space Indoor Spectrum EnhanceR Our ContributionsMeasurementIdentificationMedium AccessNetwork DesignOutdoorChicago[1, 2], etc.Cabric, Murty, etc.Yuan,Bahl, etc.Murty,IndoorThis work802.11afUpcomingFirst large scale measurement in metropolises50% and 70% of the TV spectrum are white spaces in outdoors and indoorsWISER design and proto-typingData-driven designWISER prototype identifies 30%~50% more indoor white spaces compared with alternative approachesIn this work, we take the first step in filling in the blanks. In particular,WISER – White-space Indoor Spectrum EnhanceR
8How much more white spaces are indoor? What are their characteristics?
9White Space Availability in Hong Kong A Large-scale measurement study in Hong KongOutdoor white space ratio: 50%Indoor white space ratio: 70%Urban area : occupancy rate from 57 to 69%Suburban area: occupancy rate from 43 to 52%Rural area: occupancy rate below 40%FCC: DTV Threshold = signal strength of -114 dBm averaged over a 6 MHz bandwidth, andATV Threshold = signal strength of -114 dBm averaged over a 100 kHz bandwidth.However, we are now using:DTV Threshold = signal strength of dBm summed over a 8 MHz bandwidth, andATV Threshold = signal strength of dBm summed over a 100 kHz bandwidthSince we are using 2048 bins for 8 MHz and 25 bins for 100 kHz, if we converted summed signal strength to averaged signal strength (i.e. dividing the summed signal strength by the total number of bins), thresholds are:DTV Threshold = signal strength of dBm averaged over a 8 MHz bandwidth, andATV Threshold = signal strength of dBm averaged over a 100 kHz bandwidth.Principle TV StationFill-in TV StationMeasurement Location31 measurement locationsHardware : USRP + Antenna + Laptop
10Indoor White Space Measurement Experiment Scenario:7th floor of a 10-floor office building65 measurement locations (cover all rooms and corridors)MeasurementAcross four monthsOne time profiling every dayRecord the signal strengthsfor all channels at all locations
11Indoor White Space Characteristics Indoor white spaces show spatial variation – single location sensing is not enoughIndoor white spaces are long-term unstable – one time profiling is not enough
12Indoor White Space Correlation We will use this characteristic later to design our system.TV signal strengths show strong correlation across channels and locations
14Design Space and Solution Comparison ApproachFalse Alarm RateWhite Space Loss RateTotal CostGeo-databaseLowHighOutdoor-Sensing-OnlyOne-Time-Profiling-OnlySensor-All-Over-The-PlaceWISER (This work)Design space of indoor white space identification systemWe want a low cost, low FA rate, low WS Loss rate approach.The indoor white space characteristics give us some implications.MC: Need cosmetic improvement.Intuition: Exploiting indoor white space correlation to save sensor cost!
15Indoor Positioning System WISER ArchitectureIndoor Positioning SystemServerOutdoor SensorFunction of the three modulesProfiled LocationIndoor Sensor
16Key Challenge: Indoor Sensor Placement Get the signal strengthsOne-time spectrum profilingChannel-Location clusteringIndoor sensor placementGiven k sensors to be placed, where are the best locations to place them?Compute Channel- Location clustersThe number of indoor sensors is important. There is a tradeoff between the number of indoor sensors and the system performance.The locations of indoor sensors are also very important.3. What we answer: Given the number of sensors, how to obtain the best locations for these sensors?The procedures are as follows:First, OTPSecond, Channel-Location ClusteringThird, Place indoor sensors according to the clustering resultsIn the following, we introduce the detailed steps to do these procedures.Place one sensor per cluster
17Channel-Location Clustering Simple Case:One channel, 𝑀 locationsWhat we want: 𝑘 channel-location clustersCompute the proximity matrixMerge two “closest” clustersUntil k clustersProximity matrix: euclidean distanceMerge method: Ward’s minimum variance
18Channel-Location Clustering General Case:𝑁 channels, 𝑀 locations𝑘 channel clusters, 𝑘 𝑖 channel-location clusters for channel cluster 𝑖Compute the proximity matrixMerge two “closest” channel clustersChannel 3,4Channel 1,2Proximity matrix: euclidean distanceMerge method: Ward’s minimum varianceGeneral case:First, we do channel clustering to get channel clusters.Then for each channel cluster, we repeat the procedure for the simple case.Repeat procedure for simple case
20WISER Experimentation Implement a WISER prototype on the 7th floor of a campus building20 indoor sensors and 1 outdoor sensor11 experiments across 4 monthsCompare WISER, Outdoor Sensing (OS-only), and One-Time-Profiling (OTP-Only)WISER identifies 30%-50% more indoor white space as compared to baseline approaches.
21How Many Indoor Sensors is Enough? Balance between system performance and the total sensor costWhy we study this problem? Because it is important to balance between WISER performance and the total sensor cost. To study the relationship between performance and cost, it is necessary to conduct this evaluation.
22WISER – White-space Indoor Spectrum EnhanceR ConclusionsMeasurementIdentificationMedium AccessNetwork DesignOutdoorChicago[1, 2], etc.Cabric, Murty, etc.Yuan,Bahl, etc.Murty,IndoorThis work802.11afUpcomingFirst large scale measurement in metropolises50% and 70% of the TV spectrum are white spaces in outdoors and indoorsWISER design and proto-typingData-driven designWISER prototype identifies 30%~50% more indoor white spaces compared with alternative approachesWISER – White-space Indoor Spectrum EnhanceR
23Future Works More measurements at different buildings Extending the single-floor design to multi-floor designBuilding indoor white space network to utilize the white spacesExtend the solution/idea to other spectrum bandsLimited understanding on how TV signal propagates into indoor environments
24References M. McHenry et al., “Chicago Spectrum Occupancy Measurements & Analysis and A Long-term Studies Proposal”, ACM TAPAS, 2006. T. Taher et al., “Long-term Spectral Occupancy Findings in Chicago”, IEEE DySPAN, 2011. M. Islam et al., “Spectrum Survey in Singapore: Occupancy Measurements and Analyses”, IEEE CrownCom, 2008. D. Chen et al., “Mining Spectrum Usage Data: A Large-scale Spectrum Measurement Study”, ACM MobiCom, 2009. M. Nekovee et al., “Quantifying the Availability of TV White Spaces for Cognitive Radio Operation in the UK”, IEEE ICC joint workshop on cognitive wireless networks and systems, 2009. V. Jaap et al., “UHF White Space in Europe: A Quantitative Study into the Potential of the MHz band”, IEEE DySPAN, 2011. D. Cabric et al., “Experimental Study of Spectrum Sensing Based on Energy Detection and Network Cooperation”, ACM TAPAS, 2006. H. Kim et al., “Fast Discovery of Spectrum Opportunities in Cognitive Radio Networks”, IEEE DySPAN, 2008. H. Kim et al., “In-band Spectrum Sensing in Cognitive Radio Networks: Energy Detection or Feature Dection?”, ACM MobiCom, 2008. R. Murty et al., “Senseless: A Database-Driven White Space Network”, IEEE Transactions on Mobile Computing, 2012. Y. Yuan et al., “KNOWS: Kognitiv Networking Over White Spaces”, IEEE DySPAN, 2007. R. Borth et al., “Considerations for Successful Cognitive Radio Systems in US TV White Space”, IEEE DySPAN, 2008. P. Bahl et al., “White Space Networking with Wi-Fi Like Connectivity”, ACM Sigcomm, 2009. X. Feng et al., “Database-Assisted Multi-AP Network on TV White Spaces: Architecture, Spectrum Allocation and AP Discovery”, IEEE DySPAN, 2011. V. Chandrasekhar et al., “Femtocell networks: a survey”, IEEE Communications Magazine, 2008. N. Klepeis et al., “The national human activity pattern survey”, Journal of Exposure Analysis and Environmental Epidemiology, 2001.