Inter-carrier interference resistant OFDM detection method based on deep learning

A technology of inter-subcarrier interference and deep learning, applied in the direction of modulated carrier system, phase-modulated carrier system, baseband system, etc., to achieve the effect of improving system performance

Active Publication Date: 2018-09-14
SOUTHEAST UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] Aiming at the ICI problem caused by Doppler frequency offset and phase noise in the OFDM system, the present invention provides an OFDM detection method based on deep learning for the OFDM communication system based on deep learning, and approximates the maximum by training the deep detection network. Excellent ML demodulator performance

Method used

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  • Inter-carrier interference resistant OFDM detection method based on deep learning
  • Inter-carrier interference resistant OFDM detection method based on deep learning
  • Inter-carrier interference resistant OFDM detection method based on deep learning

Examples

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Effect test

example 1

[0067] The simulation conditions are as follows:

[0068] Number of subcarriers

[0069] The simulation channel uses the Rayleigh channel with six delay paths, and the relative power level uses the ITU Vehicle-A channel model parameters. figure 2 The simulation results under the above conditions are given. It can be seen that compared with the direct decorrelation method, the bit error rate of deep network detection is about 2dB lower.

example 2

[0071] The simulation conditions are as follows:

[0072] Number of subcarriers

[0073] The simulation channel uses the Rayleigh channel with six delay paths, and the relative power level uses the ITU Vehicle-A channel model parameters. image 3 The simulation results under the above conditions are given. It can be seen that compared with the direct decorrelation method, the bit error rate of deep network detection is about 4dB lower. and figure 2 In comparison, it can also be seen that the performance of the deep detection network is basically not affected by the size of the Doppler frequency offset.

example 3

[0075] The simulation conditions are as follows:

[0076] Number of subcarriers

[0077] The simulated channel uses the Rayleigh channel with four delay paths, and the relative power level uses the ITU Pedestrian-A channel model parameters. Figure 4 The simulation results under the above conditions are given. It can be seen that compared with the direct decorrelation method, the bit error rate of deep network detection is about 4-6dB lower.

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Abstract

The invention discloses an inter-carrier interference resistant OFDM detection method based on deep learning. The method can be applied to a high-speed mobile OFDM communication system and an OFDM system with relatively large millimetric wave band carrier phase noise and can effectively resist against the inter-carrier interference brought by Doppler frequency offset and the phase noise. Accordingto the inter-carrier interference resistant OFDM detection method disclosed by the invention, a deep network structure is designed for an approximation ML detector by using a deep expansion mode on the basis of a projection gradient descent method, the training algorithm is the Adam algorithm, a small batch training mode is adopted, each batch contains multiple input and output OFDM symbols andcorresponding channel matrixes H, that is, each batch reflects the changes of the channels within a period of time. Different types of channel information are retrieved at first during the training, and then deep learning is performed by using the channel information to converge a loss function to a small value. An OFDM signal is demodulated by using a trained deep detection network to effectivelyimprove the performance of the OFDM system that is affected by the inter-carrier interference generated by greater Doppler frequency offset or phase noise.

Description

technical field [0001] The invention relates to an OFDM detection method based on deep learning to resist inter-carrier interference (Inter-Carrier Interference, ICI), and belongs to the technical field of wireless mobile communication. Background technique [0002] OFDM technology can convert high-speed data streams into low-speed parallel data streams through serial-to-parallel conversion and modulate them on mutually orthogonal sub-carriers for transmission. Inter-symbol interference can be eliminated by adding a cyclic prefix. From the perspective of frequency domain, since the subcarrier spacing in OFDM is smaller than the coherent bandwidth of the channel, OFDM can resist frequency selective fading very well, and OFDM technology is easy to combine with multiple input and multiple output, using power allocation and adaptive modulation technology The spatial diversity and multiplexing gain can be obtained, and the requirements of channel transmission can be satisfied to ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04L27/26H04L27/22H04L25/03G06N3/04
CPCH04L25/03821H04L27/22H04L27/265G06N3/045
Inventor 赵春明李骁敏姜明黄启圣
Owner SOUTHEAST UNIV
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