Sparse superposition code design scheme based on hybrid iteration power distribution

Abstract

The invention provides a sparse superposition code design scheme based on hybrid iteration power distribution, and the scheme specifically comprises the steps: carrying out the sparse representation of an input real value signal through employing a one-hot encoding mode, and enabling each group to have only one non-zero value; then, assigning the non-zero value in each group of the sparse messagevectors by using the hybrid iteration power distribution scheme provided by the invention; then performing linear combination of corresponding groups with the random Hadamard design matrix to form corresponding code words; secondly, inputting the formed code words into an AWGN channel, and adding Gaussian white noise with the mean value of 0 and the variance of 2 into the code words; and carryingout iterative estimation operation on the output noisy code word through an AMP decoding algorithm to obtain an estimation value of the message vector; setting the maximum value corresponding bit in each group of the obtained estimation values as a preset value obtained by power distribution, and setting the rest bits as 0 to obtain a reconstruction value of the original signal. An experiment is carried out by using spyder3, and a needed python third party package comprises numpy, pyfht and the like.

Description

technical field [0001] The invention belongs to the technical field of signal processing, and in particular relates to a sparse superposition code design scheme based on hybrid iterative power allocation. Background technique [0002] As an essential part of the digital age and information society, communication has always been the object of extensive attention of research scholars. In 1948, Shannon proposed that channel coding can help achieve effective and reliable transmission of signals, and Shannon's three theorems pointed out the direction for the design of channel coding. As long as the information is transmitted at a communication rate lower than the channel capacity, there must be a channel coding method that makes the probability of error in the transmitted information tend to be infinitely small as the code length increases. Although channel coding can achieve good performance, the existence of Shannon bound makes the performance of channel transmission no longer...

AI Technical Summary

Problems solved by technology

However, there is a common problem of unreasonable allocation, which makes it difficult to start the decoding process, or the power allocation does not meet the decoding requirements, resulting in low decoding accuracy

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