Shared Correlation Matrix for Wireless Interference Suppression
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Solution Overview
Problem
Conventional wireless communication systems face high computational complexity and cost due to the need for individual correlation matrix estimation and inversion for each user in interference suppression, especially in CDMA communications, which worsens with the number of users.
Innovation Solution
The solution involves sharing statistical characteristics of interference among users, using a shared inverse correlation matrix or shared correlation matrix, and modifying channel estimates based on user-specific finger delays to reduce computational complexity, thereby simplifying the process of interference suppression.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If individual correlation matrix estimation and inversion is performed for each user, then interference suppression performance is improved, but computational complexity increases
Solution Approach 1:
The patent merges the correlation matrix estimation process across multiple users by computing a single shared correlation matrix from chip samples of all users instead of estimating separate correlation matrices for each user. This combining approach maintains interference suppression effectiveness while significantly reducing computational complexity, as the shared matrix captures the common interference characteristics experienced by all users in the CDMA system.
Solution Approach 2:
The shared correlation matrix serves as a universal interference model for all users in the system. Rather than requiring user-specific correlation matrices, this single matrix structure provides the necessary interference suppression information for multiple users simultaneously, making the computation universally applicable across different user channels and reducing overall system complexity.
2Quantity of substance
If the number of users increases, then system capacity is improved, but computational complexity worsens
Solution Approach 1:
The patent combines the correlation matrix computation for all users into a single unified process. By aggregating chip samples from all users and computing one shared correlation matrix, the system avoids the exponential growth in computational complexity that would result from estimating separate matrices for each user, thereby enabling the system to accommodate more users without proportionally increasing computational burden.
Data Source
AI summary
The computational complexity required for interference suppression in the reception of wireless communications from multiple users is reduced by sharing information among the users. In some situations, information indicative of a statistical characteristic of the interference is shared among the users. Delays used to produce the interference statistic information are determined based on rake finger delays employed by the users. In some situations, a parameter estimate that is used to calculate combining weights for the users is shared among the users.