1. Network-side Positioning of Cellular-band Devices with Minimal Effort
Ayon Chakraborty, Luis Ortiz, and Samir R. Das
IEEE INFOCOM 2015

2. What is Network-Side Positioning?
Neighboring Cell Towers
Cell phone located at <X, Y>
Serving Cell Tower
RSS2
RSS1
RSS1 RSS2 RSS3
RSS3
Before going into the details let’s look at what do we mean by network side positioning?
Consider a cellphone sitting at a location (x,y). It is connected to the serving cell tower but at the same time can hear signal from the neighboring cell towers.
Then as a part of the cellular protocols the cellphone records the signal strength values it receives from these towers, consolidates them and sends it to the serving cell tower. This information can be utilized by the network providers to estimate the location of the cellphone.
Estimate Location
RSS: Received Signal Strength
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3. Network Providers are Constrained
Unlike OTT Apps …typically no direct access to such sensors
ONLY utilize cellular signal strength information
You might wonder that a modern smartphone itself can provide a huge array of information including rich sensor data. So why don’t the phone send such data to the network operator?
Although there are numerous OTT apps exactly do that, a network provider does not have direct access to the phone’s platform. The only information they can use is the signal strength information that the phone sends them as a part of the cellular protocols.
However localizing cellphones from the signal strength information is not a new problem. A methodology called fingerprinting has been enormously successful. In the next couple of slides I would introduce fingerprinting in general. (modify the linking part, GPS (samir), fingerprinting should be explained)
Sensors Galore! 3
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4. Fingerprinting in Cellular Networks
Fingerprint Database
Location
Feature Vector
X1, Y1
< RSSA RSSB RSSC>1
RSSB
RSSA
Tower A
(X1, Y1)
Tower B
RSSC
Consider the region as shown in the picture. There are three towers A, B and C. Consider a location (x1, y1). At (x1, y1) signal strengths from towers A, B and C are recorded as RSSa/b/c. This is as if the tuple of signal strengths is mapped to the location (x1,y1). This mapping is stored as an entry in our fingerprint database.
Light GRID
Tower C 4
WINGS Lab
WINGS Lab

5. Fingerprinting in Cellular Networks
Fingerprint Database
Location
Feature Vector
X1, Y1
< RSSA RSSB RSSC>1
X2, Y2
< RSSA RSSB RSSC>2
Tower A
Tower B
RSSB
RSSA
(X2, Y2)
Similarly for another location (x2,y2) we store the mapping to the signal strengths received at that location. This is the second entry in our fingerprint database.
RSSC
Tower C 5
WINGS Lab
WINGS Lab

6. Fingerprinting in Cellular Networks
Fingerprint Database
RSSA
RSSB
Location
Feature Vector
(X3, Y3)
X1, Y1
< RSSA RSSB RSSC>1
RSSC
X2, Y2
< RSSA RSSB RSSC>2
Tower A
X3, Y3
< RSSA RSSB RSSC>3
Tower B
The same thing for a new location (x3, y3) and we keep on adding entries to our database.
Tower C 6
WINGS Lab
WINGS Lab

7. Fingerprinting in Cellular Networks
Fingerprint Database
Location
Feature Vector
X1, Y1
< RSSA RSSB RSSC>1
X2, Y2
< RSSA RSSB RSSC>2
Tower A
X3, Y3
< RSSA RSSB RSSC>3
Tower B
X4, Y4
< RSSA RSSB RSSC>4
X5, Y5
< RSSA RSSB RSSC>5
… … …
XN, YN
< RSSA RSSB RSSC>N
Tower C 7
WINGS Lab
WINGS Lab

8. Fingerprinting in Cellular Networks
Fingerprint Database
Location
Feature Vector
X1, Y1
< RSSA RSSB RSSC>1
X2, Y2
< RSSA RSSB RSSC>2
Tower A
X3, Y3
< RSSA RSSB RSSC>3
Tower B
X4, Y4
< RSSA RSSB RSSC>4
X5, Y5
< RSSA RSSB RSSC>5
… … …
Once we are done with collecting enough fingerprints in the region we finalize the fingerprint database. Now it is our turn to use this database in location estimations.
XN, YN
< RSSA RSSB RSSC>N
Tower C 8
WINGS Lab
WINGS Lab

9. Fingerprinting-based Localization Techniques
Fingerprint Database
Test Data
?, ?
< RSSA RSSB RSSC >
Location
Feature Vector
X1, Y1
< RSSA RSSB RSSC>1
X2, Y2
< RSSA RSSB RSSC>2
Estimate Location
X3, Y3
< RSSA RSSB RSSC>3
X4, Y4
< RSSA RSSB RSSC>4
X5, Y5
< RSSA RSSB RSSC>5
… … …
ACCURACY exlain
In the diagram we show a generic localization technique that takes as input two things. First the fingerprint database. Second the test data. Test data is the signal strength recorded from a location unknown to the algorithm. The algorithm computes an estimate for the unknown location.
This is the way a generic fingerprinting based localization technique works. Many techniques have been proposed … some are statistical or probabilistic and some are deterministic. There is active research going on to improve such algorithms. State of the art fingerprinting algorithms can give an accuracy around a hundred meters.
XN, YN
< RSSA RSSB RSSC>N
Many deterministic / statistical techniques
Median accuracy ≈ 100 – 200m 9
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WINGS Lab

10. Accuracy Depends on Fingerprint Density
Fingerprint Database
Test Data
?, ?
< RSSA RSSB RSSC >
Location
Feature Vector
X1, Y1
< RSSA RSSB RSSC>1
X2, Y2
< RSSA RSSB RSSC>2
Estimate Location
X3, Y3
< RSSA RSSB RSSC>3
X4, Y4
< RSSA RSSB RSSC>4
X5, Y5
< RSSA RSSB RSSC>5
… … …
Although many of such algorithms offer good accuracy, they are inherently dependant on the density or the amount of fingerprint data that is fed it. More the amount of fingerprint data the better is the accuracy.
However collecting location tagged signal strength data is tedious for the operator. It might involve wardriving or incentivizing customers to install OTT apps that give out GPS data. It increases the operating costs sometimes making such algorithms impractical in reality.
EXPLAIN ACTUAL COST IN DETAILS
XN, YN
< RSSA RSSB RSSC>N ∝
(# of Locations) Cost 10
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WINGS Lab

11. Our Work: Minimizing Labeled Data Requirement
Fingerprint Database
Test Data
?, ?
< RSSA RSSB RSSC >
Location
Feature Vector
X1, Y1
< RSSA RSSB RSSC>1
____ , ____
< RSSA RSSB RSSC>2
Estimate Location
X3, Y3
< RSSA RSSB RSSC>3
____ , ____
< RSSA RSSB RSSC>4
____ , ____
< RSSA RSSB RSSC>5
… … …
Say it in a different way .. Not remove ..less data can we meet accuracy with less cost?
What if we keep the signal strength information but remove the location labels from the fingerprint database. The tuples with location stamps in it are the labeled data, where as the tuples without location stamps are our unlabeled data. The unlabeled data are available to the operator at almost zero cost.The main focus of this work is to get rid of the labeled data requirement.
XN, YN
< RSSA RSSB RSSC>N
Minimize labeled data
Provide good accuracy with less cost
Unlabeled Data
Labeled Data 11
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WINGS Lab

12. Mostly Unlabeled Data, Few Labeled Data
Tower A
Tower B
Unlabeled data + =
Labeled data
Arrow directions
Now I will present an intuition about how we solve the problem with minimal labeled data. The figure shows the same previous setting.
But in this case with the labeled data we have a bunch of unlabeled data as well. Just to remind you the unlabeled data has no location stamps but only contains a bunch of signal strength information from cell towers.
The interesting point is that the labeled combined with the unlabeled data creates a semi supervised setting.
Semi-supervised Setting
Tower C 12
WINGS Lab
WINGS Lab

13. Semi-supervised Clustering
Tower A
Tower B
Unsupervised clustering
Labeled data anchors clusters
Confusing
Consider only the unlabeled data. These data can be clustered in the signal space. The intuition is that the signals obtained from the same or similar location tend to cluster together in the signal space. However a few representative labeled data can anchor such signal clusters to physical coordinate space.
Tower C 13
WINGS Lab
WINGS Lab

14. Location Estimation L1 P1 P5 L5 L2 P2 L4 L3 P3 P4
Tower A L5
Physical location of cluster 2
Tower B L2 P2
Now we have learned the clusters. Each cluster is represented in physical space by the coordinates L1, L2 etc. These are the cluster centers.
Suppose we have the phone providing the test signal. Our algorithm assigns different probabilities to the test signal to belong to different clusters. Let these probabilities be P1, p2 and so on.
We estimate the location of the phone as an average of the cluster centers weighted by corresponding probability values. L4
RSSA RSSB RSSC
Prob. that phone belongs to cluster 4 L3 P3 P4
Tower C 14
WINGS Lab
WINGS Lab

15. Semi-supervised Modeling Approach
Marginal Gaussian PDF of Signal S (received signal strengths from towers), given location L (hidden variable)  fS|L
Mixture of independent Gaussians (GMM) over all possible locations
Learning problem: Learn fS|L
Having the intuition ready we would dive into the theoretical details of our approach. Consider S be the vector of signal strength values that are received from different cell towers.
For a given location L, S follows a gaussian distribution. The gaussians at each location are independent of each other.
Over all possible locations this becomes a mixture of Gaussians and the problem essentially is to learn the gaussian mixture model. 15
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WINGS Lab

16. A Simple Example Many unlabeled signal strength samples
Combined Gaussian distribution actually seen
Gaussian
distribution
at E
Gaussian
distribution
at W
Probability W E
Signal strength
We illustrate the method by a simple example.
Assume 1D, 1 base station, 2 discrete locations west and east.
Consider signal samples are collected from both of these locations but are mostly unlabeled. The plot shows the distribution of signal strength values, which is supposed to be a mixture of two gaussians one for east and the other for west. After we learn the mixture model, given a signal S we can figure out the probability it appears from W’s gaussian or E’s gaussian.
Many unlabeled signal strength samples
Few samples with label E or W
Given test signal STest estimate the probabilities p(W| STest) and p(E| STest) 16
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WINGS Lab

17. A Simple Example
p(W|RSS2) = 0.65
P(E|RSS2) = 0.35
p(W|RSS1) = 0.1
p(E|RSS1) = 0.9
p(W|RSS3) = 0.95
p(E|RSS3) = 0.05
Midpoint of W
Midpoint of E
Probability W E
RSS1
RSS2
RSS3
Signal strength
Change eqn
We illustrate this with a very simple example. Say this and this are the midpoints of locations W and E. We predict a test signal RSS1 using our learnt mixture model. Suppose the predictions appear to be 0.1 for the West’s gaussian and 0.9 for the East one. We estimate the location by this equation that I introduced earlier. Again for a signal value that is kind of midway between the two distributions its hard to differentiate that what distribution it came from a location midway between W and E.
Estimated location = p(W|RSS)*midpoint of W + p(E|RSS)*midpoint of E. 17
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WINGS Lab

18. Our Experimental Setup
University campus: Partitioned into uniform grid (15mx15m)
Each grid cell ⁼ one location
≈ 3K grid cells.
2.5 Kms (approx.)
Not to scale write
From hypothetical to a problem of bigger scale: locations increased, n-dimentional instead of 1 in previous.
2.5 Kms (approx.)
Not drawn to scale 18
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WINGS Lab

19. Data Collected 35K samples at outdoor locations
T-Mobile’s GSM network on Nexus4/5 phone (our technique not specific to GSM though)
10K samples kept aside for testing
Not only gsm or technology dependant 19
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WINGS Lab

20. Algorithm Overview
Step 1: Initialize each location with a Gaussian(mean, variance) and prior probability for the location
Step 2: Run Expectation Maximization (EM)
Handles partially-labeled training data
The EM converges to the local MLE
Yields ‘learned model’
Step 3: Predict using learned model
Now I will explain the EM based algorithm that we used for learning the mixture model. First for every location in our system we initialize the mean and variance of the corresponding gaussian and a uniform prior probability to the clusters. We run the EM algorithm over the partially labeled data that converges to a MLE model. The model is a mixture of gaussians where a component is estimated for each individual location. 20
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21. Median Accuracy ≈ 70m 10% training samples have labels Mixture Model
K-Nearest Neighbors
Gaussian Naïve Bayes
By using 10% labeled data in the EM the model gives an accuracy of about 70m on our campus data. MENTION X and Y LABELS of plot
10% training samples have labels 21
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WINGS Lab

22. Median Accuracy ≈ 90m 1% labeled training samples Mixture Model
K-Nearest Neighbors
Gaussian Naïve Bayes
However when we scale down the labeled data by an order of magnitude there isn’t a huge performance deficit. MENTION X and Y LABELS of plot
1% labeled training samples 22
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23. Observation 1: More Unlabeled Data Reduces Error2%
20%
Of course, up to theoretical limit
(Bayes Risk)
Now we make several observations. First we see as we increase the amount of unlabeled data it improves the accuracy, off course till a certain limit. This is interesting as unlabeled data is available to the providers at almost zero cost. MENTION X and Y LABELS of plot
Good news!
Unlabeled measurements easy to obtain 23
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WINGS Lab

24. Observation 2: Higher Errors are Spatially Clustered
The second observation we make is that the high errors show some sort of spatial clustering. Again this is a good news as this can be improved by targeted data collection by the provider. MENTION X and Y LABELS of plot 24 24
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WINGS Lab

25. Observation 3: Accuracy Improves with Cell Tower Density
# of features = cell towers heard
Almost 40m!
Feature == base station
Third we observe that with more cell towers the accuracy increases. With 6 neighboring cells we can achieve a median accuracy of 40meters. Very much applicable for small cell scenarios. MENTION X and Y LABELS of plot
Potential for good performance in high density deployments (e.g., small cells / urban regions) 25 25
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26. Summary of Our Contributions
Minimize labeled training data requirement – a cost center for operators
1% labeled data achieves ≈ 90m median accuracy
Additional unlabeled data improves accuracy, up to a theoretical limit (Bayes risk)
Possible to extend to completely unsupervised setting (see paper) 26 26
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WINGS Lab
WINGS Lab

27. Thank You Acknowledgement Huawei Technologies, New Jersey, USA 27
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WINGS Lab

29. Cell Towers
The region (University campus) is partitioned into a uniform grid (15mx15m) and each grid cell represents a candidate location (~3K grid cells).
Remove cell towers backup
These are our locations.
2.5 Kms (approx.)
Cell Tower
2.5 Kms (approx.)
WINGS Lab