Fronthaul IQ Compression Using Dual-Quantized LPC Prediction
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Solution Overview
Problem
Current base station systems face challenges with high bit-rate requirements over the fronthaul link, leading to capacity bottlenecks, particularly due to the high computational complexity and error accumulation in existing open-loop and closed-loop Linear Predictive Coding (LPC) methods, which are not efficient for signals with colored spectra and have high latency issues.
Innovation Solution
The implementation of an open-loop LPC method with an additional quantizer at the encoder input and a corresponding quantizer at the decoder, allowing for error-free signal transmission by quantizing IQ samples twice, reducing noise shaping and error accumulation, and using modular arithmetic to manage prediction errors, thereby maintaining signal quality and reducing computational complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If open-loop LPC method is used for data compression, then device complexity is reduced, but error accumulation and noise shaping increase
Solution Approach 1:
The patent introduces a feedback mechanism where the quantized IQ samples from the open-loop LPC encoder are fed back into the predictor. This allows the system to maintain the simplicity of open-loop LPC while reducing error accumulation by using the quantized values as reference for subsequent predictions, thereby improving reliability without significantly increasing complexity.
Solution Approach 2:
The patent applies parameter changes by introducing an additional quantizer at the encoder input and modifying the prediction error calculation to use quantized values. This changes the operational parameters of the LPC system, allowing it to achieve better error performance while maintaining low computational complexity through efficient quantization schemes.
2Reliability
If additional quantizer is added at encoder input, then signal quality is improved by reducing error accumulation, but device complexity increases
Solution Approach 1:
The patent merges the additional quantizer at the encoder input with the existing LPC encoding structure. By integrating the quantization operation into the existing signal flow and using the quantized values directly in the prediction error calculation, the system improves signal quality while minimizing the increase in device complexity through efficient resource sharing.
3Reliability
If quantizing IQ samples twice is performed, then noise shaping and error accumulation are reduced, but processing time increases
Solution Approach 1:
The patent applies preliminary action by performing the first quantization at the encoder input before the prediction process. This preliminary quantization prepares the data in advance, allowing the subsequent processing to work with already-quantized values and thereby reducing the overall processing time despite the additional quantization step.
4Productivity
If fronthaul link capacity is increased to meet high bit-rate requirements, then transmission capacity is improved, but cost increases dramatically
Solution Approach 1:
The patent applies parameter changes by implementing efficient quantization and prediction techniques that reduce the bit-rate requirement for fronthaul transmission. By changing the parameters of the signal representation through LPC coding, the system achieves acceptable signal quality at lower transmission rates, thereby improving productivity while reducing the energy loss associated with high-capacity transport infrastructure.
Data Source
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AI summary
Disclosed is a method performed by an encoder of a base station system (100), for handling a data stream for transmission over a transmission connection (165) between a remote unit (160) and a base unit (170) of the base station system, the remote unit being arranged to transmit wireless signals to, and receive from, mobile stations (180). The method comprises quantizing a plurality of IQ samples, converting the quantized plurality of IQ samples to IQ predictions, calculating per sample a difference between the quantized plurality of IQ samples and the IQ predictions in order to create IQ prediction errors. The method further comprises quantizing the IQ predictions or the IQ prediction errors, entropy encoding the IQ prediction errors and sending the entropy encoded IQ prediction errors over the transmission connection (165) to a decoder of the base station system. Disclosed is also a corresponding method performed by a decoder.