Bootstrap Signal Decoding via Iterative Channel Gain Correction

The patent implements an iterative feedback mechanism where the decoder initially estimates channel gain, decodes the bootstrap signal, extracts signaling information, re-estimates channel gain using the extracted information, and repeats this process. Each iteration refines the channel gain estimation by incorporating newly decoded signaling information, creating a closed-loop feedback system that progressively improves estimation accuracy and decoding reliability

The patent performs preliminary channel gain estimation before full decoding, uses this initial estimate to decode the bootstrap signal and extract signaling information, then uses this extracted information to refine the channel gain estimation. This preliminary action sequence enables the system to progressively improve estimation accuracy through multiple passes, where each pass builds upon the previous results

The patent creates a feedback loop where maximum-likelihood decoding is performed using initial channel gain estimates, the decoded signaling information is extracted, and this information is used to refine the channel gain estimates. The refined estimates are then fed back into the maximum-likelihood decoder for improved performance, allowing the system to achieve accurate decoding without requiring perfectly accurate initial channel gain estimates

The patent makes the channel gain estimation dynamic and adaptive rather than static. The estimation process evolves through multiple iterations, with each iteration updating the channel gain values based on newly decoded signaling information. This dynamic approach allows the system to adapt to the actual channel conditions revealed through decoding, improving both reliability and measurement precision