Environment mapping using wireless Channel State Information and deep learning

Adrian Donarski; Iain Collings; Stephen Hanly

doi:10.1109/ICSPCS50536.2020.9310056

Environment mapping using wireless Channel State Information and deep learning

Adrian Donarski, Iain Collings, Stephen Hanly

School of Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

Abstract

This paper uses wireless Channel State Information (CSI) and deep learning for obtaining situational awareness through environment mapping. Algorithms are developed that permit estimation of interior room dimensions using wireless Single-Input Single-Output (SISO) Channel Impulse Responses (CIRs). The impact of signal received power, and signal bandwidth, on the ability to predict room dimensions is further investigated. Under such CIR limitations, room dimension estimation accuracy of greater than 90% is achieved using Recurrent Neural Networks (RNNs) with a maximum predictive error of 0.5m. This demonstrates that existing consumer wireless systems with similar physical constraints, may be capable of accurately estimating room dimensions given multiple CIR measurements from multiple receiver locations.

Original language	English
Title of host publication	2020, 14th International Conference on Signal Processing and Communication Systems, (ICSPCS)
Subtitle of host publication	proceedings
Editors	Tadeusz A. Wysocki, Beata J. Wysocki
Place of Publication	Piscataway, NJ
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	1-9
Number of pages	9
ISBN (Electronic)	9781728199726, 9781728199719
ISBN (Print)	9781728199733
DOIs	https://doi.org/10.1109/ICSPCS50536.2020.9310056
Publication status	Published - 2020
Event	14th International Conference on Signal Processing and Communication Systems, ICSPCS 2020 - Virtual Duration: 14 Dec 2020 → 16 Dec 2020

Conference

Conference	14th International Conference on Signal Processing and Communication Systems, ICSPCS 2020
City	Virtual
Period	14/12/20 → 16/12/20

Access to Document

10.1109/ICSPCS50536.2020.9310056

Link to publication in Scopus

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Donarski, A., Collings, I., & Hanly, S. (2020). Environment mapping using wireless Channel State Information and deep learning. In T. A. Wysocki, & B. J. Wysocki (Eds.), 2020, 14th International Conference on Signal Processing and Communication Systems, (ICSPCS): proceedings (pp. 1-9). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/ICSPCS50536.2020.9310056

Donarski, Adrian ; Collings, Iain ; Hanly, Stephen. / Environment mapping using wireless Channel State Information and deep learning. 2020, 14th International Conference on Signal Processing and Communication Systems, (ICSPCS): proceedings. editor / Tadeusz A. Wysocki ; Beata J. Wysocki. Piscataway, NJ : Institute of Electrical and Electronics Engineers (IEEE), 2020. pp. 1-9

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title = "Environment mapping using wireless Channel State Information and deep learning",

abstract = "This paper uses wireless Channel State Information (CSI) and deep learning for obtaining situational awareness through environment mapping. Algorithms are developed that permit estimation of interior room dimensions using wireless Single-Input Single-Output (SISO) Channel Impulse Responses (CIRs). The impact of signal received power, and signal bandwidth, on the ability to predict room dimensions is further investigated. Under such CIR limitations, room dimension estimation accuracy of greater than 90% is achieved using Recurrent Neural Networks (RNNs) with a maximum predictive error of 0.5m. This demonstrates that existing consumer wireless systems with similar physical constraints, may be capable of accurately estimating room dimensions given multiple CIR measurements from multiple receiver locations.",

author = "Adrian Donarski and Iain Collings and Stephen Hanly",

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note = "14th International Conference on Signal Processing and Communication Systems, ICSPCS 2020 ; Conference date: 14-12-2020 Through 16-12-2020",

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Donarski, A, Collings, I & Hanly, S 2020, Environment mapping using wireless Channel State Information and deep learning. in TA Wysocki & BJ Wysocki (eds), 2020, 14th International Conference on Signal Processing and Communication Systems, (ICSPCS): proceedings. Institute of Electrical and Electronics Engineers (IEEE), Piscataway, NJ, pp. 1-9, 14th International Conference on Signal Processing and Communication Systems, ICSPCS 2020, Virtual, 14/12/20. https://doi.org/10.1109/ICSPCS50536.2020.9310056

Environment mapping using wireless Channel State Information and deep learning. / Donarski, Adrian; Collings, Iain ; Hanly, Stephen.

2020, 14th International Conference on Signal Processing and Communication Systems, (ICSPCS): proceedings. ed. / Tadeusz A. Wysocki; Beata J. Wysocki. Piscataway, NJ : Institute of Electrical and Electronics Engineers (IEEE), 2020. p. 1-9.

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

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AB - This paper uses wireless Channel State Information (CSI) and deep learning for obtaining situational awareness through environment mapping. Algorithms are developed that permit estimation of interior room dimensions using wireless Single-Input Single-Output (SISO) Channel Impulse Responses (CIRs). The impact of signal received power, and signal bandwidth, on the ability to predict room dimensions is further investigated. Under such CIR limitations, room dimension estimation accuracy of greater than 90% is achieved using Recurrent Neural Networks (RNNs) with a maximum predictive error of 0.5m. This demonstrates that existing consumer wireless systems with similar physical constraints, may be capable of accurately estimating room dimensions given multiple CIR measurements from multiple receiver locations.

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Donarski A, Collings I , Hanly S. Environment mapping using wireless Channel State Information and deep learning. In Wysocki TA, Wysocki BJ, editors, 2020, 14th International Conference on Signal Processing and Communication Systems, (ICSPCS): proceedings. Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE). 2020. p. 1-9 https://doi.org/10.1109/ICSPCS50536.2020.9310056