Data-and-Semantic Dual-Driven Spectrum Map Construction for 6G Spectrum Management

TL;DR

This paper introduces a novel dual-driven spectrum map construction method for 6G management that leverages semantic knowledge and joint reasoning to improve accuracy and coverage in complex urban environments.

Contribution

It proposes a UNet-based dual-driven approach with semantic and frequency-space reasoning, enabling comprehensive spectrum maps across all frequencies without extensive sampling.

Findings

Improves spectrum map accuracy in dense urban environments.

Effectively infers missing frequency spectrum data.

Outperforms benchmark schemes, especially with low sampling density.

Abstract

Spectrum maps reflect the utilization and distribution of spectrum resources in the electromagnetic environment, serving as an effective approach to support spectrum management. However, the construction of spectrum maps in urban environments is challenging because of high-density connection and complex terrain. Moreover, the existing spectrum map construction methods are typically applied to a fixed frequency, which cannot cover the entire frequency band. To address the aforementioned challenges, a UNet-based data-and-semantic dual-driven method is proposed by introducing the semantic knowledge of binary city maps and binary sampling location maps to enhance the accuracy of spectrum map construction in complex urban environments with dense communications. Moreover, a joint frequency-space reasoning model is exploited to capture the correlation of spectrum data in terms of space and frequency, enabling the realization of complete spectrum map construction without sampling all frequencies of spectrum data. The simulation results demonstrate that the proposed method can infer the spectrum utilization status of missing frequencies and improve the completeness of the spectrum map construction. Furthermore, the accuracy of spectrum map construction achieved by the proposed data-and-semantic dual-driven method outperforms the benchmark schemes, especially in scenarios with low sampling density.