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3D Compressed Spectrum Mapping with Sampling Locations Optimization in Spectrum-Heterogeneous Environment

Shen, F; Wang, Z; Ding, G; Li, K; Wu, Q; (2022) 3D Compressed Spectrum Mapping with Sampling Locations Optimization in Spectrum-Heterogeneous Environment. IEEE Transactions on Wireless Communications , 21 (1) pp. 326-338. 10.1109/TWC.2021.3095342. Green open access

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Abstract

Spectrum mapping has emerged as an important problem in wireless communications, which generates a spectrum map for the spectrum resource analysis and management. Given the constrained transceiver volume and the limited energy consumption, how to effectively reconstruct the spectrum situation by the limited sampling data is a pressing challenge for spectrum mapping. In this paper, by exploiting the sparse nature of spectrum situation, we firstly attempt to solve the three-dimensional (3D) compressed spectrum mapping problem in the way of compressed sensing. Then, we develop a quadrature and right-triangular (QR) pivoting based measurement matrix optimization algorithm. By iteratively selecting new dominant sampling locations, it promotes the recovery accuracy compared to random measurement. After that, we propose a 3D spatial subspace based orthogonal matching pursuit (OMP) algorithm to recover spectrum situation for 3D compressed spectrum mapping. Finally, simulations are presented to show the comparisons in terms of localization, source signal strength recovery, recovery success rate and situation recovery. Results show our proposed 3D spectrum mapping scheme not only effectively reduces the sampling number, but also achieves a high level of spectrum mapping accuracy.

Type: Article
Title: 3D Compressed Spectrum Mapping with Sampling Locations Optimization in Spectrum-Heterogeneous Environment
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/TWC.2021.3095342
Publisher version: https://doi.org/10.1109/TWC.2021.3095342
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Spectrum mapping, compressed sensing, spectrum-heterogeneity, OMP algorithm
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics
URI: https://discovery.ucl.ac.uk/id/eprint/10133402
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