Base Spreading Code Selection via Weighted Metric Evaluation
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Solution Overview
Problem
In wireless communication systems, determining an optimized base spreading code is challenging due to the large number of possible codes for a given protocol, affecting communication efficiency.
Innovation Solution
A method iteratively determines metrics for each code map formed based on base spreading codes, computes a weighted sum of these metrics, and selects an optimal base spreading code to enhance communication efficiency, which includes permuting codes, calculating minimum and average distances, auto-correlation, and side lobe metrics, and configuring wireless devices with the selected code.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a large number of base spreading codes are considered for optimization, then communication efficiency can be improved, but the complexity of determining the optimal code increases significantly
Solution Approach 1:
The patent segments the code determination process into multiple independent metrics (minimum distance, average distance, auto-correlation side lobe, longest run of bits) that can be calculated and evaluated separately. Each metric assesses a specific property of the code map, allowing the complex optimization problem to be broken down into manageable components that can be independently analyzed and combined.
Solution Approach 2:
The patent transforms the code optimization problem from a direct search approach into a parameter-based evaluation system. By defining specific parameters (metrics) that characterize code quality and computing weighted sums of these parameters, the system can efficiently compare and rank different base spreading codes without exhaustively analyzing all possible code properties.
2Reliability
If multiple metrics are computed for each code map to ensure optimization quality, then the reliability of code selection improves, but the computational time and resources increase
Solution Approach 1:
The patent applies partial action by selecting and computing only the most critical metrics needed for code evaluation rather than analyzing all possible code properties. The four key metrics (minimum distance, average distance, auto-correlation side lobe, and longest run of bits) represent a carefully chosen subset of code properties that sufficiently characterize code quality for wireless communication, avoiding unnecessary computational overhead while maintaining selection reliability.
Solution Approach 2:
The patent implements a feedback mechanism through the weighted sum computation, where multiple metric values are aggregated and used to rank and select the optimal base spreading code. This feedback loop allows the system to continuously evaluate code maps against established criteria and adjust selections based on cumulative metric performance, ensuring reliable code selection while managing computational resources efficiently.
Data Source
AI summary
In one aspect, a method includes: iteratively, for each of a plurality of code maps each formed based on one of a plurality of base spreading codes: determining, in a computing system, a plurality of metrics for the code map; and computing, in the computing system, a weighted sum for the code map based on at least some of the plurality of metrics. After this iterative operation, a first base spreading code associated with the weighted sum having an optimal value may be selected. This first base spreading code may be used to configure one or more wireless devices with the first base spreading code to cause the one or more wireless devices to communicate coded symbols using the first base spreading code.