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Channel Estimation Method for Hybrid Quantization System Based on Deep Neural Network

A deep neural network and channel estimation technology, applied in the field of wireless communication, can solve problems such as inability to obtain better performance, and achieve the effect of wide application range and good robustness

Active Publication Date: 2021-09-28
SOUTHEAST UNIV
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AI Technical Summary

Problems solved by technology

However, the optimization results based on this approximate model cannot achieve better performance in the actual quantization process, which makes the traditional method suffer from great limitations.

Method used

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  • Channel Estimation Method for Hybrid Quantization System Based on Deep Neural Network
  • Channel Estimation Method for Hybrid Quantization System Based on Deep Neural Network
  • Channel Estimation Method for Hybrid Quantization System Based on Deep Neural Network

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Experimental program
Comparison scheme
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Embodiment 1

[0053] Embodiment 1: see Figure 1-Figure 2 , a method for channel estimation of a large-scale MIMO system assisted by a hybrid ADC based on a deep neural network, the channel estimation method comprising the following steps:

[0054] The first step: use and Represent the collection of high-precision and low-precision ADC antennas, respectively, and design neural network 1 and neural network 2 to estimate and The channel of the antenna.

[0055] Firstly, training samples are generated in the simulation environment for offline training of neural networks 1 and 2, and the channel model used to generate training samples is

[0056]

[0057] where h represents the channel from the user to the base station, L represents the number of channel multipaths, α l Indicates the gain of the lth propagation path, Indicates the angle of arrival of the lth propagation path, Indicates the steering vector of the lth propagation path.

[0058] For neural networks 1 and 2, genera...

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Abstract

The invention discloses a channel estimation method of a hybrid quantization system based on a deep neural network. First, base station antennas are divided into two sets of high-precision ADC antennas and low-precision ADC antennas, and are generated according to the system transmission model and channel model in the simulation environment. The real channel and the received and quantized pilot signal are used as training data; then, the training data are sent to the designed deep neural network 1 and 2 for offline training, and the training is ended by adjusting the weight matrix of the neural network until the performance converges; finally, The trained neural networks 1 and 2 are assembled at the base station to estimate the channels corresponding to the high-precision ADC antenna and the low-precision ADC antenna, respectively. The estimation method of the present invention makes full use of the powerful learning ability of the deep neural network. After off-line training based on a large amount of data, the deep neural network can discover the spatial correlation between different antennas in a large-scale antenna system, thereby realizing the high-precision ADC antenna Accurate mapping of the corresponding channel to the corresponding channel of the low precision ADC antenna.

Description

technical field [0001] The invention relates to a channel estimation method of a hybrid quantization system based on a deep neural network, belonging to the technical field of wireless communication. Background technique [0002] The large-scale antenna system configures an antenna array with tens or even hundreds of antennas at the base station, and the base station uses a large-scale antenna array to communicate with multiple users simultaneously on the same time-frequency resource. Utilize the spatial freedom provided by the large-scale antenna array of the base station to improve the multiplexing capability of spectrum resources between multiple users, the spectrum efficiency of each user, and the ability to resist inter-cell interference, thereby greatly improving the overall utilization of spectrum resources. At the same time, the overall power efficiency is further improved by utilizing the array gain provided by the large-scale antenna array. Compared with tradition...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): H04L25/02H04B7/0413
CPCH04B7/0413H04L25/0202H04L25/0254
Inventor 潘志文高深刘楠尤肖虎
Owner SOUTHEAST UNIV