LLR Decoding with Constellation Segmentation in 5G Receivers
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Solution Overview
Problem
Conventional methods for computing log-likelihood ratios (LLRs) in 5G communication systems face challenges in balancing computational complexity with error performance, as soft decoding methods require complex operations that increase computational expense while approximations often compromise error performance.
Innovation Solution
The method involves using a centroid approach to compute LLRs by exploiting symmetry in the constellation of codewords and defining an uncertainty region, allowing for either hard decoding, soft decoding, or a combination of both based on the presence of symbols within this region, thereby reducing computational complexity without compromising error performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If soft decoding is used to compute LLR, then error performance is improved, but computational complexity increases significantly
Solution Approach 1:
The patent segments the constellation into multiple regions (e.g., inner region, outer region, uncertainty region) and applies different decoding strategies to different segments. Symbols in the inner region use hard decoding for low complexity, while symbols in the outer or uncertain regions use soft decoding for better error performance. This segmentation allows the system to achieve good overall error performance without applying complex soft decoding to all symbols.
Solution Approach 2:
The patent applies different quality levels of decoding (hard vs. soft) to different local regions of the constellation based on their specific characteristics. Rather than using a uniform decoding approach, the system tailors the decoding method to the local properties of each constellation region, using soft decoding only where necessary to maintain error performance while minimizing overall computational complexity.
2Device complexity
If hard decoding is used to compute LLR, then computational complexity is reduced, but error performance degrades
Solution Approach 1:
The patent divides the constellation into multiple regions and applies hard decoding only to specific segments (e.g., inner region symbols) where it is sufficient, while using soft decoding for other segments (e.g., outer or uncertain region symbols) where better error performance is needed. This selective application maintains low complexity for most operations while improving error performance where necessary.
Solution Approach 2:
The patent applies soft decoding partially - not to all symbols, but only to those in specific regions where it provides necessary error performance improvement. This partial application of the more complex decoding method allows the system to achieve adequate overall error performance without the full computational cost of universal soft decoding.
3Device complexity
If approximations are used to reduce complexity in soft decoding, then computational complexity is reduced, but error performance is compromised
Solution Approach 1:
The patent segments the constellation into regions where approximations are acceptable and regions where exact soft decoding is applied. By identifying specific regions (e.g., inner region) where hard decoding or simplified methods suffice, the system can use approximations selectively without significantly compromising overall error performance, thus reducing complexity where possible while maintaining reliability where necessary.
Solution Approach 2:
The patent applies different levels of decoding quality to different local regions of the constellation. Rather than using a uniform approximation across all symbols, the system tailors the decoding approach to local characteristics, applying more accurate methods in regions where they are needed and simpler approximations where they are sufficient, thereby optimizing the trade-off between complexity and error performance.
Data Source
AI summary
The present disclosure relates to a pre-5th-Generation (5G) or 5G communication system to be provided for supporting higher data rates Beyond 4th-Generation (4G) communication system such as long term evolution (LTE). Methods and systems for optimizing computation of log-likelihood ratio (LLR) for decoding modulated symbols. A method disclosed herein involves receiving at least one symbol transmitted from at least one device, wherein the received at least one symbol is encoded and modulated symbol including a plurality of data bits. The method further includes computing a log-likelihood ratio (LLR) of each bit in the received at least one symbol for decoding the received at least one symbol using a centroid method that involves exploiting a symmetry of a constellation of code words and/or a uncertainty region defined on a constellation of code words.