Spectrum Availability Window

The lookahead window for which a predictive model forecasts channel occupancy. This horizon directly dictates the feasibility of proactive transmission scheduling.

• Short horizon (milliseconds): Suitable for high-frequency trading or ultra-reliable low-latency communications.
• Long horizon (seconds to minutes): Enables complex multi-packet transactions and energy-efficient sleep cycles.
• The prediction accuracy typically degrades as the horizon extends due to the stochastic nature of primary user traffic.

A robust prediction is not a single point estimate but a probabilistic distribution over the idle duration. Gaussian Process Regression and Bayesian models output a confidence interval that quantifies prediction uncertainty.

• A 95% confidence interval indicates the range within which the true idle time is expected to fall.
• Secondary users can use this interval to make risk-aware decisions, such as transmitting only when the lower bound of the interval exceeds the required burst length.
• This prevents over-reliance on an overly optimistic single-point forecast.

The window's length is derived from a Primary User Activity Model, often represented by an ON/OFF process. The distribution of idle times is rarely exponential in practice.

• Phase-Type Distributions generalize the exponential to model complex channel holding times.
• Heavy-tailed distributions (e.g., Pareto) are common in real-world traffic, meaning very long idle windows are statistically more likely than an exponential model would predict.
• Accurate modeling of the tail distribution is critical for avoiding unexpected collisions.

Instead of predicting a single idle duration, advanced models like Encoder-Decoder LSTMs forecast a sequence of future channel states (idle/busy) for multiple consecutive time steps.

• This creates a predicted occupancy map over a future window.
• A secondary user can identify contiguous blocks of predicted idle states to schedule a transmission burst.
• This approach captures the dynamic transitions between states, not just the static duration, allowing for more granular spectrum access planning.

The statistical properties of a channel's occupancy are non-stationary; primary user traffic patterns evolve over time. A static model will suffer from prediction degradation.

• Online learning mechanisms continuously update the prediction model as new spectrum observations arrive.
• Change Point Detection algorithms identify abrupt shifts in the underlying traffic pattern, triggering a model reset or rapid re-training.
• This ensures the predicted availability window remains calibrated to the current, not historical, electromagnetic environment.

A channel's availability is often correlated with adjacent channels due to wideband primary user transmissions or coordinated network behavior. Graph Neural Networks (GNNs) capture these dependencies.

• A GNN models the spectrum as a graph where nodes are channels and edges represent correlation.
• By learning node embeddings, the model predicts the availability window for a target channel using information from its neighbors.
• This exploits the structured nature of the spectrum to improve prediction accuracy beyond what a single-channel time-series model can achieve.