Time-varying OFDM system signal detection method based on deep learning

Abstract

The invention provides a time-varying OFDM system signal detection method based on deep learning. The method comprises the following steps of: generating a signal detection network model input data set; constructing a signal detection network model; presetting training and testing parameters before network training; and training the network by adopting a mode of generating training data and test data on line, feeding the test data into a signal detection network, generating predicted sending data bits by the signal detection network model according to a fed feature vector, and comparing the predicted sending data bits with real sending data bits to test the current performance of the network. The method is applied to a fast time-varying OFDM system. By combining a deep learning method andutilizing the advantages of a recurrent neural network in time sequence processing, the receiver architecture is simplified, the signal demodulation is successfully realized, the signal detection performance in the fast time-varying OFDM system is improved, the system realization complexity is effectively reduced, and the bit error rate performance of the whole system is also improved.

Description

Technical field [0001] The present invention relates to the field of wireless communication technology. Aiming at an OFDM system with a fast time-varying channel, combined with deep learning technology, a signal detection scheme based on a recurrent neural network is proposed, so that the system has lower implementation complexity and better Bit error rate performance. Background technique [0002] Orthogonal Frequency-Division Multiplexing (OFDM) technology is an important technology of wireless communication. It has better anti-multipath fading ability and higher spectrum utilization, and it will be widely used in future mobile communications. prospect. However, because the OFDM system uses orthogonal sub-carriers for parallel transmission, it is particularly sensitive to the frequency offset introduced in wireless transmission. Once the orthogonality between the sub-carriers is destroyed, the performance of the system will drop sharply. With the rapid increase in the moving ...

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Problems solved by technology

[0006] Document 4 "Ye H, Li G Y, Juang B H F. Power of Deep Learning for Channel Estimation and Signal Detection in OFDM Systems[J]. IEEE Wireless Communications Letters, 2017, 7(1): 114-117." for time-invariant OFDM System, the first attempt to apply deep learning methods to OFDM systems, and proved the great potential of deep learning in channel estimation and signal detection in time-invariant OFDM systems, but it did not consider the time-varying nature of wireless channels

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