Abstract

Large-scale spectrum monitoring infrastructures generate high-dimensional spectral time series, providing a critical data foundation for proactive spectrum management, anomaly detection, radio environment awareness, and interference-aware decision-making. In complex electromagnetic environments, real-world deployments are highly nonstationary and frequently affected by unexpected interference, which substantially degrades the predictability of spectrum dynamics and the reliability of downstream spectrum sensing and management systems. Consequently, classical linear forecasting methods and generic deep sequence models often generalize poorly from clean training conditions to interference-corrupted scenarios, as jamming patterns distort the latent representations used for future-spectrum forecasting. This study focuses on multivariate spectrum forecasting, where the objective is to predict multi-step future amplitude or power distributions across all frequency bins from a historical observation window. To address this limitation, we propose DS-SpecIT, a Decomposed Spectral Inverted Transformer for interference-aware spectrum forecasting. Unlike generic long-term forecasting models that mainly minimize average prediction errors, DS-SpecIT is specifically designed to handle structured electromagnetic interference. Its novelty lies in the integration of spectral tokenization, inverted attention over frequency tokens, an interference-aware dual-scale objective, and orthogonality-based latent feature separation. These components enable the model to jointly preserve global spectral trends and reduce local errors inside interference-affected time–frequency regions. Using publicly available spectrum measurements, we establish evaluation protocols under both clean and synthetic-jamming settings. Experiments show that DS-SpecIT maintains competitive clean setting accuracy while achieving stronger global and local robustness under structured interference.

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