Time-Varying Data Permutation for Low-Complexity Error Decorrelation
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Solution Overview
Problem
Conventional error decorrelators face increased complexity and memory requirements with 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 permutations and block interleaving 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 memory size increase significantly
Solution Approach 1:
The patent divides the data stream into multiple segments or blocks, applying permutation operations to each segment independently or in smaller groups. This segmentation allows the system to achieve effective error decorrelation with smaller memory depth compared to processing the entire data stream as one large block, directly reducing the memory size and complexity while maintaining decorrelation performance.
Solution Approach 2:
The patent introduces time-varying permutation patterns that change over time, adding a temporal dimension to the permutation operation. Instead of using a single static permutation pattern that would require large memory to store all possible permutations, the system uses multiple smaller permutation patterns that are applied sequentially, reducing memory requirements while maintaining effective error decorrelation.
2Productivity
If conventional error decorrelators increase data throughput for high-speed communication, then communication speed improves, but complexity increases linearly with throughput
Solution Approach 1:
The patent processes data in smaller blocks or segments that can be handled in parallel or pipelined fashion. This allows the system to achieve high overall throughput by processing multiple smaller units simultaneously, rather than requiring a single complex processing unit that scales linearly with throughput requirements.
Solution Approach 2:
The patent employs time-varying permutation patterns that adapt to the data flow rate. The permutation parameters are dynamically adjusted based on the throughput requirements, allowing the system to maintain optimal complexity-performance tradeoff across different operating conditions and throughput levels.
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.