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Fingerprinting Based Localization with Channel State Information Features and Spatio-Temporal Deep Learning in Long Term Evolution Femtocell Network: An Experimental Approach

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Sustainable Communication Networks and Application (ICSCN 2019)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 39))

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Abstract

The need for accurate indoor localization for related services and radio resource management have imparted increased research attention on fingerprinting based positioning. In this work, we present a novel spatio-temporal deep learning (STDL) based fingerprinting method for indoor localization utilizing Channel State Information (CSI). The system works in two phases, acquisition and training and tracking. Experimental evaluation of the method for LTE femtocell signal with Software Defined Radio (SDR) hardware found to outperform spatial and temporal Received Signal Strength (RSS), spatial CSI and temporal CSI based methods.

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Acknowledgements

Authors would like to thank Mr. Diganta Kumar Pathak for his assistance in the measurement phase of this work and TEQIP III, MHRD, Govt. of India for the support.

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Gauhati University, Guwahati, 781014, Assam, India

    Manasjyoti Bhuyan & Kandarpa Kumar Sarma

Authors
  1. Manasjyoti Bhuyan
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  2. Kandarpa Kumar Sarma
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Correspondence to Manasjyoti Bhuyan .

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Editors and Affiliations

  1. Surya Engineering College, Kathirampatti, Tamil Nadu, India

    P. Karrupusamy

  2. Department of Electrical Engineering, Da-Yeh University, Changhua, Taiwan

    Joy Chen

  3. Department of Computer Science, Kennesaw State University, Kennesaw, GA, USA

    Yong Shi

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Bhuyan, M., Sarma, K.K. (2020). Fingerprinting Based Localization with Channel State Information Features and Spatio-Temporal Deep Learning in Long Term Evolution Femtocell Network: An Experimental Approach. In: Karrupusamy, P., Chen, J., Shi, Y. (eds) Sustainable Communication Networks and Application. ICSCN 2019. Lecture Notes on Data Engineering and Communications Technologies, vol 39. Springer, Cham. https://doi.org/10.1007/978-3-030-34515-0_16

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