A Log-Likelihood Ratio Uniform Quantization Method Based on Minimizing the l1 Norm of Quantization Error

Abstract

A log-likelihood ratio uniform quantization method based on minimization of an L1 norm of a quantization error. The method comprise the following steps: first, calculating an original log-likelihood ratio of each sample symbol according to an LLR formula; second, calculating a quantization result corresponding to the quantization interval and obtaining an L1 norm of the quantization error of the original log-likelihood ratio; third, finding out a quantization interval minimizing the L1 norm of the quantization error by using a golden section method so as to realize uniform quantization. According to the invention, under a condition that the probability distribution of the log-likelihood ratio cannot be accurately known, a problem that an LLR distribution depending traditional method has noway to start quantization is solved; the LLR quantization error is calculated by using the symbol samples only and the uniform quantization can be carried out. Dependence on LLR probability distribution can be removed. In addition, the quantization error of log-likelihood ratio is calculated by adopting the L1 norm, which accords with the logarithmic characteristic of the log-likelihood ratio, and the performance is improved distinctively.

Description

technical field [0001] The invention belongs to the field of coded modulation communication, and specifically relates to a logarithmic likelihood ratio uniform quantization method based on minimizing the quantization error L1 norm, which can minimize the computational complexity and the system performance loss caused by the quantization error. Background technique [0002] In wireless digital communication systems, coding and modulation technology is one of the core technologies to ensure communication reliability. The introduction of log-likelihood ratio (LLR) brings great convenience to soft-decision decoding algorithm. However, the directly calculated LLR is generally a floating-point number, which will complicate circuit design when using hardware circuit decoding. Therefore, proper quantization of the LLRs prior to decoding is essential. How to adopt an appropriate method for quantization to minimize the system performance loss caused by quantization errors has a great...

AI Technical Summary

Problems solved by technology

However, the L2 norm is not suitable for describing the characteristics of LLR

[0004] C.Novak, P.Fertl, G.Matz published "Quantization for soft-output demodulators in bit-interleaved coded modulation systems" in 2009IEEE International Symposium onInformation Theory: 1070-1074, 2009, and proposed a non-uniform quantization method for LLR , use the probability distribution of LLR to quantize it non-uniformly, so that the probability corresponding to each quantization interval is equal, but this method requires the probability distribution of LLR to be known, and the distribution of LLR in practical applications is often not known accurately, so it is difficult Satisfy the algorithm requirements

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