▷ Device Free Indoor Localization in the 28 GHz band based on machine learning

⭐⭐⭐⭐ Device Free Indoor Localization in the 28 GHz band based on machine learning

• ➡️ #MillimeterWave #MMwave #28GHz #IndoorLocation #WirelessInSite #Remcom #Matlab
• ✅ #ML #MachineLearning #AI #ArtificialIntelligence #RegressionLearner #Classification
• ➡️ #ANT2023: The 14th International Conference on Ambient Systems, Networks and Technologies, March 15-17, 2023, Leuven, Belgium
• ✅ Some of the functionalities used in MATLAB are found in the repository: https://github.com/vasanza/Matlab_Code
• ⭐ When using this resource, please cite the original publication:
• Avilés, J. C., Ojeda, V., & Asanza, V. (2023). Device Free Indoor Localization in the 28 GHz band based on machine learning. Procedia Computer Science, 220, 55-63.
• ✅ Abstract:
• By exploiting the received power change in a communication link produced by the presence of a human body in an otherwise empty room, this work evaluates indoor free device localization methods in the 28 GHz band using machine learning techniques. For this objective, a database is built using results from ray tracing simulations of a system comprised of 4 receivers and up to 2 transmitters, while a person is standing within the room. Transmitters are equipped with uniform linear arrays that switch their main beams sequentially at 21 angles, whereas the receivers operate with omnidirectional antennas. Statistical localization error reduction of at least 16% over a global-based classification technique can be obtained through the combination of two independent classifiers using one transmitter and a reduction of at least 19% for 2 transmitters. An additional improvement is achieved by combining each independent classifier with a regression algorithm. Results also suggest that the number of examples per class and size of the blocks (strips) in which the study area is partitioned play a role in the localization error.

✅ Conference content:

• ➡️ Introduction

• ➡️ Related Work / Motivation

• ➡️ Methodology

• ➡️ Results and Discussion

• ➡️ Conclusions

✅ References:

1. Kibilda, Jacek, et al. (2020) “Indoor Millimeter-Wave Systems: Design and Performance Evaluation.” Proceedings of the IEEE, 108 (6): 923-944
2. Xu, Chenren, Firner Bernhard, Yanyong Zhang, Richard Howard, Jun Li, and Xiaodong Lin. (2012) “Improving RF-based device-free passive localization in cluttered indoor environments through probabilistic classification methods.” ACM/IEEE 11th International Conference on Information Processing in Sensor Networks (IPSN), China.
3. Han, Bingyang, Zhenghuan Wang, Heng Liu, Shengxin Xu, Xiangyuan Bu, and Jianping An. (2016) “Shadow fading assisted device-free localization for indoor environments.” 8th International Conference on Wireless Communications & Signal Processing (WCSP), China.
4. Konings, Daniel, Fakhrul Alam, Frazer Noble, and Edmunnd M-K. Lai. (2019) “Device-Free Localization Systems utilizing Wireless RSSI: A comparative practical investigation.” IEEE sensors Journal, 19 (7): 2747-2757
5. Hong, Jihoon, and Tomoaki Ohtsuki. (2014) “Device-free passive localization from signal subspace eigenvectors.” 2014 IEEE Global Communications Conference, Austin, Tx, USA.
6. Seifeldin, Moustafa, Ahmed Saeed, Ahmed E. Kosba, Amr El-Keyi, and Moustafa Youssef. (2013) “Nuzzer: A Large-Scale Device-Free Passive Localization System for Wireless Environments.” IEEE Transactions on Mobile Computing, 12 (7): 1321-1334.
7. Mager, Brad, Philip Lundrigan, and Neal Patwari. (2015) “Fingerprint-Based Device-Free Localization Performance in Changing Environments.” IEEE Journal on Selected Areas in Communications, 33 (11): 2429-2438.
8. Shit, Rathin Chandra, Suraj Sharma, Deepak Puthal, Philip James, Biswajeet Pradhan, Aad van Moorsel, Albert Y. Zomaya, and Rajiv Ranjan. (2019) “Ubiquitous Localization (UbiLoc): A Survey and Taxonomy on Device Free Localization for Smart World.” IEEE Communications Surveys & Tutorials, 21 (4): 3532-3564.
9. Zhan, Jinnan, Jianhua Zhang, Lei Tian, Xinzhuang Zhang, Pan Tang, Jianwu Dou, and Hewen Wei. (2018) “Comparative Channel Study of Ray Tracing and Measurement for an Indoor Scenario at 28 GHz.” 12th European Conference on Antennas and Propagation, London, UK.
10. Kanhere, Ojas, and Theodore S. Rappaport. (2018) “Position Locationing for Millimeter Wave Systems.” IEEE Global Communications Conference (GLOBECOM). Abu Dhabi, United Arab Emirates.
11. Homh, Wei et al. (2021) “The Role of Millimeter-Wave Technologies in 5G/6G Wireless Communications.” IEEE Journal of Microwaves,1 (1): 101-122.
12. Guo, Xiansheng, and Nirwan Ansari. (2017) “Localization by Fusing a Group of Fingerprints via Multiple Antennas in Indoor Environment.” IEEE Transactions on Vehicular Technology, 66 (11): 9904 - 9915
13. Klus, Lucie, Darwin Quezada-Gaibor, Joaquın Torres-Sospedra, Elena Simona Lohan, Carlos Granell, and Jari NurmiǤ ሺ2Ͳ22ሻ “Towards Accelerated Localization Performance Across Indoor Positioning Datasets.” International Conference on Localization and GNSS, Tampere, Finland.
14. Ojeda, Verónica, and Juan Avilés. (2022) “Rx position effect on Device Free Indoor Localization in the 28 GHz band.” Sensors Applications Symposium (SAS). Sundsvall, Sweden.
15. Van Trees, Harry. (2002) “Optimum array processing, Part IV of Detection, Estimation and Modulation Theory.” John Wiley &Sons, NY.
16. Kaya, Aliye, and Harish Wiswanathan. (2017) “Coverage and capacity impact of mobility and Human body blocking at millimeter waves.” 2017 IEEE Global Communications Conference, Singapore.
17. Chen, Xubin, Lei Tian, Pan Tang, and Jianhua Zhang. (2016) “Modeling of Human Body shadowing based on 28 GHz indoor measurments results.” IEEE, 84th Vehicular Technology Conference (VTC-Fall), Montreal, Canada.
18. Wu, Ting, Theodore S. Rappaport, and Christopher M. Colllins. (2015) “The human body and Millimeter-Wave Wireless Communication Systems: Interactions and Implications.” 2015 IEEE International Conference on Communications (ICC), London, UK.
19. http://www.remcom.com/wireless-insite
20. Kubat, Miroslav. (2017) “An Introduction to Machine Learning.” 2 nd edition, Springer.
21. Criminisi, Antonio, Jaime Shotton, and Ender Konokogu. (2012) “Decision Forests: A Unified Framework for Classification, Regression, Density Estimation, Manifold Learning and Semi-Supervised Learning.” Foundations and Trends in Computer Graphics and Vision, 7 (2,3): 81–227

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