3D Spectrum Mapping and Reconstruction under Multi-Radiation Source Scenarios

TL;DR

This paper introduces a novel 3D spectrum mapping method for multi-radiation source scenarios in cognitive radio systems, utilizing a data-model-knowledge-driven approach that adapts to environmental changes and requires low sampling data.

Contribution

It proposes a new reconstruction scheme combining clustering, evolutionary optimization, and self-learning of path loss models for accurate 3D spectrum mapping.

Findings

High reconstruction accuracy with low sampling rate

Effective detection of multiple radiation sources in 3D space

Adaptive to environmental variations

Abstract

Spectrum map construction, which is crucial in cognitive radio (CR) system, visualizes the invisible space of the electromagnetic spectrum for spectrum-resource management and allocation. Traditional reconstruction methods are generally for two-dimensional (2D) spectrum map and driven by abundant sampling data. In this paper, we propose a data-model-knowledge-driven reconstruction scheme to construct the three-dimensional (3D) spectrum map under multi-radiation source scenarios. We firstly design a maximum and minimum path loss difference (MMPLD) clustering algorithm to detect the number of radiation sources in a 3D space. Then, we develop a joint location-power estimation method based on the heuristic population evolutionary optimization algorithm. Considering the variation of electromagnetic environment, we self-learn the path loss (PL) model based on the sampling data. Finally, the 3D spectrum is reconstructed according to the self-learned PL model and the extracted knowledge of radiation sources. Simulations show that the proposed 3D spectrum map reconstruction scheme not only has splendid adaptability to the environment, but also achieves high spectrum construction accuracy even when the sampling rate is very low.