Turbo-Like Coding Structure for Flexible Code Rates and Low BER
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Solution Overview
Problem
Turbo-like codes face limitations in flexibility, performance, and complexity, particularly in achieving high data throughput and adaptability across varying code rates, frame sizes, and modulation schemes, often requiring increased complexity or degraded performance.
Innovation Solution
The proposed solution involves an encoding scheme that uses an outer convolutional code, an interleaver, a single parity check module, and a puncture module to produce encoded outputs, allowing for configurable encoding rates and flexible operation across different data frame sizes and modulation schemes, while maintaining desirable performance metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional FEC codes (block codes or convolutional codes) are used, then the system structure is simple, but the coding gain is limited and cannot achieve performance close to the Shannon limit
Solution Approach 1:
The Turbo-like code decoder is segmented into multiple independent component decoders (first component decoder, second component decoder, third component decoder) that process different portions of the coded data in parallel. Each component decoder handles a specific aspect of the decoding task, allowing the system to achieve high coding gain through iterative processing while maintaining manageable complexity in each individual decoder unit.
2Reliability
If Turbo-like codes with high coding gain are used, then the error correction performance is significantly improved, but the decoder complexity increases substantially
Solution Approach 1:
The decoder is divided into three specialized component decoders that operate in parallel and iteratively exchange information. This segmentation allows each component to be relatively simple while the collective iterative process achieves high coding gain and low bit error rates, resolving the contradiction between performance and complexity.
Solution Approach 2:
The component decoders engage in iterative feedback processing where each decoder uses soft information from the others to improve its decoding decisions. This feedback mechanism enables the system to achieve high reliability through multiple passes of decoding, with each iteration refining the bit error rate performance without requiring any single decoder to be overly complex.
3Reliability
If Turbo-like codes are designed for specific code rates and frame sizes, then the performance in the waterfall region is optimized, but the adaptability to varying channel conditions is reduced
Solution Approach 1:
The encoder and decoder are designed with configurable parameters that allow them to function across multiple code rates and frame sizes. The systematic Turbo-like code structure with its three component decoders can be adapted to different channel conditions and performance requirements, making the system universal rather than specialized for a single configuration.
Solution Approach 2:
The code rate and frame size parameters are made dynamic rather than fixed, allowing the system to adapt to varying channel conditions. The encoder can adjust the code rate and the decoder can accommodate different frame sizes while maintaining the iterative decoding structure that provides optimized waterfall region performance across multiple operating points.
4Productivity
If the code rate is increased to improve data throughput, then the productivity is improved, but the error floor performance deteriorates
Solution Approach 1:
By segmenting the decoding into three component decoders that process different aspects of the coded data, the system can maintain robust error correction even at higher code rates. Each component decoder contributes to correcting errors in different portions of the data, allowing the system to achieve both high throughput and acceptable error floor performance.
Solution Approach 2:
The iterative feedback mechanism among the three component decoders allows the system to compensate for the reduced redundancy at higher code rates. Through multiple iterations of information exchange, the decoders can recover errors that would otherwise be undetectable, maintaining error floor performance while enabling higher data throughput.
Data Source
AI summary
Methods, apparatuses, and systems are presented for performing data encoding involving encoding data bits according to an outer convolutional code to produce outer encoded bits, processing the outer encoded bits using an interleaver and a single parity check (SPC) module to produce intermediate bits, encoding the intermediate bits according to an inner convolutional code to produce inner encoded bits, processing the inner encoded bits using a puncture module to produce punctured bits, and combining the data bits and the punctured bits to produce encoded outputs. Methods, apparatuses, and systems are also presented for performing data decoding based on soft channel metrics derived from a channel using various iterative techniques.