A Data-Driven Approach to Localization ... - Michael B. Crouse · Marcus Comiter, Michael Crouse, HT Kung Harvard University, USA. Presentation Outline • Background and Motivation

A Data-Driven Approach to Localization for High Frequency

Wireless Mobile Networks

Marcus Comiter, Michael Crouse, HT KungHarvard University, USA

Presentation Outline

• Background and Motivation for Data-Driven Localization in High Frequency/5G Networks

• Neural Network Structure Design Based on Application Domain• A Structured Model for Resolving Co-linearity• Epsilon Invariant Loss Function

• Real-world Experiments and Synthetic Data Generation• Angle Dependent Noise

• Performance Results for Real-world Scenarios• Conclusion and Future Work

IEEEGLOBECOM- 2017 2

Background and Motivations for Indoor Localization of Mobile Devices

• The planned use of millimeter waves (mmW), such as those operating at 28GHz, 38GHz or higher, by 5G cellular systems represents a major opportunity

• 5G rollout will consist of BOTH sub-6GHz and 20GHz+ capabilities• Key advantages:

• mmWaves can provide unprecedentedly high data rates for mobile devices, e.g., gbits/s• Major challenge:

• mmWaves are very directional so need to address intermittent blockages• Mobile devices and base stations need to find each other dynamically, quickly, and in high

resolution to achieve consistently high bandwidths• Background:

• In-band scanning with mmW introduces an overhead, especially for mobile• GPS-based localization infeasible for indoor deployments, early target for mmWave

deployments• Alternative methods based on fine-grained physical modeling of the environment would

generally be too labor intensive to be practical, if feasible at all


Data-driven Localization of Mobile Nodes with Neural Networks for Directional mmWave Communications

• Localization based on phase differences at low frequencies (e.g., 2.4 GHz) at multiple base stations equipped with antenna arrays

• For multipath-rich environments, a data-drivenapproach can be especially robust

• We can use pattern matching to solve equations such as for triangulation


Multiple dual mode base stations equipped with phased antenna arrays surrounding a mmWave capable mobile device

Structured Multilayer Perceptron (MLP)Neural Network for Collinearity Mitigation


A

B

A

B

Co-line

ar

C

A-CCoverage

Collinear Regions:Impossible to predict with only the corresponding pairs of nodes

We can use other pairs’ good regions to cover collinear regions of the current pair

B-CCoverage

CollinearalityIssuefor(B,C)

CollinearalityMitigation by(A,C)

C

PredictedMobileDeviceLocation

AOAfromEachoftheBaseStations

End to End Structured Network Model


• To operate on raw phase offset information, we utilize the same structured model for each base station, which is then fed into the Angle to Location Unit

Epsilon-Invariant Loss Function (EILF): Tuning to Specified Localization Tolerance


• When using localization for use in beam sector selection alignment, the level of accuracy required is relative to the antenna beamwidth, i.e., wider beams require less accurate localization for correct alignment

• The EILF loss function is shown on the right, which sets the loss to zero in network training if the predicted location is within epsilon of the true location, otherwise it is set to be the squared error loss

Real-world Localization Experiments

• We collected measurement data in three separate environments, an Outdoor Field, a Classroom and, an Open Space Lounge

• For each experiment, we created a 5x6 grid of (30) locations for the transmitter antenna (mobile node)

• Full I/Q measurements are recorded then repeated for all 30 Tx locations at each of the three base station locations

• Data collected on different days form training and test datasets, respectively


90 cm

500 cm

BS

343 cm

83.3 cm

68.6 cmBS

BS

Real-world Measurement Observation:Angle-dependent Noise in Phase Offset Measurements


• Our real-world experiments show that as the angle relative to the base stations increases, the noise and, therefore, calculated angle of arrival error (using MUSIC), increases

• Our simulation platform adds this type of angle-dependent noise for evaluating our models and remains future work for incorporating directly into the learning model

Synthetic Data Generation for Improved Accuracy under Multipath• For the applicability of data-driven models for real world use, we can

expect only a relatively small number of training points in real-world deployments

• It is important to be able to utilize synthetically generated data to augment the relatively small amount of available real-world training measurements

• We propose generating synthetic training data by adding Gaussian noise to training data to mitigate the random variation between days (not new obstacles) without having to capture significantly more samples

• This synthetic data has no cost (neither computational nor human) to generate

• Intuitively, the synthetic data allows the model to deal with noise and reduce overfitting while and capture other sources of variations in measurement data that may not be in the limited real-world training data


Real-world Performance With and Without Synthetic Training Data


• We collected measurement data in three separate environments, an outdoor field, a 30 student classroom, and a student lounge space

• For the Real + Synthetic results, a synthetic data generation factor of 20 is used (i.e., 20x the amount of collected real-world training data).

MedianSquaredErrorinmeters

Impact of the Amount Synthetic Data for Localization Accuracy for Data-Driven Models


• Increasing the amount of synthetic training data improves the localization accuracy of the model significantly

• Improvement converges after a synthetic generation factor of around 10 or 15

• The indoor lounge environment localization error reduces by 2x when trained with the additional synthetic data

• The Lounge environment is the environment with the most background noise and movement that we measured

• Increased foot traffic, more complicated scatters (types and locations of furniture)

(m)

Conclusion

• We have proposed and evaluated a domain specific neural network structure, the Structured MLP, for reducing collinearity issues in localization for mmWave beam alignment

• Introduced a modified loss function, EILF, for model training to meet the necessary localization error based on the application domain (e.g., mmWave beamwidth)

• Demonstrated the effectiveness of using synthetic training data in model training for reducing the data collection burden and improved overall localization accuracy of the model

• Provided real-world performance results for three environments with 3 base stations and a single mobile device on a dense grid


THANK YOU


Backup Slides


Co-linearity Example and Comparison


Measurement Equipment - Base Station Rig• Our base station consists of 4

USRPs operating at 867 MHz, with a GPS-based time synchronization (1 pps)

• Our UE is another USRP which transmits a pulse of 2000 symbols

• We calibrate the phase information among each USRP using a reference node which transmit the same sequence at the beginning of each measurement from a known angle (90 degrees) [1]


[1]H.-C.Chen,T.-H.Lin,H.Kung,C.-K.Lin,andY.Gwon,“Determiningrf angleofarrivalusingcotsantennaarrays:afieldevaluation,”inMILITARYCOMMUNICATIONSCONFERENCE,2012-MILCOM2012.IEEE,2012,pp.1–6.

Synthetic Data Generation Algorithm


Model Size and Inference Time

• 2 layers Lower Pair Network (LPN) with 500 and 50 neurons per layer• 2 layers in the Upper Connection Network (UCN) with 200 and 50

neurons per layer• A commodity accelerator (GPU) capable of executing 4500 GFLOPs

can execute the (3×3×500×50)+(1×50× 200 × 50) = 725, 000 multiply and addition operations in 0.02 milliseconds.

• Inference operation can also be optimized using work on network compression and fixed point/binarized network design

• Training time for the networks is on the order of minutes (< 10 minutes)


Future Work

• Consider various number of antenna configurations rather than a signal polarity with just 4 antennas

• With massive MIMO, we could see 16 x 16 antenna array which would increase accuracy significantly

• Explore synthetic data generation using recent machine learning techniques, such as Generative Adversarial Networks (GANs)

• These models have potential to capture more complex variations due to fading


Documents

A Data-Driven Approach to Localization ... - Michael B. Crouse · Marcus Comiter, Michael Crouse, HT Kung Harvard University, USA. Presentation Outline • Background and Motivation