ITU2022RSSIv2pptx.pptx

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Multi-task learning can improve indoor location estimation using Wi-Fi signals. The method uses a single model to jointly predict latitude and longitude from received signal strength indication (RSSI) measurements. This provides better accuracy than separate models for each dimension. The model accounts for non-line-of-sight signals and augments the training data. Evaluation shows the multi-task model achieves the lowest mean error compared to other techniques like XGBoost and random forests. Future work could exploit line-of-sight information and spatial/temporal dependencies to further enhance location accuracy.

Engineering

Multi-Task Learning for Location Estimation
Using RSSI of Wireless LAN in NLoS Environment
TeleAILab
2022-Dec-01

Background
• Outdoor location recognition Indoor location estimation
• People tracking in shopping malls, robot localization, autonomous car parking…
Credit to: http://www.hkzbai.com/show-10-51-1.html
Credit to: https://baike.baidu.com/pic/全球定位系统/1240960
Line of Sight (LoS) Non Line of Sight (NLoS)

Problem description
• Received Signal Strength Indication (RSSI) refers to access point (AP)
• LoS/NLoS environment

Motivation
Triangulation/Trilateration Fingerprint
• RSSI is inversely proportional to the distance to the power of a path loss exponent.
• Wi-Fi fingerprinting is usually conducted in two phases: an offline survey and an online query.
Credit to: Ilci et al. “Trilateration Technique for WiFi-Based Indoor Localization”
Credit to: Lee et al. “Random forest and WiFi fingerprint‐based indoor location
recognition system using smart watch
Credit to: Khokhar et al. “Machine Learning-Based Indoor Localization using Wi-Fi and Smartphone”
Credit to: He et al. “Wi-Fi Fingerprint-Based Indoor Positioning:
Recent Advances and Comparisons

Historical methodology
• RSSI is inversely proportional to the distance to the power of a path loss exponent.
Lognormal model
𝑃𝑅𝑋 dBm = 𝐴 − 10𝜂𝑙𝑜𝑔10
𝑑
𝑑0
+ 𝒩(0, 𝜎)
Frii’s formula
𝑃𝑅𝑋 dB = 20𝑙𝑜𝑔10
4𝜋𝑑
𝜆
FSPL
𝑃𝑅𝑋 dBm = 20𝑙𝑜𝑔10𝑑 + 20𝑙𝑜𝑔10𝑓 − 22.55
𝑑𝑖 = 𝑑𝑖 + 𝑏𝑖 + 𝑛𝑖, where 𝑏𝑖 =
0, if point is LOS
𝜓𝑖, if point is NLOS
, and 𝑛𝑖~𝒩(0, 𝜎)

Our Solution
• Our solution includes of three key steps.
Data Processing Location Estimation Evaluation
Modeling Statistics
Mean error (m)
Maximum error (m)
Processing
v.s.

Our Solution – Data Processing
• The distribution of the verification data resembles the one of the training data.
• The RSSI strength is linear to the 𝑙𝑜𝑔10𝑑

Our Solution – Data Processing
• No. offers little information, Channel contains constant values.
• There are duplicated samples, and little fluctuation with time.

Our Solution – Data Processing
• Augment data by permute the order the APs.

Our Solution – Location Estimation – Tree models
XGBoost
Random Forest
Extra Tree Regression
Latitude
Longitude
• Two models are necessary to predict latitude and longitude, respectively.

Our Solution – Location Estimation – Multi-task learning
Input Shared
Tower
Lat
Tower
Lon
Latitude estimation
Longitude estimation
RSSI Distance
𝑃𝑅𝑋 dBm = 𝐴 − 10𝜂𝑙𝑜𝑔10
𝑑
𝑑0
+ 𝒩(0, 𝜎)
Latitude
Longitude
𝑑 = 𝑥2 + 𝑦2
Trilateration/Triangulation
Predict latitude and longitude
in one model
Explanation:
…
…
…
…
…

Our Solution – Results
• Multi-task learning model obtains the best mean error.
• Most predictions of the multi-task learning model are relatively good.
error
Count

Outlook
• Explicit usage of LoS/NLoS information
• Spatial dependencies
• Dynamics
• 6G

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The rivalry between prominent international actors for dominance over Central Asia's hydrocarbon reserves and the ancient silk trade route, along with China's diplomatic endeavours in the area, has been referred to as the "New Great Game." This research centres on the power struggle, considering geopolitical, geostrategic, and geoeconomic variables. Topics including trade, political hegemony, oil politics, and conventional and nontraditional security are all explored and explained by the researcher. Using Mackinder's Heartland, Spykman Rimland, and Hegemonic Stability theories, examines China's role in Central Asia. This study adheres to the empirical epistemological method and has taken care of objectivity. This study analyze primary and secondary research documents critically to elaborate role of china’s geo economic outreach in central Asian countries and its future prospect. China is thriving in trade, pipeline politics, and winning states, according to this study, thanks to important instruments like the Shanghai Cooperation Organisation and the Belt and Road Economic Initiative. According to this study, China is seeing significant success in commerce, pipeline politics, and gaining influence on other governments. This success may be attributed to the effective utilisation of key tools such as the Shanghai Cooperation Organisation and the Belt and Road Economic Initiative.

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CHINA’S GEO-ECONOMIC OUTREACH IN CENTRAL ASIAN COUNTRIES AND FUTURE PROSPECT

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ITU2022RSSIv2pptx.pptx

1. Multi-Task Learning for Location Estimation Using RSSI of Wireless LAN in NLoS Environment TeleAILab 2022-Dec-01

2. Background • Outdoor location recognition Indoor location estimation • People tracking in shopping malls, robot localization, autonomous car parking… Credit to: http://www.hkzbai.com/show-10-51-1.html Credit to: https://baike.baidu.com/pic/全球定位系统/1240960 Line of Sight (LoS) Non Line of Sight (NLoS)

3. Problem description • Received Signal Strength Indication (RSSI) refers to access point (AP) • LoS/NLoS environment

4. Motivation Triangulation/Trilateration Fingerprint • RSSI is inversely proportional to the distance to the power of a path loss exponent. • Wi-Fi fingerprinting is usually conducted in two phases: an offline survey and an online query. Credit to: Ilci et al. “Trilateration Technique for WiFi-Based Indoor Localization” Credit to: Lee et al. “Random forest and WiFi fingerprint‐based indoor location recognition system using smart watch Credit to: Khokhar et al. “Machine Learning-Based Indoor Localization using Wi-Fi and Smartphone” Credit to: He et al. “Wi-Fi Fingerprint-Based Indoor Positioning: Recent Advances and Comparisons

5. Historical methodology • RSSI is inversely proportional to the distance to the power of a path loss exponent. Lognormal model 𝑃𝑅𝑋 dBm = 𝐴 − 10𝜂𝑙𝑜𝑔10 𝑑 𝑑0 + 𝒩(0, 𝜎) Frii’s formula 𝑃𝑅𝑋 dB = 20𝑙𝑜𝑔10 4𝜋𝑑 𝜆 FSPL 𝑃𝑅𝑋 dBm = 20𝑙𝑜𝑔10𝑑 + 20𝑙𝑜𝑔10𝑓 − 22.55 𝑑𝑖 = 𝑑𝑖 + 𝑏𝑖 + 𝑛𝑖, where 𝑏𝑖 = 0, if point is LOS 𝜓𝑖, if point is NLOS , and 𝑛𝑖~𝒩(0, 𝜎)

6. Our Solution • Our solution includes of three key steps. Data Processing Location Estimation Evaluation Modeling Statistics Mean error (m) Maximum error (m) Processing v.s.

7. Our Solution – Data Processing • The distribution of the verification data resembles the one of the training data. • The RSSI strength is linear to the 𝑙𝑜𝑔10𝑑

8. Our Solution – Data Processing • No. offers little information, Channel contains constant values. • There are duplicated samples, and little fluctuation with time.

9. Our Solution – Data Processing • Augment data by permute the order the APs.

10. Our Solution – Location Estimation – Tree models XGBoost Random Forest Extra Tree Regression Latitude Longitude • Two models are necessary to predict latitude and longitude, respectively.

11. Our Solution – Location Estimation – Multi-task learning Input Shared Tower Lat Tower Lon Latitude estimation Longitude estimation RSSI Distance 𝑃𝑅𝑋 dBm = 𝐴 − 10𝜂𝑙𝑜𝑔10 𝑑 𝑑0 + 𝒩(0, 𝜎) Latitude Longitude 𝑑 = 𝑥2 + 𝑦2 Trilateration/Triangulation Predict latitude and longitude in one model Explanation: … … … … …

12. Our Solution – Results • Multi-task learning model obtains the best mean error. • Most predictions of the multi-task learning model are relatively good. error Count

13. Our Solution – Results

14. Outlook • Explicit usage of LoS/NLoS information • Spatial dependencies • Dynamics • 6G

15. Thank you!

Editor's Notes

Multi-task learning Rising
Global Navigation Satellite System (GNSS) requires a direct LOS and the connection to at least 4 satellites simultaneously. It has a large position error and blocks signals on buildings and walls for indoor environment. Non-line of sight (NLOS) refers to the path of propagation of a radio frequency (RF) that is obscured (partially or completely) by obstacles Terminalogy: LoS/Nlos APsRSSI/RFID/Bluetooth have been studied for indoor location recognition. Indoor location-based service is promising, such as smart retail
- 4 access points Points with RSSI from corresponding Aps Latitude & longitude To estimate latitude and longitude for verification points with given RSSI
Recently, fingerprint-based methods drawn lots of attentions. Green -> blue
Recently, fingerprint-based methods drawn lots of attentions. Green -> blue
Theoretical studies on RSSI-distance relationships LOS/NLOS can be considered through a bias term Historical analysis/methodology
Modeling separasion. MTL network visulization and introduction re-orgnize
Investigate RSSI-distance relations
Channel: constant, remove Positional information LOS/NLOS Temporal information is useless
Ways of data augmentation.
Two models to predict latitude and longitude, individually.
One model to predict the position. Stein’s paradox: estimating the means of three or more Gaussian random variables using samples from all of them could yield better performance than estimating them separately Model interpretation
直方图形式还需要另加多任务优化图
直方图形式还需要另加多任务优化图 Conclusion: augmentation + mtl (mining)
Conclusion + outlook 6G application/ emphasize application value

ITU2022RSSIv2pptx.pptx

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Editor's Notes