Polar Code Encoding With Nested Aggregation for Faster Decoding
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current polar code algorithms face challenges in constructing data blocks at specific aggregation levels to match desired rates, leading to inefficiencies in encoding and decoding processes.
Innovation Solution
The method involves encoding data blocks at an aggregation level of 2L using the formula [u.2Lu.L][GLN0GLNGLN]=⌊C.2LC.L⌋, where {dot over (u)}L and {dot over (u)}2L are nested data blocks, allowing for early decoding termination and reduced decoding delay by leveraging frozen and check frozen bits.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If data blocks are encoded at higher aggregation levels using nested structures, then decoding delay is reduced through early termination, but device complexity increases due to the nested encoding framework
Solution Approach 1:
The patent applies nesting by constructing data blocks at higher aggregation levels using nested structures where {u.2L} contains {u.L} as a subset. This nested encoding framework allows the decoder to terminate early when successfully decoding at a lower aggregation level, reducing decoding delay while managing complexity through structured organization.
Solution Approach 2:
The patent implements preliminary action by pre-constructing nested data block structures with frozen and check frozen bits at multiple aggregation levels before transmission. This allows the receiver to attempt decoding at lower aggregation levels first, and if successful, terminate early without processing higher levels, thus reducing decoding delay.
2Reliability
If data blocks are encoded at higher aggregation levels with nested structures, then encoding gain is improved through increased code length, but productivity decreases due to increased computational overhead
Solution Approach 1:
The nested structure allows incremental encoding where data blocks at higher aggregation levels build upon lower levels. This enables the system to achieve full encoding gain through increased code length while optimizing productivity by only performing additional encoding computations when lower-level decoding fails.
Solution Approach 2:
The patent applies local quality by using frozen bits and check frozen bits at specific positions within the nested structure. These specially designed bit positions provide local redundancy and verification capabilities, improving encoding gain without requiring uniform increases in computational overhead across the entire data block.
3Reliability
If nested data block structures are used with frozen bits, then information bit recovery reliability is improved, but device complexity increases due to additional bit management
Solution Approach 1:
The nested structure organizes frozen bits and information bits in a hierarchical manner where {u.2L} contains both information bits and frozen bits from {u.L}. This structured organization improves information bit recovery reliability by providing multiple decoding opportunities while managing bit complexity through clear hierarchical relationships.
Solution Approach 2:
The patent segments the data block into distinct components: information bits, frozen bits, and check frozen bits at different aggregation levels. This segmentation allows the decoder to process and verify different bit types independently, improving recovery reliability while reducing management complexity through modular processing.
Data Source
AI summary
Embodiments of the application provides a method for encoding. The method includes: receiving a to-be-encoded data block; encoding the data block at an aggregation level of 2L, where a formula used during the encoding is as follows:[u.2Lu.L][GLN0GLNGLN]=⌊C.2LC.L⌋(I)u.L={uLuL-1…u1},u.2L={u2Lu2L-1…uL+1},c.L={cLcL-1…c1},c.2L={c2Lc2L-1…cL+1},GLN=GN⊗log2(L)(II)L=2n, and n is a natural number greater than or equal to 0; and outputting an encoded data block.