Hybrid ARQ With Multi-Level Coding For Decoder Complexity
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Solution Overview
Problem
Current Hybrid Automatic Repeat Request (H-ARQ) methods with Multi-Level Coding (MLC) face inefficiencies due to increased computational complexity, especially when implementing uncoded bits, which limits error correction gains and decoder performance in high-throughput communication systems.
Innovation Solution
The method involves selecting coded and uncoded bits based on different noise immunity levels in a multi-level QAM symbol, where coded bits are mapped to less immune bits and uncoded bits are mapped to more immune bits, allowing for the calculation and combination of likelihood ratio metrics across multiple transmission attempts, considering the decoding results of encoded bits for uncoded bit decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If uncoded bits are used in MLC to reduce computational complexity, then decoder complexity is reduced, but error correction gains are limited
Solution Approach 1:
The patent segments the data bits into two distinct groups: coded bits and uncoded bits. Coded bits are processed through FEC encoding and multi-stage decoding to achieve error correction, while uncoded bits are transmitted directly. This segmentation allows the system to optimize error protection for critical data while reducing overall computational complexity by avoiding encoding of all bits.
Solution Approach 2:
The patent applies different quality levels of error protection to different portions of the data. Coded bits receive full FEC protection with multi-stage decoding, while uncoded bits receive no encoding. This local differentiation in protection quality allows the system to reduce computational complexity by applying complex error correction only where necessary, rather than uniformly to all data bits.
2Productivity
If high-order modulation is used to increase throughput, then data transmission rate is improved, but computational complexity of decoder increases
Solution Approach 1:
The patent segments the modulation process into multiple stages corresponding to different bit positions in the high-order constellation. Each stage decodes one bit with reduced complexity, processing bits sequentially from most significant to least significant. This segmentation of the decoding process enables the system to handle high-order modulation (e.g., 64-QAM, 256-QAM) without requiring exponentially complex decoders.
Solution Approach 2:
The patent employs a dynamic multi-stage decoding approach where the decoding process adapts to the specific modulation order and channel conditions. The system dynamically adjusts the number of stages and processing requirements based on the modulation scheme being used, allowing flexible optimization between throughput and complexity for different operational scenarios.
3Device complexity
If coded bits are mapped to less immune bits and uncoded bits to more immune bits, then computational complexity in demodulation is reduced, but error protection performance may be affected
Solution Approach 1:
The patent inverts the conventional mapping approach by assigning coded bits to less noise-immune bit positions and uncoded bits to more noise-immune positions. Conventionally, one might expect coded bits to protect more critical information, but this patent strategically places the overhead-coded bits in positions that are more susceptible to errors, thereby maximizing the efficiency of the error correction code while using robust positions for direct uncoded data transmission.
Data Source
AI summary
A method of Hybrid Automatic Repeat Request implementation which efficiently combines received signals from multiple H-ARQ block transmission attempts encoded by the Multi-Level Coding approach with an uncoded subset of information bits, is presented. The method provides full error correction gains of the H-ARQ scheme and decoder computational complexity reduction due to transmission of uncoded bits that does not cause significant demodulator and signal processing complexity growths.The advantages are achieved via calculation of likelihood ratio metrics and the combination of at least two different data block transmission attempts for both encoded and uncoded bits of a data block. Additionally, the calculation of likelihood ratio metrics for uncoded bits is performed in consideration of the results of the decoding of the encoded bits. Receiver decisions are then determined on values of uncoded bits based on values of the combined likelihood ratio metrics for uncoded bits.