Sample-Level Error Correction Using Known Complex Sample Functions
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Solution Overview
Problem
Current wireless communication systems face challenges in effectively correcting errors at the sample level, particularly in complex sample transmission, leading to reduced performance and increased latency due to the inability to identify and remove noise introduced during transmission.
Innovation Solution
A method where a first wireless communication device encodes data by adding time domain complex samples with a known function, such as a sum of exponentials, to the data, allowing a second device to decode and remove noise samples based on this function, using techniques like curve fitting models to correct errors in complex samples.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional error correction methods are used in wireless communication, then system complexity is reduced, but error correction capability at sample level deteriorates
Solution Approach 1:
The patent segments the error correction process into distinct functional modules: a fitting function determination module that identifies the noise function, a noise sample determination module that calculates specific noise values, and a signal reconstruction module that removes noise. This segmentation enables sample-level error correction while maintaining manageable system complexity through modular design.
Solution Approach 2:
The patent applies preliminary action by determining the fitting function of noise samples before actual error correction occurs. By pre-establishing the noise model (e.g., using sum of exponentials or curve fitting) and identifying characteristic patterns in advance, the system can rapidly correct errors during signal processing without adding significant computational overhead.
2Reliability
If noise samples are identified and removed from complex samples, then communication reliability improves, but processing time increases
Solution Approach 1:
The patent replaces traditional brute-force noise identification methods with mathematical function fitting and pattern recognition. By substituting the mechanical process of examining each sample individually with a mathematical model (sum of exponentials, curve fitting), the system achieves rapid noise identification and removal, significantly reducing processing time while maintaining high reliability.
Solution Approach 2:
The patent changes the parameter representation of noise from raw sample values to functional parameters (coefficients of exponential sums, curve fitting parameters). This transformation enables efficient computation of noise characteristics and facilitates rapid determination of noise samples for removal, thereby reducing processing time while improving reliability through more accurate noise modeling.
3Measurement precision
If curve fitting models are used to determine noise function, then noise identification accuracy improves, but computational complexity increases
Solution Approach 1:
The patent extracts the essential characteristics of noise by isolating the dominant functional components (exponential terms, polynomial components) from the complex noise signal. By taking out only the significant parameters that define noise behavior rather than processing the entire noise signal, the system achieves high identification accuracy while keeping computational complexity manageable through selective extraction of critical features.
Data Source
AI summary
A first wireless communication device may encode real samples of data to obtain encoded data based at least in part on adding one or more time domain complex samples to the real samples of the data. A function of the one or more time domain complex samples may be a known value, and the function may be a sum of exponentials of the one or more time domain complex samples. The first wireless communication device may transmit, to a second wireless communication device, the encoded data.