Polar Code Bit Allocation with Base-Sequence Channel Grouping
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Solution Overview
Problem
Existing bit allocation techniques for encoding and decoding in wireless communications systems, particularly for polar codes, are resource-heavy and computationally complex, requiring significant storage and processing resources, which can lead to increased latency and reduced coding efficiency.
Innovation Solution
The proposed method involves partitioning channel instances into groups based on a base sequence length, determining the number of information bits to allocate to each group using reliability metrics, and recursively polarizing vectors to optimize bit allocation, thereby reducing storage and processing demands while improving coding performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If reliability metrics are used during encoding and decoding to allocate information bits to channel instances, then coding performance is improved, but storage and computational resources are significantly increased
Solution Approach 1:
The channel instances are divided into multiple groups, with each group containing a subset of channel instances. Instead of calculating reliability metrics for all channel instances individually, the encoder calculates reliability metrics at the group level and allocates information bits to groups based on these metrics. This segmentation reduces the computational complexity and storage requirements while maintaining effective bit allocation across the channel instances.
2Reliability
If complex encoding algorithms with reliability metrics are implemented, then error correction capability is improved, but processing time and latency are increased
Solution Approach 1:
The encoder performs preliminary calculations to determine the reliability metrics for each group of channel instances before the actual encoding process. These reliability metrics are used to pre-determine the allocation of information bits to groups. By performing this allocation decision in advance, the encoder avoids complex real-time reliability calculations during the encoding process, thereby reducing processing time and latency while maintaining error correction capability.
3Productivity
If bit allocation is optimized using reliability metrics, then coding efficiency is improved, but device complexity and resource requirements are increased
Solution Approach 1:
The encoder applies different treatment to different groups of channel instances based on their local characteristics. Each group is evaluated for its reliability metrics and allocated an appropriate number of information bits according to its specific needs. This local quality approach allows the system to optimize coding efficiency for each group independently rather than applying a uniform allocation strategy, thereby improving overall coding efficiency while managing device complexity through localized optimization.
Data Source
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AI summary
Methods, systems, and devices for encoding and decoding are described. To encode a vector, an encoder allocates information bits of the vector to channel instances of a channel that are separated into groups. The groups may vary in size and allocation of the information bits is based on a base sequence of a given length. During decoding, a decoder assigns different bit types to channels instances by dividing a codeword into a plurality of groups and assigning bit types to channel instances of the plurality of groups using the base sequence.