HARQ LLR Compression Control Under Memory and Signal Quality Constraints
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Solution Overview
Problem
The compression of log likelihood ratio (LLR) signals in Hybrid Automatic Repeat reQuest (HARQ) systems for wireless communication devices leads to performance deterioration due to inefficient memory usage, as existing methods do not adequately consider the quality of received signals and available memory sizes when determining compression levels.
Innovation Solution
A device and method that dynamically adjust the compression level of LLR signals based on the quality of received signals and available memory sizes, using a combiner to generate composite signals, a compression level decision unit to calculate optimal compression levels, and a compressor to store and decompress signals efficiently in HARQ memory.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Volume of stationary object
If the LLR signal is compressed to reduce memory size, then the HARQ memory size is reduced, but the reception performance deteriorates
Solution Approach 1:
The patent applies dynamics by making the compression level adjustable and adaptive rather than fixed. The compression level decision unit dynamically selects the compression level based on current channel conditions and signal quality, allowing the system to optimize between memory efficiency and reception performance in real-time different conditions.
Solution Approach 2:
The patent changes the parameter of compression level from a fixed value to a variable that can be adjusted based on signal quality and channel conditions. By modifying this parameter dynamically, the system can achieve better reception performance when needed while still maintaining memory size reduction when conditions allow.
2Device complexity
If a fixed compression level is used to simplify the system, then the device complexity is reduced, but the memory usage efficiency decreases
Solution Approach 1:
The patent implements feedback by having the compression level decision unit continuously monitor signal quality metrics and channel conditions, then adjust the compression level accordingly. This feedback mechanism enables the system to automatically optimize memory usage efficiency without requiring complex manual configuration or control.
Solution Approach 2:
The system performs self-service by autonomously determining the appropriate compression level based on its own monitoring of signal quality and memory status. The compression level decision unit independently makes decisions without external intervention, simplifying the overall control architecture while maintaining high memory usage efficiency.
Data Source
AI summary
A device for optimizing a data compression level when processing a Hybrid Automatic Repeat reQuest (HARQ) signal includes a combiner which receives a log likelihood ratio (LLR) signal, determines whether the LLR signal is a new or retransmitted signal, and generates a composite signal by combining the LLR signal with a related signal received and previously stored when the LLR signal is the retransmitted signal; a compression level decision unit which calculates a first compression level based on quality of a received signal, calculates a second compression level based on an available memory size, and decides a final compression level according to the first compression level and the second compression level; a compressor which compresses the LLR signal according to the final compression level; a HARQ memory which stores the compressed signal; and a decompressor which decompresses a signal read from the HARQ memory.