5G NR LDPC Decoding Using Sparsity-Adaptive Hybrid Operations
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Solution Overview
Problem
Existing wireless communication signal decoding processes are resource-intensive and time-consuming, requiring significant computing resources and latency.
Innovation Solution
A hybrid approach for LDPC decoding using a row-layered min-sum algorithm with box-plus and compressed C2V operations based on the sparsity of data, selecting between these methods based on the degree of base graph rows to optimize performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If traditional decoding methods are used, then decoding accuracy is maintained, but processing time and computing resource consumption increase significantly
Solution Approach 1:
The patent applies dynamics by making the decoding method adaptive rather than static. The system dynamically selects between box-plus operation and compressed C2V operation based on real-time assessment of data sparsity characteristics. This dynamic adaptation allows the decoder to optimize its computational approach for each specific data condition, reducing overall latency while maintaining decoding accuracy.
Solution Approach 2:
The patent changes the operational parameters of the decoding process by introducing a hybrid approach that switches between two different decoding operations. The key parameter being changed is the selection criterion based on row degree thresholds. When row degree is below the threshold, box-plus operation is used; when above, compressed C2V operation is used. This parameter-based selection optimizes the balance between speed and resource consumption.
2Productivity
If a single decoding method is used for all data types, then implementation is simple, but performance is suboptimal across varying data sparsity conditions
Solution Approach 1:
The patent applies local quality by applying different decoding operations to different parts of the decoding process based on local data characteristics. Specifically, it examines the row degree of the base graph and applies box-plus operation to rows below the threshold while applying compressed C2V operation to rows above the threshold. This localized approach optimizes throughput for each segment according to its specific sparsity characteristics.
Solution Approach 2:
The patent segments the decoding process into two distinct operational modes based on row degree thresholds. The base graph rows are divided into two groups: those with row degree less than or equal to the threshold (using box-plus) and those with row degree greater than the threshold (using compressed C2V). This segmentation allows each segment to be processed with the most appropriate algorithm, improving overall throughput while managing complexity through structured division.
Data Source
AI summary
Apparatuses, systems, and techniques to decode encoded data for fifth-generation (5G) new radio (NR). In at least one embodiment, a processor includes one or more circuits to select one or more data decoding operations to decode one or more 5G signals based, at least in part, on a sparsity of data received by the processor.