Pruned Polar Factor Graphs for Flexible High-Throughput Decoding
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Solution Overview
Problem
The decoding complexity of polar codes increases with block-length, limiting their adoption for high-throughput applications like 5G NR eMBB data rates, and existing LDPC-like BP decoding methods require separate pruning processes for each code rate and size, making them impractical for flexible code configurations.
Innovation Solution
A method involving the generation of a pruned factor graph and corresponding parity-check matrix for polar encoding and decoding, which minimizes connections and allows flexible code rate and length adaptations, enabling efficient decoding with reduced latency and memory requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If polar codes are used for high-throughput applications, then data rate capability is improved, but decoding complexity increases with block-length
Solution Approach 1:
The polar code block is segmented into multiple sub-blocks that can be decoded independently or semi-independently. This segmentation reduces the effective block-length for decoding operations, thereby reducing decoding complexity while maintaining the ability to handle high data rates through parallel processing of segments
Solution Approach 2:
The decoding structure is made dynamic by allowing flexible configuration of the factor graph based on code rate and block-length parameters. The pruning process dynamically removes unnecessary nodes and edges from the factor graph, adapting the decoding complexity to match the actual requirements of the transmission, thus reducing overall complexity while maintaining high throughput capability
2Productivity
If LDPC-like BP decoding methods are used, then decoding efficiency is improved, but separate pruning processes are required for each code rate and size
Solution Approach 1:
A universal pruning framework is developed that can handle multiple code rates and block lengths using a single unified process. The method defines general rules for identifying and removing redundant nodes and edges that apply across different code configurations, eliminating the need for separate pruning processes for each code rate and size while maintaining decoding efficiency
3Reliability
If standard polar decoding is implemented, then code reliability is maintained, but latency and memory requirements increase
Solution Approach 1:
Redundant nodes and edges are extracted from the polar code factor graph through systematic pruning. This removal of unnecessary computational elements reduces the number of operations required for decoding, thereby reducing latency and memory requirements while preserving the essential error-correcting functionality that ensures code reliability
Data Source
AI summary
A method performed by a WTRU may comprise generating a polar factor graph and pruning the polar factor graph to generate a pruned factor graph. The pruned factor graph may include input variable nodes, check nodes and output variable nodes. The method may further comprise the initializing input variable nodes. For each of a plurality of encoding levels of the pruned factor graph, values from the input variable nodes may be transferred to the check nodes. Operations, for example, XOR) additions, may be performed on the values of the check nodes. Check nodes having a single connection to another node not used in a previous transfer may be identified. Values from the identified check nodes may be transferred to the input variable nodes. Binary values from the input variable nodes may be transferred to the output variable nodes for transmission to a receiver.