Signal-to-noise ratio weighting based OFDM system non-linear demapping method

Abstract

The present invention relates to a signal-to-noise ration weighting based OFDM system non-linear demapping method, which is used for obtaining soft decision information required for decoding a receiving data signal of an OFDM system. The method comprises: according to a channel coefficient of each sub-carrier in the receiving data signal and an overall signal-to-noise ratio of the signal, calculating an absolute signal-to-noise ratio of each sub-carrier; based on the absolute signal-to-noise ratio and a reference signal-to-noise ratio of the signal, calculating a non-linear demapping curve of each sub-carrier; and mapping channel equalizing data of each sub-carrier in each symbol of the receiving data signal via the non-linear demapping curve subjected to signal-to-noise ratio weighting so as to obtain a signal-to-noise ratio weighting quantified value, and obtaining soft decision information by mapping the signal-to-noise weighting quantified value. With adoption of the signal-to-noise ratio weighting based OFDM system non-linear demapping method provided by the present invention, more precise and more reliable soft decision information can be obtained, and the information is used for improving decoding performance of receivers, especially receiver performance under deep fading channels.

Description

technical field [0001] The invention relates to a demapping method for an OFDM system, and is especially suitable for a demapping method similar to an 802.11n system. Background technique [0002] In the OFDM system, the function of the demapping module is to map the channel equalized data to the corresponding constellation diagram, so as to demodulate the correct bit information. OFDM systems usually use convolutional codes for encoding, and the best decoding method for convolutional codes is the Viterbi algorithm. Theoretically speaking, the soft decision of the Viterbi algorithm has a certain performance improvement compared with the hard decision algorithm. The soft decision decoding algorithm needs to input Quantified information including maximum likelihood information, that is, the confidence degree of whether the equalized data corresponds to each bit in the constellation diagram is 1 or 0. This confidence degree is usually represented by the Euclidean distance betwe...

AI Technical Summary

Problems solved by technology

Usually, the receiving end weights the soft decision information according to the strength of the channel coefficient of the carrier. However, the credibility weight based on the strength of the channel coefficient is a relative weight and has nothing to do with the overall SNR, which makes the weighted data available. The reliability is not high, and when the overall signal-to-noise ratio of the signal is high, the reliability of the weighted data is too low, and the packet error rate of the receiver will have a platform effect at this time

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