Multi-Level Code Decoding With Auxiliary LLR Post-Processing
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Solution Overview
Problem
Current 5G NR communication systems face challenges in efficiently decoding multi-level coded signals due to high complexity and resource-intensive demodulation processes, particularly in multi-stage decoding scenarios.
Innovation Solution
The proposed solution involves a method where a network entity receives a communication encoded with multi-level coding, generates estimates of log-likelihood ratios (LLRs) for each coding level, and uses auxiliary information based on exclusive disjunction (XOR) of bits to improve decoding efficiency, reducing the complexity of demodulation and decoding processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multi-level coded signals are decoded using conventional demodulation processes, then decoding accuracy is maintained, but computational complexity and resource consumption increase significantly
Solution Approach 1:
The patent segments the multi-level coded signal into multiple coding levels (e.g., first coding level, second coding level, third coding level), each associated with different bit sets. The decoding process is divided into separate stages where each level is decoded independently using its own LLR estimates, rather than processing all bits simultaneously. This segmentation reduces the computational complexity at each stage while maintaining overall decoding accuracy.
Solution Approach 2:
The patent performs preliminary actions by generating auxiliary information (such as LLR estimates for parity bits or check bits) before the main decoding process. These preliminary LLR estimates are used to simplify subsequent decoding operations. For example, LLR values for check bits are computed in advance based on received signals and used to reduce the complexity of parity verification during decoding.
2Reliability
If multi-stage decoding is performed with complete LLR estimation for all bits, then decoding reliability is improved, but processing time and resource costs increase
Solution Approach 1:
The patent applies local quality by generating different quality levels of LLR estimates for different bit positions and coding levels. Critical bits (such as information bits) receive more accurate LLR estimates, while less critical bits (such as parity bits) use simplified estimates. This differentiated approach maintains decoding reliability for essential data while reducing overall processing time and resource consumption.
Solution Approach 2:
The patent implements partial action by computing LLR estimates for only a subset of bits at each decoding stage rather than all bits. For example, in the first decoding stage, LLR estimates are generated only for bits at the first coding level, while bits at other levels are processed in subsequent stages. This partial processing approach reduces processing time while maintaining sufficient decoding reliability through multiple stages.
Data Source
AI summary
Apparatus, methods, and computer program products for decoding are provided. An example method may include receiving a communication from a second network entity via a channel, where the communication is encoded based on multi-level coding associated with at least a first coding level and a second coding level, and where the first coding level is associated with a first set of bits and the second coding level is associated with a second set of bits. The example method may further include means for generating a first estimate of a first LLR associated with the first coding level, a second estimate of a second LLR associated with the second coding level, and auxiliary information including a third estimate of a third LLR based on the first set of bits and the second set of bits.