Temporal prediction for spectrum environment maps with moving radiation sources
Spectrum resources are becoming harder to come by for wireless communications. The spectrum environment map (SEM), which depicts the electromagnetic environment's current state and future trend, is a valuable technique for managing and allocating spectrum resources. Most SEM construction approa...
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Veröffentlicht in: | IET Communications 2023-03, Vol.17 (5), p.538-548 |
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Sprache: | eng |
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Zusammenfassung: | Spectrum resources are becoming harder to come by for wireless communications. The spectrum environment map (SEM), which depicts the electromagnetic environment's current state and future trend, is a valuable technique for managing and allocating spectrum resources. Most SEM construction approaches only take static SEMs into account and cannot forecast time‐domain changes and trends of SEMs in dynamic scenes. In this paper, a brand‐new temporal SEM prediction method for the high dynamic spectrum environment is proposed. This method is based on knowledge of radiation source and the optical flow driven by propagation channel models. First, a novel radiation source localization strategy is designed to obtain the radiation source movement information. Then, the optical flow field of the available SEMs is combined with the information regarding radiation source movement. In order to forecast future SEMs, a propagation model driven reconstruction technique is developed. Simulation findings demonstrate how well the suggested strategy is tailored to capture the spatiotemporal correlation of SEMs. This technique performs better than the state‐of‐the‐art in terms of single‐ and multiple‐step SEM predictions.
In this paper, we proposes a novel temporal prediction method for dynamic spectrum environment maps (SEMs), based on the radiation source information and the optical flow driven by propagation channel models. Simulation results show that our temporal prediction method has splendid adaptability to obtain the time‐domain variation of SEM. |
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ISSN: | 1751-8628 1751-8636 |
DOI: | 10.1049/cmu2.12560 |