QAM Co-Set Mapping With Strongly Coded Identifiers
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Solution Overview
Problem
Conventional systems combining binary error correcting codes and multi-level signal sets, such as QAM, face increased complexity and coding loss as spectral efficiency grows, with noise leading to multiple bit errors that are not always correctable, especially in larger constellations.
Innovation Solution
The method involves splitting data streams for differential encoding and constellation mapping, using turbo encoding for one stream and weaker encoding for another, with constellation shaping to create co-sets and partitions, allowing for improved error correction by mapping to a subset of constellation points with larger minimum distances within co-sets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If conventional QAM constellation mapping with Gray coding is used, then implementation simplicity is maintained, but coding gain drops significantly as spectral efficiency increases
Solution Approach 1:
The patent segments the QAM constellation into multiple co-sets, where each co-set contains constellation points with specific distance properties. This segmentation allows different encoding strategies to be applied to different parts of the constellation, resolving the contradiction by maintaining simple Gray mapping within co-sets while using coded modulation to select among co-sets, thereby preserving both implementation simplicity and coding gain.
Solution Approach 2:
The patent applies different mapping qualities to different regions of the constellation. Specifically, Gray mapping is applied locally within each co-set to maintain simplicity, while the selection of co-sets is controlled by coded modulation to maximize coding gain. This local differentiation resolves the contradiction by allowing simplicity where needed and performance where critical.
2Productivity
If larger QAM constellations are used to increase spectral efficiency, then data transmission capacity increases, but noise-induced multiple bit errors become more frequent and harder to correct
Solution Approach 1:
By segmenting the large QAM constellation into smaller co-sets, the patent ensures that even when using large constellations for high spectral efficiency, the actual decision space for error-prone bits is limited to smaller subsets. This segmentation maintains error correction capability because fewer bits need to be reliably distinguished within each co-set, while the overall system achieves high spectral efficiency through the combination of multiple co-sets.
Solution Approach 2:
The patent introduces co-set identifiers as an intermediary layer between the coded data and the final constellation mapping. This intermediary structure allows the system to achieve high spectral efficiency by utilizing the full large constellation, while error correction is maintained by first decoding the co-set identifiers (which have stronger error protection) and then mapping to the appropriate co-set, reducing the impact of noise on the final bit decisions.
3Reliability
If strong turbo encoding is applied to all data streams, then error correction performance improves, but system complexity increases due to additional interleaving and decoding stages
Solution Approach 1:
The patent segments the data stream into co-set identifier bits and co-set selection bits, applying different encoding strengths to each segment. Strong turbo encoding is applied only to the co-set identifier portion which requires higher reliability, while the co-set selection bits use simpler mapping. This segmentation maintains error correction performance for the critical portions while reducing overall system complexity by avoiding strong encoding throughout the entire stream.
Solution Approach 2:
Different encoding qualities are applied locally to different parts of the data stream based on their importance. The co-set identifier bits, which are more critical for error correction, receive strong turbo encoding, while the remaining bits use simpler mapping schemes. This local differentiation maintains overall error correction performance while significantly reducing the complexity compared to uniform strong encoding of all bits.
Data Source
AI summary
A method of encoding a stream of data elements is provided which involves splitting the stream of data elements into a first stream and a second stream; encoding the first stream to produce a first encoded stream; performing a constellation mapping using a combination of the first encoded stream and a third stream which is based an the second stream. This may involve defining a signal constellation; defining a plurality of co-sets within the constellation such that a minimum distance between constellation points within each co-set is larger than a minimum distance between any constellation points within the signal constellation; performing said constellation mapping by using the first encoded stream to identify a sequence of co-sets of said plurality of co-sets, and by using the third stream to identify a sequence of constellation points within respective co-sets of the sequence of co-sets identified by said first encoded stream.