Radio Environment Map Construction with Gaussian Process and Kernel Transformation

Xu, Yiqun; Zhang, Bangning; Zhang, Xiaokai; Hu, Jingming; Guo, Daoxing

doi:10.1109/ccisp52774.2021.9639312

Cited by 4 publications

(1 citation statement)

References 11 publications

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“…The accuracy of the spectrum maps achieved by using the model-based methods was poor due to the lack of flexibilities in the radio propagation. In contrast, the crowdsourcing-based methods constructed the spectrum maps more accurately via regression techniques or machine learning [5], such as matrix recovery [6], Kriging interpolation [7], the inverse distance weighted (IDW) interpolation [8], kernel-based learning [9], [10] and some data-driven methods proposed to further improve the accuracy [11], [12]. However, the spatial-correlationbased schemes only use spatial interpolation and cannot obtain the spectrum maps when there is no signal information about the target frequency.…”

Section: Introductionmentioning

confidence: 99%

Accurate Spectrum Map Construction Using An Intelligent Frequency-Spatial Reasoning Approach

Wang

Zhou

et al. 2022

GLOBECOM 2022 - 2022 IEEE Global Communications Conference

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Spectrum map is of crucial importance for realizing efficient spectrum management in the sixth-generation (6G) wireless communication networks. However, the existing spectrum map construction schemes mainly depend on spatial interpolation and cannot construct the spectrum map when the measurement data of the target frequency are not obtained. In order to overcome this challenge, an accurate spectrum map construction scheme is proposed by using an intelligent frequency-spatial reasoning approach. The frequency correlation among different spectrum maps at different frequencies is fully exploited to construct the highly accurate spectrum maps of the frequencies without spectrum data. A novel autoencoder adapting to the three-dimensional (3D) spectrum data is proposed. Simulation results demonstrate that our proposed scheme is superior to the benchmark schemes in terms of the construction accuracy. Moreover, it is shown that our proposed autoencoder network has a fast convergence speed.

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