Model-driven Turbo code deep learning decoding method

A model-driven, deep learning technique applied in the field of wireless communication

Active Publication Date: 2019-10-01
SOUTHEAST UNIV
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Problems solved by technology

However, whether this model-driven decoding method based on BP algorithm can be applied to se

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  • Model-driven Turbo code deep learning decoding method
  • Model-driven Turbo code deep learning decoding method
  • Model-driven Turbo code deep learning decoding method

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Embodiment Construction

[0034] Below in conjunction with accompanying drawing, technical scheme of the present invention is described in further detail:

[0035] A kind of model-driven Turbo code deep learning decoding method of the present invention comprises the following steps:

[0036] (1) Expand the Turbo code iterative decoding structure into a "flat" structure: treat each iteration in the Turbo code iterative decoding structure as an independent unit, so that the original 3 iterations of the Turbo code decoding The structure can be expanded into three independent units, where the output of the mth unit corresponds to the prior information of the information bits calculated in the mth iteration in the original Turbo iterative decoding structure, m=1,2, and the output of the third unit Output the logarithmic likelihood ratio of the posterior probability corresponding to the information bit calculated in the third iteration in the original Turbo iterative decoding structure, and connect these thr...

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Abstract

The invention discloses a model-driven Turbo code deep learning decoding method, which comprises the following steps of: firstly, unfolding a Turbo code iterative decoding structure into a tiled structure, and replacing each iteration with a DNN decoding unit to form a network Turbo Net for Turbo code decoding; then constructing a graphic structure of a traditional Max-Log-MAP algorithm and carrying out parameterization of the graphic structure to obtain a deep neural network based on the Max-Log-MAP algorithm as a sub-network in a TurboNet decoding unit, so that an SISO decoder in a traditional decoding structure and calculation of external information obtained through output of the SISO decoder are replaced; training a TurboNet composed of M DNN units to obtain model parameters; and finally, normalizing an output value of the Turbo Net by using a sigmoid function, and performing hard decision on a normalization result to obtain an estimated value of the real information sequence u todecode the Turbo code. According to the invention, Max-can be improved; log Compared with a model driven by pure data, the error rate performance of the Max-Log-MAP algorithm is improved, the numberof parameters is reduced by two orders of magnitudes, and the time delay is greatly reduced.

Description

technical field [0001] The invention relates to a model-driven Turbo code deep learning decoding method, which belongs to the technical field of wireless communication. Background technique [0002] As an important channel coding technology, Turbo codes have been widely used in the third and fourth generation mobile communication systems due to their excellent performance close to the Shannon limit. In deep space communication, Turbo codes are also written into CCSDS recommendations as standard codes. However, the traditional iterative decoding algorithm of Turbo code can only be processed serially and requires multiple iterations, which is difficult to meet the extremely low delay characteristic proposed by the fifth generation mobile communication system. [0003] In recent years, deep learning technology has achieved great success in the fields of image and voice, making applications such as unmanned driving and smart home possible. Deep learning is an important branch ...

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Application Information

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IPC IPC(8): H03M13/29G06N3/04
CPCH03M13/2957G06N3/045
Inventor 金石何云峰韩彬张静温朝凯
Owner SOUTHEAST UNIV
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