Multi-Dimensional Data Permutation for Error Decorrelation
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Solution Overview
Problem
Conventional error decorrelators face increased complexity and memory requirements due to high correlation in noise samples, particularly in high-speed communication channels, which affects the performance of Forward-Error-Correction (FEC) codes designed for Additive White Gaussian Noise (AWGN) conditions.
Innovation Solution
The complexity of error decorrelators is reduced by splitting data permutation into multiple operations across different dimensions, using time-varying permutation elements and block interleavers/de-interleavers, which allows for effective data permutation with smaller blocks, thereby reducing gate count and overall complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional error decorrelators are used to handle highly correlated noise, then error decorrelation performance is improved, but device complexity and memory requirements increase linearly with throughput and proportionally to the square of memory depth
Solution Approach 1:
The patent segments the data stream into multiple parallel lanes, with each lane processed by a separate permutation element. This divides the single complex permutation operation into multiple simpler parallel operations, reducing the complexity of each individual element while maintaining overall decorrelation effectiveness.
Solution Approach 2:
The patent employs time-varying permutation elements that dynamically change their permutation patterns over time rather than using static permutation. This dynamic approach allows the system to adapt to different noise correlation conditions and reduces the memory depth required compared to conventional static permutation methods.
2Productivity
If data throughput is increased to meet high-speed communication requirements (40 Gbps and above), then productivity is improved, but complexity of the error decorrelator increases linearly
Solution Approach 1:
By dividing the high-speed data stream into multiple parallel lanes, each processed at a lower individual rate, the patent achieves high overall throughput without requiring a single complex high-speed permutation element. Each permutation element operates at reduced complexity while the parallel structure maintains high aggregate throughput.
Solution Approach 2:
The patent combines multiple parallel permutation operations to achieve the equivalent effect of a single complex permutation at high throughput. The merged output from multiple simpler permutation elements produces the same decorrelation effect as a single complex element would at lower throughput, avoiding the linear complexity increase.
3Reliability
If memory depth is increased to handle higher degrees of noise correlation, then error decorrelation performance is improved, but complexity increases proportionally to the square of memory depth
Solution Approach 1:
The time-varying permutation elements reduce the required memory depth by dynamically adapting permutation patterns rather than relying on deep static memory structures. This dynamic approach achieves effective decorrelation with shallower memory, avoiding the quadratic complexity increase associated with increased memory depth.
Solution Approach 2:
The patent introduces time as an additional dimension to the permutation operation, transforming the problem from a spatial memory-intensive approach to a time-varying computational approach. This dimensional change allows decorrelation to be achieved through temporal variation rather than spatial expansion, reducing memory depth requirements.
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.