Radio Node Power Space Distribution Detection

The patent segments the detection process into two distinct phases: (1) constructing a power space distribution model using statistical parameters from a large number of resource blocks during system setup, and (2) using this pre-built model for rapid detection with minimal resource blocks during actual operation. This segmentation allows the computationally intensive modeling to be performed once, while subsequent detections require minimal processing resources.

The patent performs preliminary action by constructing the power space distribution model in advance using historical data from multiple resource blocks. The model includes pre-calculated statistical parameters (mean, covariance matrix) that capture the characteristics of different uplink/downlink states. When actual detection is needed, this pre-computed model is directly applied, eliminating the need to process large numbers of resource blocks in real-time.

The system performs preliminary action by building a comprehensive power space distribution model during system initialization or idle periods, using data from many resource blocks to ensure statistical reliability. The model stores pre-computed characteristics of different node types and transmission states. During actual operation, this pre-built model enables rapid and reliable node type identification using minimal resource blocks, thus maintaining high reliability while improving operational efficiency.

The patent creates a simplified copy of the complex power space distribution characteristics in the form of a statistical model. Instead of directly analyzing raw signal data from multiple resource blocks during operation, the system uses the pre-computed model that captures the essential characteristics. This copying approach maintains identification reliability while dramatically reducing processing time and resource requirements during actual detection.

The patent performs the accuracy-critical computation in advance by constructing the power space distribution model using a large number of resource blocks during system setup or idle periods. The model captures the statistical characteristics (mean, covariance matrix) of power space distributions for different scenarios. During actual operation with limited resource blocks, this pre-computed model provides the accuracy that would otherwise require extensive real-time data collection, thus enabling both high operational efficiency and maintained accuracy.

The system creates a simplified statistical representation (copy) of the complex power space distribution that can be rapidly queried during operation. This copied model contains pre-computed statistical parameters that approximate the results of analyzing large numbers of resource blocks. By using this copied representation instead of direct analysis during operation, the system achieves both speed (using few resource blocks) and accuracy (matching results from extensive analysis).