Dynamic Pilot Pattern Selection for Channel Estimation

The patent applies dynamics by transitioning from static predefined pilot patterns to dynamic pilot pattern selection. The system adapts the pilot pattern based on real-time channel conditions (SNR, delay spread, Doppler spread) using deep learning models, allowing the pilot configuration to change dynamically according to the actual wireless channel state, thereby resolving the contradiction between simplicity and accuracy.

The patent changes the parameters of pilot pattern selection by using deep learning models (Autoencoders and Regression networks) to determine optimal pilot positions and densities based on channel parameters such as SNR, delay spread, and Doppler spread. This parameter change enables the system to achieve high estimation accuracy without requiring a fixed complex set of predefined patterns.

The patent applies local quality by determining optimal pilot densities and positions specific to local channel conditions. Instead of uniformly increasing pilot density across the entire spectrum, the system uses deep learning models to identify local regions and frequency bands where pilots are most needed, based on channel characteristics like delay spread and Doppler spread, thereby improving accuracy without proportionally increasing overall overhead.

The patent changes the pilot density parameter dynamically based on channel conditions. Using Regression networks and Autoencoders, the system calculates the optimal number and distribution of pilots required for accurate channel estimation under specific SNR, delay spread, and Doppler spread conditions, avoiding unnecessary pilots in low-complexity scenarios while ensuring sufficient coverage in high-complexity scenarios.

The patent transitions from static uniform pilot spacing to dynamic non-uniform spacing. The system uses deep learning models to calculate optimal pilot positions based on real-time channel conditions, allowing the pilot distribution to adapt dynamically to the actual channel state, thereby achieving accurate channel representation in low SNR conditions while maintaining operational simplicity through automated selection.

The patent replaces the mechanical/simple approach of uniform pilot spacing with an intelligent system using deep learning models (Autoencoders and Regression networks). This substitution allows the system to automatically determine optimal pilot configurations based on channel characteristics, achieving superior channel representation accuracy without requiring complex manual configuration, thus resolving the contradiction between ease of operation and measurement precision.