Uplink LLR Decoding with Resource Element Separation
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Solution Overview
Problem
In 5G wireless telecommunications networks, efficiently processing uplink transmissions is challenging due to the mixing of reference signals with data and uplink control information, requiring specialized processing to account for and separate these components effectively.
Innovation Solution
A decoding system utilizing log-likelihood ratio (LLR) optimization, including a resource element identifier to categorize uplink control information, LLR preprocessors to separate data and CSI2 streams, and a machine learning algorithm to dynamically adjust soft-demapping parameters for enhanced processing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If reference signals are mixed with data in uplink transmissions, then spectral efficiency is improved, but processing complexity increases due to the need to separate and account for reference signals during decoding
Solution Approach 1:
The patent segments the uplink transmission processing by categorizing resource elements into distinct types (data, control information, reference signals) using a resource element identifier. This segmentation allows the decoder to process different element types through specialized pathways, separating the handling of reference signals from data to reduce processing complexity while maintaining the spectral efficiency benefits of mixed transmission.
2Measurement precision
If specialized processing is implemented to separate reference signals from data, then decoding accuracy is improved, but processing time increases
Solution Approach 1:
The patent performs preliminary categorization of resource elements before the main decoding process. The resource element identifier pre-classifies each resource element as data, control information, or reference signal, and stores this categorization information for later use. This preliminary action allows the decoding stage to quickly access pre-identified data elements without re-analyzing resource element types, thereby maintaining decoding accuracy while reducing processing time.
Solution Approach 2:
The system uses the categorization information generated during initial processing to automatically guide subsequent decoding operations. The decoder retrieves pre-stored categorization data and uses it to self-direct the processing flow, identifying which resource elements require decoding and which should be skipped, thereby reducing processing time without sacrificing accuracy.
3Ease of operation
If all resource elements are processed uniformly, then processing simplicity is maintained, but efficiency decreases due to unnecessary processing of reference signal elements
Solution Approach 1:
The patent implements a dynamic processing approach where the decoding behavior adapts based on resource element type. Rather than a static uniform processing rule, the system dynamically adjusts its processing path by consulting the resource element categorization information. This allows the decoder to switch between processing modes (decode vs. skip) based on the specific resource element being encountered, maintaining operational simplicity through a unified framework while achieving high efficiency through selective processing.
Data Source
AI summary
Methods and apparatus for decoding received uplink transmissions using log-likelihood ratio optimization. In an embodiment, a method includes soft-demapping resource elements based on soft-demapping parameters as part of a process to generate log-likelihood ratios (LLR) values, decoding the LLRs to generate decoded data, and identifying a target performance value. The method also includes determining a performance metric from the decoded data, and performing a machine learning algorithm that dynamically adjusts the soft-demapping parameters to move the performance metric toward the target performance value.