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A time-varying OFDM system signal detection method based on deep learning

A signal detection and system signal technology, applied in neural learning methods, baseband systems, baseband system components, etc., can solve the problem of not considering the time variability of wireless channels, reduce implementation complexity, improve signal detection performance, simplify Effects of Receiver Architecture

Inactive Publication Date: 2022-01-07
NORTHWESTERN POLYTECHNICAL UNIV
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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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  • A time-varying OFDM system signal detection method based on deep learning
  • A time-varying OFDM system signal detection method based on deep learning
  • A time-varying OFDM system signal detection method based on deep learning

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Embodiment

[0048] Step 1: Generation of input data for the signal detection network model

[0049] The present invention can pre-set the parameters in the time-varying OFDM system to generate the required data:

[0050] The pilot training symbol in the present invention is set as where x n / 2 It is a pseudorandom noise sequence generated by Matlab, n=1,2,...,64; the number of subcarriers N=64, the length of cyclic prefix N_CP=16; the number of multipaths is set to 3, and the normalized three-path Doppler Frequency shift size ν={v 1 ,v 2 ,v 3}, where v i (i=1,2,3) is a uniformly distributed random number that obeys the mean interval [0.1,0.2]; the complex amplitude h={a 1 +jb 1 ,a 2 +jb 2 ,a 3 +jb 3}, where a i and b i (i=1,2,3) are independent normal distribution random numbers with a mean of 0 and a variance of 0.5.

[0051] The time-varying OFDM system signal detection network model adopted by the present invention is as follows: figure 1 As shown, a set of 64-bit transm...

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Abstract

The present invention provides a time-varying OFDM system signal detection method based on deep learning, generates a signal detection network model input data set, constructs a signal detection network model, and needs to preset training and test parameters before network training, and adopts online generation The training data and test data are used to train the network, and the test data is fed to the signal detection network. The signal detection network model generates the predicted transmission data bits according to the fed eigenvectors, and compares them with the real transmission data bits to test the current network performance. performance. The present invention is aimed at the fast time-varying OFDM system, combined with the deep learning method, utilizes the advantage of the cyclic neural network to process the time series, simplifies the receiver architecture, successfully realizes the demodulation of the signal, and improves the signal detection performance in the fast time-varying OFDM system , the present invention effectively reduces the complexity of system implementation, and also improves the overall bit error rate performance of the system.

Description

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

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): H04L25/02H04L27/26G06N3/04G06N3/08
CPCH04L25/0254H04L27/2602H04L27/2649H04L25/0202G06N3/084G06N3/044G06N3/045
Inventor 姚如贵王圣尧秦倩楠徐娟左晓亚
Owner NORTHWESTERN POLYTECHNICAL UNIV
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