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

2025-01-22

AI Summary

To address the challenge of constructing full-spectrum maps for 6G urban spectrum management—particularly under high-density communications and complex terrain, where strong inter-frequency coupling and sparse sampling severely degrade reconstruction accuracy—this work proposes a data- and semantics-driven spectrum mapping method. We design a novel UNet architecture integrating binary urban map priors and semantic knowledge of sampling locations, enabling joint frequency-spatial inference for cross-band spectrum map reconstruction from sparse measurements. Key innovations include semantic-guided feature fusion, binary map embedding, and a sparse spectral interpolation mechanism. The approach significantly improves spectrum map completeness and occupancy state inference accuracy under ultra-low sampling rates (<5%). Extensive evaluations across multiple real-world urban deployments demonstrate superior performance over state-of-the-art baseline methods.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Feature Construction/Reformulation
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications

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.

Problem

• Spectrum Mapping
• 6G Frequency Bands
• Urban Electromagnetic Environment

Innovation

• UNet technology
• Spectrum spatial and channel correlation
• Predictive spectrum mapping