Polar Code Bit Allocation with Recursive Grouping and Base Sequences
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Solution Overview
Problem
Current bit allocation techniques for encoding and decoding in wireless communications systems are resource-heavy and computationally complex, particularly when using reliability metrics for polar coding, which can lead to increased latency and inefficiency.
Innovation Solution
The proposed method involves recursively polarizing channel instances into groups of varying sizes, using a base sequence to determine information bit, frozen bit, or parity bit locations, and allocating bits based on reliability metrics, allowing for efficient encoding and decoding operations by assigning bit types to channel instances during transmission and decoding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If reliability metrics are used for bit allocation in polar coding, then coding performance is improved, but storage and computational resources increase
Solution Approach 1:
The channel instances are divided into multiple groups based on reliability metrics. Instead of treating all channel instances uniformly, the encoder segments them into groups with similar reliability characteristics, allowing for more efficient bit allocation strategies that reduce computational complexity while maintaining coding performance.
Solution Approach 2:
Different bit allocation strategies are applied to different groups of channel instances based on their local reliability characteristics. High-reliability groups receive different treatment compared to low-reliability groups, optimizing the overall system performance while reducing the need for complex global optimization.
2Productivity
If recursive polarization is performed on channel instances, then bit allocation efficiency is improved, but processing complexity increases
Solution Approach 1:
The recursive polarization process is applied in a segmented manner, where channel instances are progressively divided into groups at different levels of recursion. This hierarchical segmentation allows the system to achieve efficient bit allocation without requiring full recursive processing of all channel instances, thereby reducing processing complexity.
Solution Approach 2:
Instead of performing complete recursive polarization on all channel instances, the system applies partial polarization by stopping at a predetermined recursion level or by selecting only certain groups for further processing. This partial action maintains sufficient bit allocation efficiency while significantly reducing processing complexity.
3Speed
If base sequences are used to determine bit locations, then encoding and decoding speed is improved, but storage requirements increase
Solution Approach 1:
Base sequences are used as templates to determine bit locations in encoded and decoded data. Instead of recalculating bit locations from scratch, the system copies the structural pattern defined by the base sequence, significantly improving encoding and decoding speed. The base sequences are stored once and reused multiple times, making the storage requirement manageable.
4Productivity
If channel instances are grouped by reliability metrics, then resource allocation efficiency is improved, but system complexity increases
Solution Approach 1:
Channel instances are segmented into distinct groups based on their reliability metrics. This segmentation enables the system to allocate resources more efficiently by applying group-specific strategies rather than uniform allocation, improving overall resource allocation efficiency while keeping the grouping logic relatively simple.
Solution Approach 2:
The system changes the parameter of channel instance organization from individual treatment to grouped treatment based on reliability metrics. This parameter change enables more efficient resource allocation by allowing different allocation strategies for different groups, while the grouping itself is based on a simple metric comparison that does not require complex system architecture.
Data Source
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.