Multi-Dimensional Data Permutation for Low-Complexity Error Decorrelation
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Solution Overview
Problem
Conventional error decorrelators face increased complexity and memory requirements with highly correlated noise, particularly in high-speed communication channels, as they need to permute and store larger amounts of data, leading to inefficiencies in gate count and memory usage.
Innovation Solution
The approach involves splitting data permutation into multiple operations across different dimensions, using time-varying permutation elements and block interleavers/de-interleavers to achieve effective data permutation with smaller blocks, reducing overall complexity and gate count compared to classical designs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional error decorrelators use larger memory depth to handle highly correlated noise, then error decorrelation performance improves, but device complexity and gate count increase linearly with data throughput and proportionally to the square of memory depth
Solution Approach 1:
The patent divides the large permutation operation into multiple smaller permutation operations applied sequentially to different segments of the data stream. Instead of permuting all data at once with a single large permutation matrix, the data is processed in blocks through multiple smaller permutation matrices, reducing the computational complexity from O(N²) to O(k·m²) where N is total data size, k is number of blocks, and m is block size.
Solution Approach 2:
The patent introduces time as an additional dimension by applying permutations at different time instances or stages. The permutation operation is extended from a single spatial dimension to multiple dimensions including time, allowing the system to achieve equivalent error decorrelation with smaller per-stage memory requirements while processing data throughput efficiently.
2Reliability
If conventional error decorrelators increase memory depth for high-speed channels, then error decorrelation effectiveness improves, but memory size and complexity become prohibitively large
Solution Approach 1:
The patent segments the large memory requirement into multiple smaller memory units that process data in sequential stages. Each stage uses a small permutation matrix operating on a block of data, eliminating the need for a single large memory structure while achieving the same overall permutation effect through cumulative transformation across stages.
Solution Approach 2:
The patent applies preliminary permutation operations to data blocks before they are fully assembled or before final processing. By performing permutation on smaller blocks in advance through multiple stages, the system prepares the data for error decorrelation without requiring all data to be stored simultaneously in large memory.
3Productivity
If conventional error decorrelators process data at high throughput rates, then communication channel capacity increases, but complexity increases linearly with throughput making implementation impractical
Solution Approach 1:
The patent segments the high-throughput data stream into parallel processing channels, each handling a portion of the data with a small permutation matrix. This allows the system to achieve high aggregate throughput while each individual processing unit maintains low complexity, avoiding the linear complexity increase that would result from processing the entire high-rate stream sequentially with a single large permutation operation.
Data Source
AI summary
Multiple data permutation operations in respective different dimensions are used to provide an overall effective data permutation using smaller blocks of data in each permutation than would be used in directly implementing the overall permutation in a single permutation operation. Data that has been permuted in one permutation operation is block interleaved, and the interleaved data is then permuted in a subsequent permutation operation. A matrix transpose is one example of block interleaving that could be applied between permutation operations.