Method for selecting codebooks based on deep learning under large scale MIMO

A deep learning and large-scale technology, applied in space transmit diversity, radio transmission systems, electrical components, etc., can solve problems such as increased computational burden and the impact of average bit error rate

Active Publication Date: 2016-07-20
CHONGQING UNIV OF POSTS & TELECOMM
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Problems solved by technology

However, the former generally uses exhaustive search to find the optimal codeword, such as random search, alternate prediction, and Lloyd's iterative algorithm. The computational burden of these algorithms will increase sharply with the increase in the number of transmitting antennas.
While DFT systematically provides high chordal distances between precoding vectors, the average bit error rate is easily affected when transmit antennas suffer from high spatial correlation

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  • Method for selecting codebooks based on deep learning under large scale MIMO
  • Method for selecting codebooks based on deep learning under large scale MIMO
  • Method for selecting codebooks based on deep learning under large scale MIMO

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Embodiment Construction

[0042] The preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0043] figure 1 It is a schematic flow chart of the method of the present invention, as shown in the figure, the method specifically includes the following steps:

[0044] S1: Information collection step: the information collection system collects the pilot information of the user terminal in the test area;

[0045] S2: Obtain training samples: construct a pilot training sequence according to the pilot information, and then obtain pilot training samples;

[0046] S3: Initialize the neural network: initialize the parameters of the neural network model;

[0047] S4: Neural network learning: Neural network deep learning is carried out from the pilot training samples to obtain the final network weight value;

[0048] S5: Construct a complete codebook: use the improved DFT (DiscreteFourierTransform, discrete Fourier transform) method to constru...

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Abstract

The invention relates to a method for selecting codebooks based on deep learning under large scale MIMO(Multiple-Input Multiple-Output) and belongs to the technical field of wireless communication. The method comprises following steps: acquiring pilot frequency information of a test zone to establish a pilot frequency training sequence and further obtaining a pilot frequency training sample; performing neural network iteration learning to the pilot frequency sample to obtain a final network weight value; selecting optical code words from a complete codebook according to the signal channel output by the neural network after learning. performing signal channel information matching to an unknown zone and the test zone to obtain a wireless signal channel thereof, and further obtaining code words corresponding to the wireless signal channel. By means of the method, wireless signal model and codebook query can be effectively, accurately and quickly established to avoid signal channel estimation of unknown zones and greatly reduce the complexity of unknown zone signal channel codebook selection.

Description

technical field [0001] The invention belongs to the technical field of wireless communication, and relates to a codebook selection method based on deep learning under massive MIMO (Multiple-Input Multiple-Output, multiple-input multiple-output). Background technique [0002] Any communication system, the channel is an essential component. The wireless channel is a typical "variable parameter channel". The characteristics of the wireless channel are closely related to the propagation environment, such as: terrain, surface features, climate characteristics, electromagnetic interference, communication object moving speed and frequency band used. The communication capability and quality of service (Quality of Service, QoS) of the wireless communication system are closely related to the performance of the wireless channel. Therefore, in order to transmit useful information with high quality and large capacity as possible on limited spectrum resources, it is necessary to have a g...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04B7/04
CPCH04B7/0456
Inventor 龙恳刘月贞余翔王维维闫冰冰杜飞
Owner CHONGQING UNIV OF POSTS & TELECOMM
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