Permute Codes for High-Throughput Wireless Error Correction
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Solution Overview
Problem
Current wireless network protocols face challenges in achieving high throughput due to channel conditions such as signal attenuation, noise, and interference, which existing error-correction codes like spinal codes and LDPC struggle to efficiently address.
Innovation Solution
The introduction of permute codes, which use pseudo-randomly chosen permutations of k-bit blocks to produce symbols for transmission over noisy channels, allowing for efficient error correction and adaptation to varying channel conditions through a decoder that explores a reduced constellation space, providing feedback to the encoder.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional error-correction codes (spinal codes, LDPC) are used to address channel conditions, then error correction capability is provided, but throughput is limited and computational complexity increases
Solution Approach 1:
The patent changes the fundamental parameters of error-correction coding by using pseudo-random permutations of k-bit blocks instead of traditional code structures. This allows the system to adapt to varying channel conditions while maintaining high throughput, resolving the contradiction between reliability and productivity
Solution Approach 2:
The permute code uses dynamic pseudo-random permutations that can adapt to changing channel conditions. The decoder explores a reduced constellation space dynamically based on received symbols, allowing the system to maintain high throughput while providing robust error correction across varying channel states
2Measurement precision
If brute force approaches are used to explore all 2^k constellation points, then decoding accuracy is maximized, but computational complexity becomes prohibitive
Solution Approach 1:
The patent extracts only the most relevant constellation points for decoding by using the received symbol to identify a reduced subset of probable k-bit blocks. Instead of examining all 2^k points, the decoder focuses on a manageable subset, achieving high decoding accuracy with reduced computational complexity
Solution Approach 2:
The received symbol acts as an intermediary that guides the decoder to the relevant subset of constellation points. This intermediary information allows the system to achieve accurate decoding without the prohibitive computational cost of brute force exploration
3Productivity
If adaptive selection of coding parameters is implemented to match channel conditions, then throughput is improved, but system complexity increases
Solution Approach 1:
The permute code provides a universal framework that can adapt to various channel conditions without requiring multiple specialized code structures. The same permute code mechanism works across different SNR levels and channel states, improving throughput while avoiding the complexity of implementing multiple adaptive code sets
Data Source
AI summary
Described herein are new error-correction (channel) codes: permute codes, iterative ensembles of permute and spinal codes, and graphical hash codes. In one aspect, a wireless system includes an encoder configured to encode data using one of the aforementioned channel codes. The wireless system also includes a decoder configured to decode the encoded data.