A Spatial Interpolation Method for Radio Frequency Maps Based on the Discrete Cosine Transform

Vanhoy, Garrett; Volos, Haris; Bastidas, Carlos E. Caicedo; Bose, T.

doi:10.1109/milcom.2013.181

Cited by 8 publications

(4 citation statements)

References 17 publications

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“…The accuracy of RSS values for a smooth RFMap depends on the interpolation technique used. In [15], the authors have employed Discrete Cosine Transform (DCT) for the interpolation of RSS values to construct a smooth RFMap:…”

Section: Rfmap Conceptmentioning

confidence: 99%

Three-Way Deep Neural Network for Radio Frequency Map Generation and Source Localization

Gill¹,

Nguyen²,

Thein³

et al. 2021

Preprint

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In this paper, we present a Generative Adversarial Network (GAN) machine learning model to interpolate irregularly distributed measurements across the spatial domain to construct a smooth radio frequency map (RFMap) and then perform localization using a deep neural network. Monitoring wireless spectrum over spatial, temporal, and frequency domains will become a critical feature in facilitating dynamic spectrum access (DSA) in beyond-5G and 6G communication technologies. Localization, wireless signal detection, and spectrum policy-making are several of the applications where distributed spectrum sensing will play a significant role. Detection and positioning of wireless emitters is a very challenging task in a large spectral and spatial area. In order to construct a smooth RFMap database, a large number of measurements are required which can be very expensive and time consuming. One approach to help realize these systems is to collect finite localized measurements across a given area and then interpolate the measurement values to construct the database. Current methods in the literature employ channel modeling to construct the radio frequency map, which lacks the granularity for accurate localization whereas our proposed approach reconstructs a new generalized RFMap. Localization results are presented and compared with conventional channel models.

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Section: Rfmap Conceptmentioning

confidence: 99%

Three-Way Deep Neural Network for Radio Frequency Map Generation and Source Localization

Gill¹,

Nguyen²,

Thein³

et al. 2021

Preprint

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“…, where y and z are calculated as follow: 30 October 2019. The soil composition, and adopted potato and wheat plantation strategy is presented in reports by Panigrahi et al 26 and Devi et al 27 , respectively.…”

Section: Experimentation Setupmentioning

confidence: 99%

“…The measured RSSI readings for wheat crop in sowing and flowering stages are shown in Figure 2. The Discrete Cosine Transform (DCT) interpolation technique has been used to develop the RF map from the RSS-sample dataset 30 . The employed path-loss model is the Log-Normal Shadowing and is represented by Equation 2.…”

Section: Experimentation Setupmentioning

confidence: 99%

Genetic Algorithm Optimized Node Deployment in IEEE 802.15.4 Potato and Wheat Crop Monitoring Infrastructure

Pal

Tripathi

Kumar

et al. 2020

Preprint

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This proposal investigates the effect of vegetation height and density on received signal strength between two sensor nodes communicating under IEEE 802.15.4 wireless standard. With the aim of investigating the path loss coefficient of 2.4 GHz radio signal in an IEEE 802.15.4 precision agriculture monitoring infrastructure, measurement campaigns were carried out in different growing stages of potato and wheat crops. Experimental observations indicate that initial node deployment in the wheat crop experiences network dis-connectivity due to increased signal attenuation, which is due to the growth of wheat vegetation height and density in the grain-filling and physical-maturity periods. An empirical measurement-based path loss model is formulated to identify the received signal strength in different crop growth stages. Further, a NSGA-II multi-objective evolutionary computation is performed to generate initial node deployment and is optimized over increased coverage, reduced over-coverage, and received signal strength. The results show the development of a reliable wireless sensor network infrastructure for wheat crop monitoring.

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“…Along with the rapidly increasing number of wireless devices and the scarcity of spectrum resources, the challenges, including spectrum resource scarcity and serious spectrum security status, are extending to aerospace from terrestrial space [3]. The question of how to efficiently manage and utilize the existing limited spectrum resources has received considerable attention [4][5][6]. Spectrum map is a database integrated technique that can visually display the distribution of different radio parameters such as received signal strength (RSS) over a geographical area and provide rich and accurate knowledge of radio context [7].…”

Section: Introductionmentioning

confidence: 99%

UAV-Assisted Three-Dimensional Spectrum Mapping Driven by Spectrum Data and Channel Model

Zhu

Ding

et al. 2021

Symmetry

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As the number of civil aerial vehicles increase explosively, spectrum scarcity and security become an increasingly challenge in both the airspace and terrestrial space. To address this difficulty, this paper presents an unmanned aerial vehicle-assisted (UAV-assisted) spectrum mapping system and a spectrum data reconstruction algorithm driven by spectrum data and channel model are proposed. The reconstruction algorithm, which includes a model-driven spectrum data inference method and a spectrum data completion method with uniformity decision mechanism, can reconstruct limited and incomplete spectrum data to a three-dimensional (3D) spectrum map. As a result, spectrum scarcity and security can be achieved. Spectrum mapping is a symmetry-based digital twin technology. By employing an uniformity decision mechanism, the proposed completion method can effectively interpolate spatial data even when the collected data are unevenly distributed. The effectiveness of the proposed mapping scheme is evaluated by comparing its results with the ray-tracing simulated data of the campus scenario. Simulation results show that the proposed reconstruction algorithm outperforms the classical inverse distance weighted (IDW) interpolation method and the tensor completion method by about 12.5% and 92.3%, respectively, in terms of reconstruction accuracy when the collected spectrum data are regularly missing, unevenly distributed and limited.

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A Spatial Interpolation Method for Radio Frequency Maps Based on the Discrete Cosine Transform

Cited by 8 publications

References 17 publications

Three-Way Deep Neural Network for Radio Frequency Map Generation and Source Localization

Three-Way Deep Neural Network for Radio Frequency Map Generation and Source Localization

Genetic Algorithm Optimized Node Deployment in IEEE 802.15.4 Potato and Wheat Crop Monitoring Infrastructure

UAV-Assisted Three-Dimensional Spectrum Mapping Driven by Spectrum Data and Channel Model

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