Spectrum Occupancy Database

Organizes measurements across four critical dimensions to enable rapid lookups and complex queries:

• Frequency: Indexed by center frequency and bandwidth, often aligned with channel raster definitions.
• Time: Timestamped with high precision, supporting both real-time streams and historical time-series analysis.
• Space: Geotagged with latitude, longitude, and altitude, using geohashing or tile-based spatial indexing for efficient area queries.
• Power: Stores received signal strength indicator (RSSI) or power spectral density (PSD) values as the primary occupancy metric. This structure allows a cognitive radio to query, for example, 'average noise floor on Channel 25 within a 500m radius over the last 10 minutes.'

Transforms raw power measurements into actionable probability distributions for predictive algorithms. Key statistical outputs include:

• Duty Cycle: The fraction of time a channel is occupied above a defined energy detection threshold.
• Channel Holding Time: The statistical distribution of transmission durations, critical for predicting when a frequency hole will close.
• Inter-Arrival Time: The distribution of silent periods between transmissions, used to estimate the maximum safe secondary transmission length. These models allow a cognitive radio to select a channel not just because it is currently idle, but because its statistical profile predicts a long, stable availability window.

Bridges the gap between sparse physical sensor deployments and the need for a continuous occupancy map. The database uses RF propagation models to interpolate between measurement points:

• Terrain-Integrated Rough Earth Model (TIREM): Accounts for terrain diffraction and ground conductivity to estimate signal strength at unmeasured locations.
• Longley-Rice Irregular Terrain Model (ITM): Used for predicting median signal strength over irregular terrain profiles.
• Ray Tracing: High-fidelity urban propagation modeling using 3D building data to predict shadowing and multipath effects. This interpolation enables the database to provide occupancy estimates for any geographic coordinate, not just those directly measured by a sensor.

Ingests and reconciles heterogeneous data streams from a network of spatially distributed sensors to overcome the hidden node problem. The fusion engine must handle:

• Sensor Heterogeneity: Integrating data from high-end spectrum analyzers, embedded software-defined radios (SDRs), and low-cost IoT energy detectors, each with different noise figures and calibration accuracy.
• Temporal Alignment: Synchronizing measurements from sensors with unsynchronized clocks using Network Time Protocol (NTP) offsets or Precision Time Protocol (PTP).
• Conflict Resolution: Applying weighted voting or Bayesian inference when multiple sensors report conflicting occupancy states for the same channel, weighting sensors by their historical reliability and proximity to the query point.

Leverages historical occupancy patterns to forecast future spectrum availability, moving beyond reactive sensing. Common forecasting architectures include:

• Long Short-Term Memory (LSTM) Networks: Deep learning models trained on long sequences of occupancy data to capture complex temporal dependencies and periodicities.
• Autoregressive Integrated Moving Average (ARIMA): A classical statistical model effective for channels with stable, predictable usage patterns.
• Prophet: A decomposable time-series model robust to missing data and shifts in trend, useful for modeling weekly human-driven usage cycles. The output is a predicted occupancy probability for a future time window, enabling proactive spectrum mobility before a primary user even appears.

The application of time-series forecasting models to historical data stored within the Spectrum Occupancy Database. By analyzing past usage patterns, these models predict future spectrum holes, enabling proactive rather than reactive dynamic access. Common techniques include:

• Recurrent Neural Networks (RNNs)
• Long Short-Term Memory (LSTM) networks
• Gaussian Process Regression

The real-time measurement process that populates the Spectrum Occupancy Database. Cognitive radios perform sensing to detect primary user signals and identify spectrum holes. Key sensing techniques include:

• Energy detection
• Matched filter detection
• Cyclostationary feature detection The database aggregates these distributed observations to overcome the hidden node problem.

Unsupervised learning models that continuously monitor the Spectrum Occupancy Database to identify statistical deviations from normal spectral activity. These anomalies may indicate:

• Unauthorized transmitters
• Primary User Emulation (PUE) attacks
• Equipment malfunctions Detection triggers alerts for spectrum enforcement agencies and initiates automated mitigation responses.