Indoor Direct Positioning With Imperfect Massive MIMO Array Using Measured Near-Field Channels

Pan, Yujian; Bast, Sibren De; Pollin, Sofie

doi:10.1109/tim.2021.3067963

Cited by 8 publications

(13 citation statements)

References 31 publications

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“…Later, we showed a practical distributed antenna system that is indeed able to reliably locate users based on measured data [10]. Moreover, [7] and [11] show that for an indoor scenario, a distributed antenna deployment increases the localisation accuracy in comparison to co-located systems.…”

Section: A Distributed Localisationmentioning

confidence: 84%

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Expert-Knowledge-Based Data-Driven Approach for Distributed Localization in Cell-Free Massive MIMO Networks

2022

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Massive Multiple-Input Multiple-Output (MaMIMO) communication networks are recently being investigated for hltheir high potential for localisation services. This is enabled by the high-dimensional channel state information (CSI) captured by the many antennas in the system. Previously, it has been shown that these systems can achieve a very high localisation accuracy. However, many challenges still remain, we identified two of them. First, the recent trend towards cell-free MaMIMO with many highly distributed Access Points (AP), leads to the question of how this impacts the localisation methods. Current localisation methods process the signals in a central processing unit (CPU), resulting in a high fronthaul requirement when deploying these algorithms in a distributed network, limiting the deployment and scalability. Second, there exists a trade-off between using model-driven and data-driven localisation methods. In this work, we propose two new localisation methods which employ a distributed processing strategy and compare them against two centralised localisation methods. In addition, the four analysed methods explore the tradeoff between being model-and data-driven. Moreover, the proposed ML-MUSIC method blurs the lines between the two by combining Machine Learning and traditional signal processing. Next to comparing the localisation accuracy, we evaluate the performance in a dynamic setting, the scalability and fronthaul requirement of the methods. The proposed Machine Learning-enhanced Multiple Signals Classification method, ML-MUSIC, reaches a median error of 34.2 mm on the test set while only using 500 training samples. Due to ML-MUSICs distributed design, the fronthaul throughput requirement is reduced 1200fold in comparison to the centralised methods. Furthermore, ML-MUSIC has the lowest computational complexity of all analysed methods, making it an ideal method to localise users in upcoming distributed cell-free MaMIMO networks.

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Section: A Distributed Localisationmentioning

confidence: 84%

“…As a result, the calibration accuracy achieved is limited in practise. To combat this, [7] proposes a method to calibrate NF MUSIC using data measured during the dense channel survey presented in Section II.…”

Section: ) End-to-end Machine Learningmentioning

confidence: 99%

Expert-Knowledge-Based Data-Driven Approach for Distributed Localization in Cell-Free Massive MIMO Networks

2022

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“…In total, the testbed supports up to 128 antennas at the base station and 12 antennas at the user side [47]. The setup has previously been used for the purposes of positioning and communications [48]- [50].…”

Section: The Massive Mimo Testbed a System Setupmentioning

confidence: 99%

Near-Field Coherent Radar Sensing Using a Massive MIMO Communication Testbed

Sakhnini

Bast

Guenach

et al. 2022

IEEE Trans. Wireless Commun.

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This paper considers the problem of radar sensing by using a large number of antennas. We use the orthogonal frequency division multiplexing (OFDM) waveform, and show that the large arrays used in massive multiple-input multipleoutput (MIMO) communications enable accurate localization in the array near-field, even at the narrow bandwidths typically encountered at low carrier frequencies. We validate our findings experimentally with a massive MIMO testbed operating at 3.5 GHz carrier frequency and 18 MHz OFDM bandwidth in an indoor environment. We consider a single moving cylinder, and demonstrate a median accuracy of (3.4, 5.6) cm in (x, y) in the near-field. We show that the accuracy is maintained with only a single subcarrier, and that the resolution increases with an order of magnitude when combining all antennas, effectively surpassing the 16.67 m bistatic range resolution set by the OFDM waveform. We use a radar symbol duration of 71.88 µs at an effective transmission period of 2.5 ms, which indicates that the radar and communication systems can be implemented in timedivision with a capacity loss of only 2.9%. Our results suggest that near-field radar sensing can be integrated into future massive MIMO systems operating at low carrier frequencies and narrow bandwidths.

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“…In-situ calibration measures the array error in the actual operating environment using auxiliary calibration sources whose positions, which are also denoted as the CPPs, are known [21]- [27], [29]- [34]. It reaches a reasonable compromise between chamber calibration and self-calibration as it obtains the exact in-field array response with significantly lower complexity and much better accuracy than self-calibration.…”

mentioning

confidence: 99%

“…Besides, other research efforts in counteracting the multipath effect for in-situ calibration include: Pan et al [34], who solve it from the statistical perspective by treating the summation of the multipath components as Gaussian noise based on the Rayleigh fading assumption; and Sippel et al [26], who propose to choose the CPPs within the array's near field to elude multipath effects.…”

mentioning

confidence: 99%

In-Situ Calibration of Antenna Arrays for Positioning With 5G Networks

Pan¹,

Liu²,

Liu³

et al. 2023

Preprint

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Owing to the ubiquity of cellular communication signals, positioning with the fifth generation (5G) signal has emerged as a promising solution in global navigation satellite system-denied areas. Unfortunately, although the widely employed antenna arrays in 5G remote radio units (RRUs) facilitate the measurement of the direction of arrival (DOA), DOA-based positioning performance is severely degraded by array errors. This paper proposes an in-situ calibration framework with a user terminal transmitting 5G reference signals at several known positions in the actual operating environment and the accessible RRUs estimating their array errors from these reference signals. Further, since sub-6GHz small-cell RRUs deployed for indoor coverage generally have small-aperture antenna arrays, while 5G signals have plentiful bandwidth resources, this work segregates the multipath components via super-resolution delay estimation based on the maximum likelihood criteria. This differs significantly from existing in-situ calibration works which resolve multipaths in the spatial domain. The superiority of the proposed method is first verified by numerical simulations. We then demonstrate via field test with commercial 5G equipment that, a reduction of 46.7% for 1-σ DOA estimation error can be achieved by in-situ calibration using the proposed method.

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Indoor Direct Positioning With Imperfect Massive MIMO Array Using Measured Near-Field Channels

Cited by 8 publications

References 31 publications

Expert-Knowledge-Based Data-Driven Approach for Distributed Localization in Cell-Free Massive MIMO Networks

Expert-Knowledge-Based Data-Driven Approach for Distributed Localization in Cell-Free Massive MIMO Networks

Near-Field Coherent Radar Sensing Using a Massive MIMO Communication Testbed

In-Situ Calibration of Antenna Arrays for Positioning With 5G Networks

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