Iterative Frequency Offset Estimation for Low-Complexity PSS Detection

The patent segments the frequency offset estimation process into multiple iterations, where each iteration tests a subset of frequency hypotheses rather than all hypotheses simultaneously. This divides the complex testing process into manageable stages, reducing the computational burden in each individual iteration while maintaining overall estimation accuracy through cumulative refinement across iterations.

The patent employs periodic action by structuring the hypothesis testing as repeated iterations, where each iteration周期ically tests a refined set of frequency hypotheses. The testing process is organized into discrete cycles that progressively narrow down the frequency offset estimate, allowing the system to achieve high accuracy through multiple passes rather than a single exhaustive test, thereby optimizing the trade-off between accuracy and processing time.

The patent applies dynamics by making the set of frequency hypotheses adaptive across iterations. The frequency hypotheses are dynamically adjusted and refined in each iteration based on results from previous iterations, allowing the testing process to focus computational resources on the most promising frequency candidates. This dynamic refinement reduces the effective search space in later iterations, lowering computational complexity while maintaining estimation accuracy.