Fronthaul IQ Compression Using Adaptive Constellation Scaling
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Solution Overview
Problem
Existing communication technologies in computing systems face challenges in efficiently compressing IQ data for fronthaul interfaces without causing distortion, particularly in ORAN architectures, which often result in higher compression ratios leading to signal degradation.
Innovation Solution
A method involving determining a maximum absolute value and constellation measure for IQ data, followed by compressing and decompressing the data using these parameters to maintain signal integrity, achieving a lossless compression similar to 6-bit block floating point performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If higher compression ratios are used for IQ data in fronthaul interfaces, then network resource efficiency is improved, but signal quality deteriorates due to distortion
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting the compression precision based on the signal characteristics. Specifically, it changes the number of bits used to represent IQ data samples according to the signal's dynamic range and constellation properties, allowing higher compression ratios for low-signal regions while maintaining full precision for high-signal regions, thus resolving the contradiction between compression efficiency and signal quality
Solution Approach 2:
The patent implements dynamics by making the compression ratio adaptive rather than fixed. The compression algorithm dynamically adjusts its parameters based on real-time signal conditions, including the maximum absolute value of IQ samples and constellation measure, enabling the system to optimize between compression efficiency and signal fidelity for each signal segment
2Quantity of substance
If compression is applied to IQ data, then bandwidth consumption is reduced, but manufacturing precision of signal representation is worsened
Solution Approach 1:
The patent changes the precision parameter (number of bits) based on signal characteristics. It uses a variable precision approach where the bit depth is adjusted according to the maximum absolute value and constellation measure of the IQ data, achieving lower bandwidth consumption while maintaining sufficient representation accuracy for each signal condition
Solution Approach 2:
The patent applies local quality by using different compression precisions for different parts of the signal. Instead of applying uniform compression, it adjusts the compression ratio locally based on the signal's instantaneous properties, ensuring high representation accuracy where needed and higher compression where acceptable
3Reliability
If lossless compression is implemented, then signal integrity is maintained, but device complexity increases
Solution Approach 1:
The patent manages complexity by changing parameters in a systematic and predictable manner. It uses well-defined parameters (maximum absolute value, constellation measure) to control compression behavior, making the algorithm implementable with moderate complexity while maintaining signal integrity through parameter-driven adaptation
Data Source
AI summary
A system includes one or more data processors. The system further includes a non-transitory computer-readable storage medium containing instructions which, when executed on the one or more data processors, cause the one or more data processors to perform operations. The operations include receiving a stream of in-phase components and quadrature phase components as IQ data. The IQ data indicates which subcarriers each of the IQ data is to be sent. The operations further include determining a maximum absolute value associated with the IQ data, determining a constellation measure of a largest bounding box covering all subcarriers of the IQ data, and compressing the IQ data to obtain compressed IQ data based on the maximum absolute value and the constellation measure.