Scalable Spectrum Availability Prediction using a Markov Chain Framework and ITU-R Propagation Models

• URL: http://arxiv.org/abs/2508.00028v1
• Date: Wed, 30 Jul 2025 03:22:55 GMT
• Title: Scalable Spectrum Availability Prediction using a Markov Chain Framework and ITU-R Propagation Models
• Authors: Abir Ray,
• Abstract summary: This paper proposes a scalable framework for spectrum availability prediction.<n>It combines a two-state Markov chain model of primary user activity with high-fidelity propagation models from the ITU-R.<n>The framework is flexible and can be adapted to various frequency bands and scenarios.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Spectrum resources are often underutilized across time and space, motivating dynamic spectrum access strategies that allow secondary users to exploit unused frequencies. A key challenge is predicting when and where spectrum will be available (i.e., unused by primary licensed users) in order to enable proactive and interference-free access. This paper proposes a scalable framework for spectrum availability prediction that combines a two-state Markov chain model of primary user activity with high-fidelity propagation models from the ITU-R (specifically Recommendations P.528 and P.2108). The Markov chain captures temporal occupancy patterns, while the propagation models incorporate path loss and clutter effects to determine if primary signals exceed interference thresholds at secondary user locations. By integrating these components, the proposed method can predict spectrum opportunities both in time and space with improved accuracy. We develop the system model and algorithm for the approach, analyze its scalability and computational efficiency, and discuss assumptions, limitations, and potential applications. The framework is flexible and can be adapted to various frequency bands and scenarios. The results and analysis show that the proposed approach can effectively identify available spectrum with low computational cost, making it suitable for real-time spectrum management in cognitive radio networks and other dynamic spectrum sharing systems.

Related papers

• SpectrumFM: A New Paradigm for Spectrum Cognition [65.65474629224558]
  We propose a spectrum foundation model, termed SpectrumFM, which provides a new paradigm for spectrum cognition.<n>An innovative spectrum encoder that exploits the convolutional neural networks is proposed to effectively capture both fine-grained local signal structures and high-level global dependencies in the spectrum data.<n>Two novel self-supervised learning tasks, namely masked reconstruction and next-slot signal prediction, are developed for pre-training SpectrumFM, enabling the model to learn rich and transferable representations.
  arXiv  Detail & Related papers  (2025-08-02T14:40:50Z)
• SpectrumFM: A Foundation Model for Intelligent Spectrum Management [99.08036558911242]
  Existing intelligent spectrum management methods, typically based on small-scale models, suffer from notable limitations in recognition accuracy, convergence speed, and generalization.<n>This paper proposes a novel spectrum foundation model, termed SpectrumFM, establishing a new paradigm for spectrum management.<n>Experiments demonstrate that SpectrumFM achieves superior performance in terms of accuracy, robustness, adaptability, few-shot learning efficiency, and convergence speed.
  arXiv  Detail & Related papers  (2025-05-02T04:06:39Z)
• MFRS: A Multi-Frequency Reference Series Approach to Scalable and Accurate Time-Series Forecasting [51.94256702463408]
  Time series predictability is derived from periodic characteristics at different frequencies.<n>We propose a novel time series forecasting method based on multi-frequency reference series correlation analysis.<n> Experiments on major open and synthetic datasets show state-of-the-art performance.
  arXiv  Detail & Related papers  (2025-03-11T11:40:14Z)
• Deep Learning for Spectrum Prediction in Cognitive Radio Networks: State-of-the-Art, New Opportunities, and Challenges [9.499371206380546]
  This paper proposes a novel intra-bandtemporal spectrum prediction framework named ViLSTransTM.<n>The framework integrates visual self-attention and long short-term memory to capture both local and global long-term dependencies of spectrum usage patterns.
  arXiv  Detail & Related papers  (2024-12-13T04:36:05Z)
• Federated Learning for UAV-Based Spectrum Sensing: Enhancing Accuracy Through SNR-Weighted Model Aggregation [0.0]
  Unmanned aerial vehicle (UAV) networks need different points of view concerning 3D space, its challenges, and opportunities. We propose a federated learning (FL)-based method for spectrum sensing in UAV networks to account for their distributed nature and limited computational capacity. We also develop a federated aggregation method, namely FedSNR, that considers the signal-to-noise ratio observed by UAVs to acquire a global model.
  arXiv  Detail & Related papers  (2024-11-17T19:24:49Z)
• RF Challenge: The Data-Driven Radio Frequency Signal Separation Challenge [66.33067693672696]
  We address the critical problem of interference rejection in radio-frequency (RF) signals using a data-driven approach that leverages deep-learning methods.<n>A primary contribution of this paper is the introduction of the RF Challenge, which is a publicly available, diverse RF signal dataset.
  arXiv  Detail & Related papers  (2024-09-13T13:53:41Z)
• Realtime Spectrum Monitoring via Reinforcement Learning -- A Comparison Between Q-Learning and Heuristic Methods [0.0]
  Two approaches for controlling available receiver resources are compared. The Q-learning algorithm used has a significantly higher detection rate than the approach at the expense of a smaller exploration rate.
  arXiv  Detail & Related papers  (2023-07-11T19:40:02Z)
• Three-Way Deep Neural Network for Radio Frequency Map Generation and Source Localization [67.93423427193055]
  Monitoring wireless spectrum over spatial, temporal, and frequency domains will become a critical feature in beyond-5G and 6G communication technologies. In this paper, we present a Generative Adversarial Network (GAN) machine learning model to interpolate irregularly distributed measurements across the spatial domain.
  arXiv  Detail & Related papers  (2021-11-23T22:25:10Z)
• Deep Autoregressive Models with Spectral Attention [74.08846528440024]
  We propose a forecasting architecture that combines deep autoregressive models with a Spectral Attention (SA) module. By characterizing in the spectral domain the embedding of the time series as occurrences of a random process, our method can identify global trends and seasonality patterns. Two spectral attention models, global and local to the time series, integrate this information within the forecast and perform spectral filtering to remove time series's noise.
  arXiv  Detail & Related papers  (2021-07-13T11:08:47Z)
• Machine Learning Framework for Sensing and Modeling Interference in IoT Frequency Bands [2.6839965970551276]
  There is growing need for better understanding of the spectrum occupancy with newly emerging access technologies supporting the Internet of Things. We present a framework to capture and model the traffic behavior of short-time spectrum occupancy for IoT applications in the shared bands to determine the existing interference.
  arXiv  Detail & Related papers  (2021-06-10T19:10:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.