Decoding method based on maximum posterior probability in analog coding

Abstract

The invention relates to a decoding method based on maximum posterior probability in analog coding, the method comprises the following steps: S1, by a sending end, sending a signal, subjected to analog coding, and a block variance to a receiving end through introducing pseudo-analog transmission SoftCast of a set chaos function; S2, modeling a noise of the received analog signal into a gaussian noise, and obtaining a to-be-decoded signal of equivalent modeling; S3, calculating a coding gain; S4, through the set chaos function and a closed expression of a MAP decoder gaussian distribution function, obtaining a MAP decoding closed expression of 1 / 2 analog coding and 1 / 3 analog coding; S5, performing IDCT reconstruction for a IDCT reconstruction coefficient matrix to obtain a decoding outputof a noisy coding symbol. Compared with the prior art, the method provided by the invention makes full use of prior probability distribution information of an information source and known received data, simultaneously balances performance and computation complexity and has the advantage of being easy to use in actual systems.

Description

technical field [0001] The invention relates to the field of wireless communication, in particular to a decoding method based on maximum a posteriori probability in analog coding. Background technique [0002] Although the current layered coding technology based on digital transmission can achieve the scalability of the receiving quality, it still cannot completely avoid the cliff effect, but only converts it into a ladder effect. The reason is that the core technology of wireless digital transmission is coding. The input source is first compressed and removed by source coding techniques such as transformation, quantization, and entropy coding to remove redundancy. The compressed source is very sensitive to transmission errors. Errors often cause a large amount of data to be undecodable, so the compressed source is protected by introducing redundancy through channel coding. [0003] In the face of increasingly prominent digital wireless transmission problems, considering th...

AI Technical Summary

Problems solved by technology

The optimization goal of the minimum mean square error is to minimize the mean square error based on the estimated value of the received data and the target data, but this algorithm involves highly nonlinear numerical calculations, such as the Q function, which is due to the complexity of the algorithm when used in an actual system Too high is often difficult to achieve

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