Novel Direction-of-Arrival-Based Localization in Massive DECT-2020 5G NR Networks
This research investigates an affordable, energy-efficient direction-of-arrival (DOA)-based localization solution for digital enhanced cordless telecommunications (DECTs) 2020 new radio (NR), a new standard lacking a native positioning feature. This standard enables massive Internet of Things (IoT) networks, a vast 5G network interconnecting an unparalleled number of low-cost and battery-operated smart sensors. However, integrating DOA localization into such networks is challenging due to cost constraints and power limitations. We propose a potentially cost-effective solution using a single radio-frequency (RF) chain for uniform L-shaped antenna arrays. Each antenna takes turns sampling the orthogonal frequency division multiplexing (OFDM) signal via an RF switch, enabled by time-dividing the OFDM signal into sample and switch slots. Further, we introduce a novel DOA method optimized for single Line-of-Sight (LOS) OFDM signals and array sequential sampling. This method leverages the dual shift-invariant properties of L-shaped antenna arrays and the array frequency response to estimate the azimuth and elevation angles. Experiments in an indoor environment reveal that at a signal-to-noise ratio (SNR) of 15 dB, over 50% of data achieve subdegree angular accuracy, increasing to 75% at 20 dB. Thus, over 50% of position estimations fall below the submeter error level at 15 dB SNR, rising to nearly 75% at 25 dB SNR. Our findings also indicate that halving the slot rate by proportionately reducing active subcarriers does not compromise accuracy. Experiments on the nRF52480 system-on-chip show the new DOA method is both fast and energy-efficient, taking only 0.76–2.26 ms and consuming 5.08–15.1 nWh.
Authors
- Hans Jakob Damsgaard (ORCID: https://orcid.org/0000-0001-8409-0282)
- Tiago Troccoli (ORCID: https://orcid.org/0000-0003-0022-2589)
- Jari Nurmi (ORCID: https://orcid.org/0000-0003-2169-4606)
- Ville Kaseva (ORCID: https://orcid.org/0000-0002-5592-4476)
- Aleksandr Ometov (ORCID: https://orcid.org/0000-0003-3412-1639)
- Elena Simona Lohan (ORCID: https://orcid.org/0000-0003-1718-6924)
- Jorge Morte (ORCID: https://orcid.org/0000-0003-1468-2404)
- Juho Pirskanen (ORCID: https://orcid.org/0000-0002-8694-3944)
Institutions
- Tampere University of Applied Sciences (FI)
- Tampere University (FI)
- Tampere University (FI)
Publication Details
- Journal
- IEEE Internet of Things Journal
- Published
- 2024-10-07
- DOI
- https://doi.org/10.1109/jiot.2024.3474918
- Citations
- 4
- Primary Topic
- Indoor and Outdoor Localization Technologies
- Type
- article
- Field-Weighted Citation Impact
- 0.83
Funders
- H2020 Marie Skłodowska-Curie Actions