Data Subset Selection for Autocorrelation-Resistant LMS Convergence
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Solution Overview
Problem
Data-pattern autocorrelations impede the convergence rate and introduce steady-state errors in digital adaptive methods, particularly the LMS method, leading to incorrect solutions and increased convergence time in digital communication systems.
Innovation Solution
The Data Subset Selection (DSS) method involves selecting a subset of the digital stream with reduced or eliminated autocorrelations by separating n-bit spaced subsets, using detector blocks and a logic gate to enable the adaptation engine only when predetermined conditions are met, thereby reducing the impact of autocorrelations on adaptation engines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the adaptation engine processes the complete digital stream, then all available data is utilized for adaptation, but data-pattern autocorrelations increase convergence time and introduce steady-state errors
Solution Approach 1:
The patent extracts and removes autocorrelated data patterns from the digital stream by identifying repeating sequences and excluding them from adaptation processing. This extraction of harmful autocorrelated components allows the adaptation engine to process only independent, informative data samples, thereby reducing convergence time while maintaining solution accuracy.
Solution Approach 2:
The patent applies different processing quality to different portions of the digital stream: autocorrelated segments are identified and excluded, while independent segments are processed with full adaptation. This local differentiation in data quality treatment ensures that only beneficial data contributes to adaptation, improving both convergence speed and steady-state accuracy.
2Measurement precision
If the adaptation engine processes the complete digital stream, then comprehensive data utilization is achieved, but autocorrelations cause convergence to incorrect solutions
Solution Approach 1:
The patent introduces an intermediary autocorrelation detection and removal mechanism between the digital stream and the adaptation engine. This intermediary component identifies and filters out autocorrelated patterns before data reaches the adaptation engine, preventing convergence to incorrect solutions while maintaining relatively simple adaptation logic.
3Productivity
If data-pattern autocorrelations are reduced by selecting subsets, then convergence speed and accuracy improve, but additional processing complexity is introduced
Solution Approach 1:
The patent implements a self-service approach where the autocorrelation detection and subset selection process automatically identifies and removes harmful patterns without requiring complex external control. The system uses inherent properties of the data stream (repeating patterns) to trigger automatic exclusion, achieving subset selection with minimal additional processing complexity.
Data Source
AI summary
A method and apparatus are provided for reducing, and preferably substantially eliminating, data-pattern autocorrelations found in digital communication systems. The method employed is referred to as Data Subset Selection (DSS) and is implemented in the form of DSS engine. Autocorrelations in the data-pattern can cause many digital adaptive systems to converge to an incorrect solution. For example, the LMS method, which is often used in adaptive filtering applications, can converge to an incorrect set of filter coefficients in the presence of data-pattern autocorrelations. Digital timing recovery methods are also susceptible. Other impairments that result from data-pattern autocorrelations include increased convergence time and increased steady-state chatter. DSS reduces, and preferably substantially eliminates, data-pattern autocorrelations by selecting a subset of the data stream that either demonstrates smaller autocorrelations or no autocorrelations, thus improving the performance of the aforementioned digital adaptive systems.