Radio Frequency Mapping using an Autonomous Robot: Application to the 2.4 GHz Band

In the era of 5G mobile communication, radio environment maps are increasingly viewed as a powerful weapon for the optimization of spectrum resources, especially in the field of autonomous vehicles. However, due to the constraint of limited resources when it comes to sensor networks, it is crucial to select a suitable scale of sensor measurements for radio environment map construction. This article proposes an adaptive ordinary Kriging algorithm based on affinity propagation clustering as a novel spatial interpolation method for the construction of the radio environment map, which can provide precise awareness of signal strength at locations where no measurements are available. Initially, a semivariogram is obtained from all the sensor measurements. Then, in order to select the minimum scale of measurements and at the same time guarantee accuracy, the affinity propagation clustering is introduced in the selection of sensors. Moreover, the sensor estimation groups are created based on the clustering result, and estimation results are obtained by ordinary Kriging. In the end, the simulation of the proposed algorithm is analyzed through comparisons with three conventional algorithms: inverse distance weighting, nearest neighbor, and ordinary Kriging. As a result, the conclusion can be drawn that the proposed algorithm is superior to others in accuracy as well as in efficiency.

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“…27 The applicability of this channel model in REM has been empirically tested in previous works. 22,28,29…”

Section: Model and Problem Statementmentioning

confidence: 99%

Radio environment map construction by adaptive ordinary Kriging algorithm based on affinity propagation clustering

Xia

Zha

Huang

et al. 2020

International Journal of Distributed Sensor Networks

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“…where G 0 is a constant that captures antenna and other propagation gains, γ is the path-loss exponent and ||x t − x i || is the Euclidean distance between TX and RX positions, and ψ(x i ) is the spatially correlated shadow fading component (in dB), which is Gaussian distributed with mean zero and variance σ 2 ψ . The channel model (1) has been empirically confirmed by [13], [21]- [23], allowing to model the variations of received signal power in a wireless channel. Due to the imperfect RX characteristics, each agent is assumed to obtain a noisy version of the received power y i = P RX (x i ) + n i , where n i ∼ N (0, σ 2 n ).…”

Section: A Network and Channel Modelmentioning

confidence: 99%

Distributed channel prediction for multi-agent systems

Chowdappa

Frohole

Wymeersch

et al. 2017

2017 IEEE International Conference on Communications (ICC)

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Multi-agent systems (MAS) communicate over a wireless network to coordinate their actions and to report their mission status. Connectivity and system-level performance can be improved by channel gain prediction. We present a distributed Gaussian process regression (GPR) framework for channel prediction in terms of the received power in MAS. The framework combines a Bayesian committee machine with an average consensus scheme, thus distributing not only the memory, but also computational and communication loads. Through Monte Carlo simulations, we demonstrate the performance of the proposed GPR.

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“…For the scenario of non-cooperative transmitters, existing construction methods of radio maps process the estimation of signal power or any other electromagnetic physical quantities at one time slot, at any arbitrary locations within region of interest, based on the related measurements without prior information. These dominating methods include but not limit to interpolation [15], [16] or extrapolation and their revised algorithms [17], [18], semi-parameter regression [19], matrix completion [20], kernel-based learning [21], and dictionary learning [22]. These methods are effective to a certain extent, but challenges still remain.…”

Section: Introductionmentioning

confidence: 99%

Spectrum cartography based on dynamic compressed sensing by using multiple domains information

Xia,

Zha,

Huang

et al. 2023

J. Commun. Netw.

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Radio maps have experienced their success in applications of wireless communications for years by offering metrics of radio frequency (RF) information, e.g., power spectral density (PSD), within a geographical region of interest. Spectrum cartography technique constructs radio maps to expand the abilities of RF awareness. However, seldom of existing methods aim at constructing radio maps by utilizing multiple domains information. In this paper, a novel framework inspired by dynamic compressed sensing (DCS) has been proposed firstly to solve this problem. This flexible framework first to apply joint group-Lasso for PSD map construction based on the different sparse patterns between space and frequency domains as well as innovatively utilizes transmitters' mobility patterns for support prediction of DCS. Simulation experiments have been processed to assess the performance of methods within the proposed framework and framework's superiority has been proven.

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Radio Frequency Mapping using an Autonomous Robot: Application to the 2.4 GHz Band

Cited by 6 publications

References 2 publications

Radio environment map construction by adaptive ordinary Kriging algorithm based on affinity propagation clustering

Radio environment map construction by adaptive ordinary Kriging algorithm based on affinity propagation clustering

Distributed channel prediction for multi-agent systems

Spectrum cartography based on dynamic compressed sensing by using multiple domains information

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