Nonlinear LLR Quantization for Soft-Decision Decoding Compression
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The challenge in telecommunications networks is the degradation of data signal strength due to noise and distance, which requires more storage and bandwidth for soft information, making conventional methods inefficient for accurate signal decoding.
Innovation Solution
A method and apparatus that compress soft information using a compression mechanism during the decoding process, allowing for efficient storage and transmission by converting soft information into compressed bits, which are then decompressed for error correction and integrity verification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If soft information is used to improve signal decoding accuracy, then decoding precision is improved, but storage space and bandwidth requirements increase
Solution Approach 1:
The patent applies parameter changes by transforming the soft information from its original linear scale to a logarithmic scale (LLR - Log Likelihood Ratio). This parameter transformation compresses the dynamic range of soft information values, allowing accurate representation of signal reliability with fewer bits. The logarithmic transformation maintains the essential decoding accuracy while reducing the storage and transmission requirements.
Solution Approach 2:
The patent extracts only the essential information needed for accurate decoding by quantizing the soft information to a limited number of bits. Instead of storing or transmitting the full-precision soft information, the system extracts the most significant bits that carry the critical reliability information, discarding less important precision details. This extraction approach maintains decoding accuracy while significantly reducing resource requirements.
2Quantity of substance
If conventional hard information decoding is used, then storage and bandwidth requirements are reduced, but signal decoding accuracy deteriorates
Solution Approach 1:
The patent transforms the decoding approach by changing from hard decision (binary 0/1) to soft decision using LLR values. This parameter change allows the decoder to consider the reliability or confidence level of each received bit, improving decoding accuracy. The logarithmic transformation of soft information enables this soft decision process while keeping the data representation compact.
Solution Approach 2:
The patent introduces LLR (Log Likelihood Ratio) as an intermediary representation between the received hard bits and the final decoded data. Instead of directly decoding hard information or transmitting full-precision soft information, the LLR values serve as a compact intermediary that captures the essential reliability information needed for accurate soft decision decoding, bridging the gap between hard and soft information approaches.
3Measurement precision
If full-precision soft information is transmitted and stored, then decoding accuracy is maintained, but system complexity and resource consumption increase
Solution Approach 1:
The patent extracts only the necessary precision level for accurate decoding by quantizing soft information to a limited number of bits (e.g., 4-8 bits instead of full floating-point precision). This extraction of essential information maintains decoding accuracy while dramatically simplifying storage and processing requirements. The system removes unnecessary precision that does not contribute to actual decoding performance.
Solution Approach 2:
The patent changes the precision parameter of soft information from high-precision floating-point representation to fixed-point or reduced-bit representation after logarithmic transformation. This parameter change in precision level reduces memory requirements and processing complexity while maintaining sufficient accuracy for practical decoding applications. The system adapts the precision to match the actual needs of the decoding process.
Data Source
AI summary
A process capable of employing compression and decompression mechanism to receive and decode soft information is disclosed. Upon receiving a set of signals representing a logic value from a transmitter via a physical communication channel, the set of signals is demodulated in accordance with a soft decoding scheme and subsequently, a Log Likelihood Ratio (“LLR”) value representing the logic value is generated. After generating a quantized LLR value in response to the LLR value via a non-linear LLR quantizer, the quantized LLR value representing the compressed logic value is stored in a local storage.