# Deep Learning for Spectrum Prediction in Cognitive Radio Networks: State-of-the-Art, New Opportunities, and Challenges

arXiv: https://arxiv.org/abs/2412.09849 · HTML: https://mlsanity.com/paper/2412.09849/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 15 references that are in the map.
- First posted on arXiv 13 Dec 2024.

## Builds on (most cited first, 3 of 3)

- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [PredRNN: A Recurrent Neural Network for Spatiotemporal Predictive Learning](https://mlsanity.com/paper/2103.09504/) (2021)
- [Movable Antenna Enabled Interference Network: Joint Antenna Position and Beamforming Design](https://mlsanity.com/paper/2403.13573/) (2024)

## Built on by (most cited first, 2 of 2)

- [Spectrum Prediction in the Fractional Fourier Domain with Adaptive Filtering](https://mlsanity.com/paper/2508.17872/) (2025)
- [RadioMapMotion: A Dataset and Baseline for Proactive Spatio-Temporal Radio Environment Prediction](https://mlsanity.com/paper/2511.17526/) (2025)

## Abstract (arXiv)

Spectrum prediction is considered to be a promising technology that enhances spectrum efficiency by assisting dynamic spectrum access (DSA) in cognitive radio networks (CRN). Nonetheless, the highly nonlinear nature of spectrum data across time, frequency, and space domains, coupled with the intricate spectrum usage patterns, poses challenges for accurate spectrum prediction. Deep learning (DL), recognized for its capacity to extract nonlinear features, has been applied to solve these challenges. This paper first shows the advantages of applying DL by comparing with traditional prediction methods. Then, the current state-of-the-art DL-based spectrum prediction techniques are reviewed and summarized in terms of intra-band and crossband prediction. Notably, this paper uses a real-world spectrum dataset to prove the advancements of DL-based methods. Then, this paper proposes a novel intra-band spatiotemporal spectrum prediction framework named ViTransLSTM. This framework integrates visual self-attention and long short-term memory to capture both local and global long-term spatiotemporal dependencies of spectrum usage patterns. Similarly, the effectiveness of the proposed framework is validated on the aforementioned real-world dataset. Finally, the paper presents new related challenges and potential opportunities for future research.