Cyclic prefix-free OFDM receiving method based on model driving depth learning

Abstract

The invention discloses a cyclic prefix-free OFDM receiving method based on model driving depth learning, comprising the following steps: (1) transforming a pilot frequency signal yp in a received signal y to obtain a frequency domain pilot signal, performing least square estimation initialization on the frequency domain pilot signal and a local frequency domain pilot signal to obtain a least square channel estimation result, then, inputting the least square channel estimation result to a fully-connected first deep neural network for improvement, and performing Fourier inverse transformation on an improvement result to obtain a time domain channel estimation signal (formula); (2) performing inter-symbol interference on the received signal y and then converting the received signal into a real-number domain received signal (formula); (3) taking the real-number domain received signal (formula) as an input, and using a second deep neural network to perform iterative solution according to the time domain channel estimation signal (formula) to obtain a final estimated modulation signal (formula); and (4) demodulating the modulation signal (formula) to obtain a transmitted information bit(formula). The method is less in time consumption and high in detection performance.

Description

technical field [0001] The present invention relates to communication technology, in particular to a model-driven deep learning-based OFDM receiving method without cyclic prefix. Background technique [0002] In recent years, deep learning, as a basic technology in artificial intelligence, has achieved great success in disciplines such as computer vision and natural language processing. Deep learning is a branch of the field of machine learning. It is a method of supervised learning. By minimizing the loss function between the predicted value and the real value of the deep neural network, a set of optimal neural network parameters is obtained to make the deep neural network The network is able to make accurate predictions. [0003] There have been some exploratory studies on the application of deep learning in the physical layer of wireless communication, including channel estimation, signal detection, encoder, decoder, channel feedback information reconstruction and end-to...

AI Technical Summary

Problems solved by technology

However, there are still very few examples of integrating the algorithmic knowledge in the field of wireless communication into the design of neural networks. Most of the functions of existing neural networks regard wireless communication systems or modules as a black box, and the training of neural networks depends entirely on a large number of Data-driven, the parameters of the neural network are relatively large, and the training speed is slow

In OFDM without cyclic prefix, inter-symbol interference and inter-carrier interference caused by multipath channels make channel estimation and channel detection face great challenges

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