Wireless Terminal Resource Allocation for Channel State Information Estimation

The system pre-allocates additional transmission resources containing known padding symbols before actual data transmission. These pre-prepared resources enable the access point to perform accurate channel state information estimation by comparing received signals against the known padding symbols, thereby improving measurement precision without requiring additional real-time resources during critical transmission phases.

The patent applies partial action by allocating transmission resources that exceed the minimum required for data transmission alone. The additional resources are dedicated specifically to channel estimation purposes, providing enough excess capacity to achieve accurate CSI measurement while avoiding the waste of allocating far more resources than necessary. This balanced approach optimizes the trade-off between measurement accuracy and resource consumption.

The system implements self-service by having the access point automatically perform machine learning analysis on collected CSI samples without requiring external processing assistance. The access point utilizes its own computational resources to train and apply machine learning models, enabling it to autonomously improve sensing accuracy while managing its own computational complexity through efficient algorithm selection and optimization.

The system performs preliminary collection and preprocessing of CSI samples before applying machine learning analysis. By accumulating sufficient training data and preparing it in advance, the system enables the machine learning model to achieve high sensing accuracy while distributing the computational load over time, thereby managing computational complexity more effectively rather than requiring all processing to occur simultaneously.