Soft Decoding Metric Buffering With Compression for HARQ Receivers
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Solution Overview
Problem
Current communication receivers face challenges in efficiently processing and storing soft decoding metrics, leading to high memory and bandwidth requirements, which can result in increased costs and power consumption.
Innovation Solution
The implementation of a method that compresses soft decoding metrics using codebook-based schemes and optimizes buffer management by dividing metrics into significant and least significant portions, allowing for reduced buffer size and bandwidth usage, while maintaining performance through selective storage and retrieval strategies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If soft decoding metrics are buffered for later use in HARQ retransmission mechanism, then decoding performance is improved, but memory size and bandwidth requirements increase
Solution Approach 1:
The patent segments soft decoding metrics into two distinct parts: significant portions and least significant portions. This segmentation allows differential buffering strategies where significant portions are always buffered while least significant portions are buffered selectively or not at all, thereby reducing overall memory requirements while maintaining essential decoding performance
Solution Approach 2:
The patent applies local quality by treating different parts of the soft decoding metrics differently. Significant portions receive full buffering guarantee while least significant portions receive reduced or conditional buffering. This localized differentiation optimizes the balance between memory usage and decoding performance by allocating resources according to the actual importance of each metric portion
2Reliability
If soft decoding metrics are buffered for later use in HARQ retransmission mechanism, then decoding performance is improved, but communication bandwidth requirements increase
Solution Approach 1:
The patent extracts and separates the least significant portions of soft decoding metrics from the significant portions. By taking out the less critical data elements, the system reduces the amount of data that needs to be transmitted and buffered, thereby reducing bandwidth requirements while preserving the essential significant portions needed for reliable decoding
3Quantity of substance
If codebook-based compression is applied to soft decoding metrics, then memory size is reduced, but processing complexity increases
Solution Approach 1:
The patent applies segmentation by dividing soft decoding metrics into significant and least significant portions before compression. This pre-segmentation simplifies the compression process by allowing different compression strategies for different portions, reducing the overall processing complexity compared to compressing the entire metric set uniformly while still achieving memory reduction
Data Source
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AI summary
A method includes, in a receiver (20), computing soft decoding metrics for decoding a received signal. The soft decoding metrics are stored in compressed form in a memory buffer (40). The soft decoding metrics in the compressed form are retrieved from the memory buffer, the retrieved soft decoding metrics are decompressed, and the received signal is decoded using the decompressed soft decoding metrics.