LLR Compression for 5G Fronthaul Bandwidth and Hardware Limits
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Solution Overview
Problem
Existing 5G wireless communications networks face challenges in managing high bandwidth requirements and the number of wireline connections between radio units (RUs) and distributed units (DUs) due to conventional LLR compression methods, particularly in the fronthaul network, which are inefficient and costly.
Innovation Solution
A hardware-friendly LLR compression technique that simplifies processing by using real value samples, handles zero-quantized values, and employs variable RBG sizes and MCS-specific compression parameters to optimize data transfer rates and reduce eCPRI traffic, while allowing for flexible compression parameterization based on link information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If conventional LLR compression methods are used in 5G fronthaul networks, then data transfer capacity is maintained, but the number of wireline connections and bandwidth requirements increase, leading to higher cost and complexity
Solution Approach 1:
The patent applies parameter changes by transforming LLR values through exponentiation (y = exp(x)) to compress the dynamic range of values. This mathematical transformation changes the parameter distribution, allowing efficient quantization and compression while maintaining the essential information needed for signal reconstruction at the receiver end.
Solution Approach 2:
The patent implements local quality by applying different quantization strategies to different parts of the LLR value distribution. Specifically, it uses separate handling for positive and negative values, and applies different bit allocation schemes based on the statistical characteristics of LLR values in different signal conditions, thereby optimizing compression efficiency locally.
2Quantity of substance
If compressed frequency domain I/Q samples are transmitted to reduce data rate, then bandwidth is reduced, but hardware complexity and processing requirements increase
Solution Approach 1:
The patent extracts only the essential information from the full LLR data by identifying and transmitting only the most significant bits after exponential transformation. This extraction process removes redundant information while preserving the critical signal characteristics needed for accurate reconstruction, thereby reducing bandwidth without proportionally increasing hardware complexity.
Solution Approach 2:
The patent performs preliminary exponential transformation and quantization at the transmitter side before transmission. This preliminary action prepares the data in an optimized format that is easier to compress and transmit efficiently, reducing the burden on the transmission infrastructure and making the system more hardware-friendly.
3Productivity
If LLR values are compressed to reduce eCPRI traffic, then network efficiency improves, but measurement precision of signal quality may deteriorate
Solution Approach 1:
The patent implements feedback mechanisms where the receiver uses the compressed LLR values to reconstruct signals and generate feedback information about signal quality and decoding performance. This feedback loop allows the system to monitor and maintain signal quality accuracy even with compressed data, ensuring that network efficiency improvements do not compromise measurement precision.
Solution Approach 2:
The patent applies partial compression by retaining the most significant bits of the LLR values after exponential transformation, using an excessive number of bits in critical regions and fewer bits in less critical regions. This selective retention strategy maintains sufficient precision for accurate signal quality measurement while achieving overall compression for improved network efficiency.
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AI summary
At least one example embodiment provides a radio access network element comprising at least one processor and at least one memory. The at least one memory stores instructions that, when executed by the at least one processor, cause the radio access network element to: extract sign information from a plurality of log-likelihood ratio (LLR) sample values included in LLR data in a resource block group (RBG); convert negative LLR sample values, from among the plurality of LLR sample values, into positive integer values to obtain converted LLR data; generate compressed LLR data based on the converted LLR data; and output the compressed LLR data.