Differential Vectoring Coefficient Encoding for Lower Memory Use
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Solution Overview
Problem
Current compression methods for vectoring coefficients in Vectoring Processors are inefficient, requiring significant memory storage and failing to adequately reduce the noise from Far-End Crosstalk (FEXT) in high-frequency communication systems, which limits data transmission rates and stability.
Innovation Solution
A novel compression scheme that differentially encodes vectoring coefficients using variable-length codes, adjusting the number of bits required for each coefficient based on the minimum required for encoding, ensuring that successive elements differ by a threshold value, thereby reducing storage needs while maintaining fidelity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If fixed-length encoding is used for vectoring coefficients, then decoding is simpler and faster, but memory storage requirements increase significantly
Solution Approach 1:
The patent applies dynamic encoding by using variable-length codes instead of fixed-length codes. The number of bits used to encode each vectoring coefficient varies based on the magnitude of the coefficient, allowing smaller coefficients to use fewer bits while larger coefficients use more bits. This dynamic adaptation resolves the contradiction by optimizing the balance between storage efficiency and decoding complexity.
Solution Approach 2:
The patent changes the encoding parameter from fixed bit-length to variable bit-length based on coefficient magnitude. By introducing a threshold-based encoding scheme where coefficients below a threshold use one bit and coefficients above the threshold use multiple bits, the system achieves both reduced storage requirements and manageable decoding complexity.
2Measurement precision
If more bits are used for encoding each coefficient, then encoding precision is higher, but memory storage requirements increase
Solution Approach 1:
The patent applies local quality by using different encoding precision for different coefficients based on their magnitude. Small coefficients (below threshold) are encoded with 1 bit, while large coefficients (above threshold) are encoded with multiple bits. This local adaptation of precision resolves the contradiction by allocating encoding resources efficiently - high precision only where necessary - thereby reducing overall storage requirements while maintaining adequate precision for significant coefficients.
3Quantity of substance
If variable-length encoding is used to reduce storage, then memory requirements decrease, but decoding complexity increases
Solution Approach 1:
The patent segments the coefficient values into two distinct groups based on a threshold: small coefficients (≤ threshold) and large coefficients (> threshold). Each segment is encoded with a specific bit-length (1 bit for small, multiple bits for large). This segmentation simplifies decoding complexity by providing clear rules for interpreting each encoded value, resolving the contradiction between storage reduction and decoding complexity.
4Quantity of substance
If compression is applied to vectoring coefficients, then memory storage is reduced, but data transmission fidelity may deteriorate
Solution Approach 1:
The patent uses parameter changes by adjusting the number of bits allocated to encode each coefficient based on its magnitude. This adaptive encoding maintains fidelity for significant coefficients (those above the threshold) by using more bits, while accepting coarser representation for less significant coefficients (those below the threshold). This resolves the contradiction by preserving data transmission fidelity where it matters most while achieving overall compression.
Data Source
AI summary
In accordance with an embodiment, the method includes determining a second sequence of numbers of digits for encoding the respective integer coefficient values of the first sequence, the second sequence including, as first element, a first number of digits for encoding the first integer coefficient value of the first sequence, and as second and subsequent elements, constrained numbers of digits that are greater than or equal to respective minimum required numbers of digits for encoding the second and subsequent integer coefficient values of the first sequence. The constrained numbers of digits are such that any two successive elements of the second sequence do not differ from each other by more than a given threshold value. The method further includes encoding difference values between the successive elements of the second sequence; and encoding the integer coefficient values of the first sequence using the respective numbers of digits of the second sequence.