Polar Code Bit Allocation for Reduced ML Decoding Search Space
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Solution Overview
Problem
Polar codes suffer from poor minimum distance and high decoding latency for lower code sizes, particularly in next-generation wireless systems like 5G, due to the complexity and latency associated with maximum likelihood (ML) search spaces in decoding trees.
Innovation Solution
The proposed solution involves strategically distributing redundant outer-code bits within the polar decoding tree to reduce the search space size of internal nodes, allowing for more efficient ML decoding by maximizing the degree and quantity of ML nodes, thereby reducing decoding latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If maximum likelihood decoding is applied to polar codes, then decoding accuracy is improved, but decoding latency increases due to large search space size
Solution Approach 1:
The patent segments the polar decoding tree into multiple levels and identifies specific internal nodes where ML decoding should be applied. By selectively applying ML decoding only at certain segmented nodes rather than throughout the entire tree, the search space is divided into manageable portions that maintain accuracy while reducing overall latency.
Solution Approach 2:
The patent applies different decoding strategies to different parts of the decoding tree. ML decoding with reduced search space is applied locally at specific internal nodes where it provides the most benefit, while other nodes use standard decoding methods. This local optimization maintains overall decoding accuracy while reducing total search space complexity.
2Loss of time
If the search space size of internal nodes in polar decoding tree is reduced, then decoding latency is reduced, but decoding accuracy may deteriorate
Solution Approach 1:
The patent performs preliminary analysis to identify which internal nodes in the decoding tree benefit most from ML decoding with reduced search space. By pre-determining the optimal set of nodes before decoding operations, the system can reduce search space at critical nodes without compromising overall accuracy, as the most important nodes are protected with adequate search space.
Solution Approach 2:
The patent changes the parameter of search space size dynamically based on the node's position and importance in the decoding tree. Different internal nodes are assigned different search space sizes according to their significance, allowing the system to reduce total search space while maintaining adequate search capacity at nodes where accuracy is most critical.
3Productivity
If polar codes are used in next-generation wireless systems, then spectral efficiency is improved, but minimum distance performance deteriorates for lower code sizes
Solution Approach 1:
The patent combines polar codes with outer codes (such as CRC or parity-check codes) to create a composite coding scheme. The polar inner code provides spectral efficiency while the outer code contributes excellent minimum distance properties. This composite structure allows the system to achieve both high spectral efficiency and reliable minimum distance performance, even for lower code sizes.
Data Source
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AI summary
Certain aspects of the present disclosure generally relate to techniques for encoding, generally including obtaining a payload, determining a set of internal nodes to distribute one or more non-payload bits to based, at least in part, on a target maximum likelihood (ML) search space size for internal nodes in a polar decoding tree, a search space size of each of the internal nodes, and an available number of the non-payload bits left to distribute, forming an information stream by interleaving the non-payload bits with bits of the payload by, for each internal node in the set of internal nodes, assigning one or more non-payload bits to one or more leaf nodes in a subtree rooted at that internal node in the set of internal nodes, and generating a codeword by encoding the information stream using a Polar code.