Mu-Law Compression Approximation for Low-Logic Wireless Encoding
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Solution Overview
Problem
The existing mu-law compression algorithms are resource-intensive and require significant logic resources, making them inefficient for implementing in programmable chips and ASICs, especially in wireless communication systems like LTE, where efficient compression is necessary for reducing data dynamic range and bandwidth usage.
Innovation Solution
An approximation of the mu-law compression algorithm is implemented, which divides the compression process into segments with different slopes and shifts the input data based on value ranges, using an exponent and mantissa portion to represent the compressed data, reducing the need for complex logarithmic operations and resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If the standard mu-law compression algorithm is used, then data compression is achieved, but resource consumption and logic requirements increase significantly
Solution Approach 1:
The compression process is divided into multiple segments based on input value ranges. Each segment uses a simplified compression function with different parameters, avoiding the need for complex logarithmic operations across the entire range. This segmentation allows the system to achieve compression while reducing logic resource requirements by using simpler operations in each segment.
Solution Approach 2:
The patent changes the parameters of the compression function based on the input value range. By using piecewise linear approximations with different slopes and intercepts for different input ranges, the system maintains compression effectiveness while replacing complex mathematical operations with simpler conditional logic and arithmetic operations.
2Stability of the object's composition
If the mu-law compression algorithm is implemented, then dynamic range reduction is achieved, but processing time and computational resources increase
Solution Approach 1:
By segmenting the input range and applying simplified compression functions to each segment, the patent reduces the computational complexity of each processing step. This allows dynamic range reduction to be achieved through multiple simple operations rather than a single complex logarithmic operation, thereby reducing processing time.
Solution Approach 2:
The patent uses computationally inexpensive operations (comparisons, shifts, additions) that can be executed quickly, replacing expensive logarithmic operations. These simple operations consume minimal processing time and resources, enabling efficient real-time compression.
3Measurement precision
If complex logarithmic operations are used for mu-law compression, then accurate compression is achieved, but hardware resource requirements increase
Solution Approach 1:
The patent uses piecewise linear functions with carefully selected parameters (slopes and intercepts) to approximate the logarithmic mu-law compression curve. By optimizing these parameters for each segment, the system maintains compression accuracy while implementing the function using simple arithmetic operations instead of complex logarithmic calculations.
Solution Approach 2:
The patent creates simplified copies of the compression function for different input ranges, where each copy uses a linear approximation tailored to its specific range. This allows the system to maintain accuracy within each segment while using resource-efficient linear operations instead of expensive logarithmic operations.
Data Source
AI summary
Techniques and mechanisms provide a technique for compression using an approximation of a mu-law algorithm.