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Ordinary kriging interpolation for indoor 3D REM

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Abstract

This work has investigated the feasibility of applying inverse distance weight (IDW), ordinary kriging (OK) and K-nearest neighbour (K-NN) interpolation to capture the three dimensional (3D) distribution of television (TV) grey space (TVGS) within the interior of a six storied building. Comparison of root mean square error (RMSE), correlation coefficient (CC) and speed of interpolation has been made to identify the trade-offs involved. Texture-patch transformation (TPT) has been applied for the first time to transform 3D to two dimensional (2D) for predicting TVGS. This work is the first to design an interpolation-based 3D indoor radio environment map (REM) for an active ultra-high frequency TV channel with wideband spectrum sensing. OK was shown as the most accurate method using a cross-validated comparison on RMSE and CC as metrics. A TVGS volume of 18,200 m3 was identified inside the 3D REM structure through interpolation. With an extensive experimental study of the symmetric vertical profile and choice of patch size, a TPT framework for indoor 3D REM was applied to speed-up IDW and K-NN interpolation. The complexity of the TPT based interpolation methods was also carried out to analyze the advantage of speed. Several semivariogram models were tested to arrive at the best one for using them in kriging algorithms. K-NN based interpolation for TVWS REM in 3D and through TPT has also been reported as a new approach to test the comparative performance, and through extensive cross-validation, we have deduced optimum values of K in terms of RMSE and CC accuracies.

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References

  • Akyildiz IF et al (2010) Flexible and spectrum-aware radio access through measurements and modelling in cognitive radio systems. FARAMIR Document: D2.1, (ICT 248351). https://www.yumpu.com/en/document/read/4637589/flexible-and-spectrum-aware-radio-access-through-faramir. Accessed 30 Apr 2010

  • Bedogni L, Felice MD, Malabocchia F, Bononi L (2014a) Indoor communication over TV gray spaces based on spectrum measurements. In: 2014 IEEE wireless communications and networking conference (WCNC). IEEE, pp 3218–3223

  • Bedogni L, Achtzehn A, Petrova M, Mahonen P (2014b) Smart meters with TV gray spaces connectivity: a feasibility study for two reference network topologies. In: 2014 eleventh annual IEEE international conference on sensing, communication, and networking (SECON). IEEE, pp 537–545

  • Bedogni L, Trotta A, Felice MD (2015) On 3-dimensional spectrum sharing for TV white and Gray Space networks. In: 2015 IEEE 16th international symposium on a world of wireless, mobile and multimedia networks (WoWMoM). IEEE, pp 1–8

  • Bedogni L, Malabocchia F, Felice MD, Bononi L (2017) Indoor use of gray and white spaces: another look at wireless indoor communication. IEEE Veh Technol Mag 12(1):63–71

    Article  Google Scholar 

  • Chaudhari S, Kosunen M (2017) Spatial interpolation of cyclostationary test statistics in cognitive radio networks: methods and field measurements. IEEE Trans Veh Technol 67(2):1113–1129

    Article  Google Scholar 

  • Chou S et al (2019) A REM-enabled diagnostic framework in cellular-based IoT networks. IEEE Internet Things J 6(3):5273–5284

    Article  Google Scholar 

  • Cisco (2019) Cisco visual networking index: global mobile data traffic forecast update, 2017–2022, White paper. Cisco public. http://media.mediapost.com/uploads/CiscoForecast.pdf. Accessed Feb 2019

  • Dagres I et al (2011) Flexible and spectrum-aware radio access through measurements and modelling in cognitive radio systems. FARAMIR Document: D4.1, (ICT 248351). https://upcommons.upc.edu/bitstream/handle/2117/14940/FARAMIR-D4.1-Final.pdf?sequence=1. Accessed 30 Apr 2011

  • Evans D (2011) The Internet of Things how the next evolution of the internet is changing everything. White paper, Cisco Internet Business Solutions Group (IBSG). https://www.cisco.com/c/dam/en_us/about/ac79/docs/innov/IoT_IBSG_0411FINAL.pdf. Accessed Apr 2011

  • FCC (2008) Unlicensed operation in the TV broadcast bands. Federal Communications Commission (FCC 08-260). https://www.fcc.gov/document/unlicensed-operation-tv-broadcast-bands-additional-spectrum-3. Accessed 25 May 2004

  • Flores AB et al (2013) IEEE 802.11af: a standard for TV white space spectrum sharing. IEEE Commun Mag 51(10):92–100

    Article  Google Scholar 

  • Government of India Ministry of Communications Department of Telecommunications (2018) National frequency allocation plan-2018. Wireless Planning and Coordination Wing. https://dot.gov.in/whatsnew/national-frequency-allocation-plan-2018. Accessed 5 Nov 2018

  • Hashimoto R, Suto K (2020) SICNN: spatial interpolation with convolutional neural networks for radio environment mapping. In: 2020 International conference on artificial intelligence in information and communication (ICAIIC). IEEE, pp 167–170

  • Li J, Ding G, Zhang X, Wu Q (2018) Recent advances in radio environment map: a survey. In: MLICOM 2017: EAI international conference on machine learning and intelligent communications. Springer International Publishing, Berlin, pp 247–257

  • Ma J (2013) Three-dimensional irregular seismic data reconstruction via low-rank matrix completion. Geophysics 78(5):181–192

    Article  Google Scholar 

  • Maiti P, Mitra D (2017) Explore TV White Space for indoor small cells deployment and practical pathloss measurement. In: 2017 international conference on innovations in electronics, signal processing and communication (IESC). IEEE, pp 79–84

  • Mishra AK, Johnson DL (2015) White space communication: advances, developments and engineering challenges. Springer International Publishing, Cham

    Book  Google Scholar 

  • Nasreddine J et al (2013) The World is not flat: wireless communications in 3D environments. In: 2013 IEEE 14th international symposium on a world of wireless, mobile and multimedia networks (WoWMoM). IEEE, pp 1–9

  • Oh SW et al (2016) Introduction to Cognitive Radio and Television White Space. In: TV white space: the first step towards better utilization of frequency spectrum, 1st edn. Wiley-IEEE Press, Hoboken, pp 1-22

  • Patino M, Vega F (2018) Model for measurement of radio environment maps and location of white spaces for cognitive radio deployment. In: 2018 IEEE-APS topical conference on antennas and propagation in wireless communications (APWC). IEEE, pp 913–915

  • Pesko M et al (2014) Radio environment maps: the survey of construction methods. KSII Trans Internet Inf Syst 8(11):3789–3809

    Google Scholar 

  • Pesko M et al (2015) The indirect self-tuning method for constructing radio environment map using omnidirectional or directional transmitter antenna. EURASIP J Wirel Commun Netw 50(1):1–12

    Google Scholar 

  • Romanik J et al (2019) Electromagnetic situational awareness of cognitive radios supported by radio environment maps. In: 2019 signal processing symposium (SPSympo). IEEE, pp 1–6

  • Rufaida SI et al (2020) Construction of an indoor radio environment map using gradient boosting decision tree. Wirel Netw 26(8):6215–6236

    Article  Google Scholar 

  • Saeed RA, Shellhammer SJ (2012) TV white space spectrum technologies. CRC Press, Taylor & Francis Group, Florida

    Google Scholar 

  • Sato K, Inage K, Fujii T (2019) On the performance of neural network residual kriging in radio environment mapping. IEEE Access 7:94557–94568

    Article  Google Scholar 

  • Sinclair AJ, Blackwell GH (2002) Applied mineral inventory estimation, 1st edn. Cambridge University Press, Cambridge

  • Singh AS, Gangopadhyay R, Debnath S (2018) On the construction of radio environment map for underlay device-to-device networks. In: 2018 24th Asia-Pacific conference on communications (APCC). IEEE, pp 413–417

  • Suchański M et al (2020) Radio environment maps for military cognitive networks: density of small-scale sensor network vs. map quality. EURASIP J Wirel Commun Netw 2020(1):189

    Article  Google Scholar 

  • Tang M, Ding G (2016) A joint tensor completion and prediction scheme for multi-dimensional spectrum map construction. IEEE Access 4:8044–8052

    Article  Google Scholar 

  • Ulaganathan S et al (2015) Building accurate radio environment maps from multi-fidelity spectrum sensing data. Wirel Netw 22(8):2551–2562

    Article  Google Scholar 

  • Wellmer F-W (1998) Statistical evaluations in exploration for mineral deposits. Springer, Hannover

    Book  Google Scholar 

  • Yilmaz HB, Tugcu T (2015) Location estimation-based radio environment map construction in fading channels. Wirel Commun Mob Comput 15(3):561–570

    Article  Google Scholar 

  • Yilmaz HB, Tugcu T, Alagöz F, Bayhan S (2013) Radio environment map as enabler for practical cognitive radio networks. IEEE Commun Mag 51(12):162–169

    Article  Google Scholar 

  • Ying X et al (2017) Exploring indoor white spaces in metropolises. ACM Trans Intell Syst Technol 9(1):1–25

    Article  Google Scholar 

  • Zhang X, Knightly EW (2016) WATCH: WiFi in active TV channels. IEEE Trans Cogn Commun Netw 2(4):330–342

    Article  Google Scholar 

  • Zhang Q, Liu S, Huang Y, Feng Z (2015) Time-spectrum-space three dimensions radio environment map construction and utilization in TV white space. Wirel Pers Commun 84(4):2271–2287

    Article  Google Scholar 

Download references

Acknowledgements

The work was supported by Central Public Works Department, Dhanbad and Doordarshan Kendra, Dhanbad.

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  1. Department of Electronics Engineering, IIT(ISM) Dhanbad, Dhanbad, Jharkhand, India

    Pradipta Maiti & Debjani Mitra

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  1. Pradipta Maiti
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  2. Debjani Mitra
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Correspondence to Pradipta Maiti.

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Maiti, P., Mitra, D. Ordinary kriging interpolation for indoor 3D REM. J Ambient Intell Human Comput 14, 13285–13299 (2023). https://doi.org/10.1007/s12652-022-03784-2

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