Data model dual-drive GFDM receiver and method

Abstract

The invention discloses a data model dual-drive GFDM receiver and method. The method comprises the steps: respectively obtaining a channel estimation and a signal detection neural network; taking thereal-number result of a matrix comprising transmitted pilot frequency information and a received time domain pilot frequency vector as the input of a channel estimation neural network, and outputtingan estimation of the frequency domain channel state information; obtaining an equivalent channel matrix, taking the real-number result of the equivalent channel matrix and the received time domain signal vector as the input of a signal detection neural network, and outputting the result as the estimation of a GFDM symbol; establishing a demapping neural network, taking an estimation of the GFDM symbol outputted by the signal detection neural network as the input, and outputting the estimation as the estimation of the original bit information; determining the output of the demapping network andthe size of a set threshold, and outputting a detection result of the original bit information according to a determination result. The method has the advantages that the training parameters do not change with the data dimension, the training speed is fast, and the adaptability to different channel environments is strong.

Description

technical field [0001] The invention relates to a data model double-driven GFDM receiver and a method thereof, belonging to the technical field of wireless communication. Background technique [0002] Deep learning is a branch of artificial neural network, and the initial model is an artificial neural network with a deep network structure. After 2006, deep learning has received high attention from academia and industry, and its application fields have expanded from the initial image and speech recognition to natural language processing, computer vision, big data feature extraction and search. For a long time, the design and analysis of communication systems have relied on various mathematical models established. But in some scenarios, the difficulty and complexity of modeling has led practitioners to look for entirely new alternatives. In recent years, deep learning has gradually been applied to the field of wireless communication physical layer to help solve some problems...

AI Technical Summary

Problems solved by technology

[0004] The technical problem to be solved by the present invention is to overcome the deficiencies in the prior art, provide a GFDM receiver and method with dual data model drivers, and solve the problems of difficulty in training and poor adaptability of the single data-driven network of the existing GFDM receiver. The advanced iterative algorithm in the field is applied in the design of GFDM receiver based on deep learning to realize the dual drive of data model

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