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

TL;DR

This paper reviews deep learning techniques for spectrum prediction in cognitive radio networks, compares them with traditional methods, and introduces a novel spatiotemporal prediction framework validated on real data, highlighting future challenges.

Contribution

It provides a comprehensive review of DL-based spectrum prediction methods and proposes a new ViTransLSTM framework combining visual attention and LSTM for improved accuracy.

Findings

DL outperforms traditional spectrum prediction methods.

The proposed ViTransLSTM effectively captures spatiotemporal dependencies.

Validation on real-world data demonstrates the framework's superiority.

Abstract

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.