Crowdsourced Indoor Wi-Fi REMs: Does the Spatial Interpolation Method Matter?

El-friakh, Zakarya; Voicu, Andra M.; Shabani, Shaham; Mähönen, Petri

doi:10.1109/dyspan.2018.8610487

Cited by 16 publications

(18 citation statements)

References 18 publications

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“…This work is extended to study how Kriging behaves in the presence of propagation model uncertainties, that stem from shadowing [33]. Studies that consider the recovery of sparse coverage data in an indoor environment include [34], [35], [36]. Using low-cost spectrum sensors in an office indoor environment, authors in [34] present an accuracy comparison between the spatial interpolation methods of Kriging, Gradient Plus Inverse Distance Squared and Inverse Distance Weighted methods.…”

Section: ) Data Sparsity Onlymentioning

confidence: 99%

“…Studies that consider the recovery of sparse coverage data in an indoor environment include [34], [35], [36]. Using low-cost spectrum sensors in an office indoor environment, authors in [34] present an accuracy comparison between the spatial interpolation methods of Kriging, Gradient Plus Inverse Distance Squared and Inverse Distance Weighted methods. The results show that there is no significant difference in the accuracy for the considered interpolation methods, relative to the variability in the measurements reported by different low-cost devices.…”

Section: ) Data Sparsity Onlymentioning

confidence: 99%

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Enhanced MDT-Based Performance Estimation for AI Driven Optimization in Future Cellular Networks

2020

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Minimization of drive test (MDT) allows coverage estimation at a base station by leveraging measurement reports gathered at the user equipment (UE) without the need for drive tests. Therefore, MDT is a key enabling feature for data and artificial intelligence driven autonomous operation and optimization in future cellular networks. However, to date, the utility of MDT feature remains thwarted by issues such as sparsity of user reports and user positioning inaccuracy. We characterize three key types of errors in MDT-based coverage estimation that stem from inaccurate user positioning, scarcity of user reports and quantization. For the first time, the presented analysis shows existence of joint interplay between these errors on coverage estimation that result from inter-dependency between positioning error and bin width. The analysis also shows that there exists an optimal bin width for given user positioning inaccuracy and user density that minimizes the overall error in MDT-based estimated coverage. Utility of our framework is presented by addressing two applications from network optimization perspective: determining optimal bin width to maximize accuracy of MDT-based coverage estimation and its calibration to further improve its accuracy. INDEX TERMS Minimization of drive test, autonomous coverage estimation, optimal bin width, sparse data, coverage calibration, data driven optimization

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Section: ) Data Sparsity Onlymentioning

confidence: 99%

Section: ) Data Sparsity Onlymentioning

confidence: 99%

Enhanced MDT-Based Performance Estimation for AI Driven Optimization in Future Cellular Networks

2020

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“…Because of its practicality, many researchers have investigated embedding an REM into wireless systems over the last decade, e.g., spectrum sharing over television white space (TVWS) [6], coverage prediction in cellular networks [7], and communication quality prediction in WLANs [8].…”

Section: A Kriging-based Radio Environment Mapmentioning

confidence: 99%

On the Performance of Neural Network Residual Kriging in Radio Environment Mapping

2019

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This paper addresses the research question: can feedforward neural network (FFNN)-based path loss modeling improve the accuracy of Kriging? Radio propagation factors, which consist of path loss and shadowing, can accurately be obtained via crowdsourcing with Kriging. In most works on Kriging-aided radio environment mapping, measurement datasets are first regressed via linear path loss modeling to ensure spatial stationarity of the shadowing. However, in practical situations, the path loss often contains an anisotropy owing to terrain and obstacle effects. Thus, Kriging may not perform an optimal interpolation because of the errors in path loss modeling. In this paper, an FFNN is used for path loss modeling. Then, ordinary Kriging is applied to interpolate the shadowing. We first evaluate the performance of this method in a case where the transmitter is fixed. It is shown that this method does not improve Kriging in a large-scale and fixed transmitter system; although the FFNN outperforms OLS in path loss modeling. Then, this method is extended to distributed wireless networks where transmitters are arbitrarily located, such as in mobile ad hoc networks (MANETs) and vehicular ad hoc networks (VANETs). The results of a measurement-based experiment show that the FFNN is capable of improving Kriging in such a distributed network case.

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“…To test the proposed algorithm on real data, we consider an office environment with one WiFi AP, using the set-up and data from [43]. In the set-up, different sensing Raspberry PI boards (RPi3) are placed on the floor on a grid with inter-distance of 20cm.…”

Section: The Proposed Sensing Algorithm As a Remmentioning

confidence: 99%

“…Each node cooperates with neighboring nodes that are within 2m, and obtain w k (we drop the subscript m). Then, we use the simple inverse distance weighting spatial interpolation (IDW) method [43] at every j-th node in (S) c , which is expressed as w j = l∈S β lj w l ∀j ∈ (S) c , where here we set β lj to be inversely proportional to the distance squared between the j-th and l-th nodes [43].…”

Section: The Proposed Sensing Algorithm As a Remmentioning

confidence: 99%

Distributed Wideband Sensing-Based Architecture for Unlicensed Massive IoT Communications

Hattab

Čabrić

2019

IEEE Trans. Cogn. Commun. Netw.

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Providing Internet connectivity to a massive number of Internet-of-things (IoT) objects over the unlicensed spectrum requires: (i) identifying a very large number of narrowband channels in a wideband spectrum and (ii) aggressively reusing the available channels over space to accommodate the high density of IoT devices. To this end, we propose a sensing-based architecture that identifies spectral and spatial resources at a fine resolution. In particular, we first propose a sensing assignment scheduler, where each base station (BS) is assigned a subset of the spectrum to sense at a high resolution. We then propose a distributed sensing algorithm, where BSs locally process and share their sensing reports, so that each BS obtains occupancy information of the wideband spectrum at its location. Once the spatio-spectral resource blocks are identified, we further propose a distributed resource allocation algorithm that maintains high spatial reuse of spectral opportunities while limiting the intra-network and inter-network interference. Numerical simulations are presented to validate the effectiveness of the proposed distributed algorithms, comparing them to centralized and non-cooperative schemes. It is shown that our architecture identifies more spatiospectral resources, with lower misdetection of incumbents. As a result, more IoT devices are connected with limited interference into incumbents.

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Crowdsourced Indoor Wi-Fi REMs: Does the Spatial Interpolation Method Matter?

Cited by 16 publications

References 18 publications

Enhanced MDT-Based Performance Estimation for AI Driven Optimization in Future Cellular Networks

Enhanced MDT-Based Performance Estimation for AI Driven Optimization in Future Cellular Networks

On the Performance of Neural Network Residual Kriging in Radio Environment Mapping

Distributed Wideband Sensing-Based Architecture for Unlicensed Massive IoT Communications

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