Large-scale MIMO channel joint estimation and feedback method based on deep learning

A technology of deep learning and joint estimation, applied in the field of communication, can solve problems such as channel estimation without consideration, achieve the effect of promoting reconstruction accuracy, improving reconstruction accuracy, and high practical significance

Active Publication Date: 2021-04-30
SOUTHEAST UNIV
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Problems solved by technology

[0004] Technical problem: the technical problem to be solved by the present invention is to overcome the shortcomings of the existing CsiNet and other models based on it for structural optimization, and provide a large The large-scale MIMO downlink channel joint estimation and feedback method makes up for the lack of channel estimation in previous models, and since the feedback is the estimated channel matrix containing noise and estimation errors, the present invention also uses a new decoding method at the base station To improve the network reconstruction performance

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  • Large-scale MIMO channel joint estimation and feedback method based on deep learning
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  • Large-scale MIMO channel joint estimation and feedback method based on deep learning

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Embodiment Construction

[0034] Embodiments of the present invention will be described below in conjunction with the accompanying drawings.

[0035] Such as figure 1 As shown, the present invention designs a massive MIMO channel joint estimation and feedback method based on deep learning. In order to verify that the method of the present invention can obtain downlink channel estimation with less error at the user end, and feed back the estimated noisy channel to the base station for reconstruction, and ensure extremely high reconstruction accuracy, a verification example is given for illustration.

[0036] This verification example is a large-scale MIMO channel joint estimation and feedback method based on deep learning. Through a lightweight convolutional layer structure, the super-resolution estimation of the channel matrix is ​​realized at the user end, and then through data-driven noise reduction automatic encoding At the user end, the channel estimation value to be fed back is compressed and enc...

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Abstract

The invention discloses a large-scale MIMO channel joint estimation and feedback method based on deep learning. The method comprises the steps: performing initial channel estimation at a user side; constructing a channel estimation subnet CEnet, and minimizing the estimation error through training; constructing a channel feedback subnet; at the user side, inputting the optimized channel estimation value, and outputting compressed code words; at the base station end, inputting the code words, and outputting the reconstructed channel matrix. And the two subnets jointly form a channel estimation and feedback joint network CEFnet. A previous CSI feedback network assumes that perfect channel state information is obtained, does not consider that a channel in practice is obtained by estimation, and has errors and noise. According to the invention, a complete downlink channel estimation and feedback process is realized by constructing a channel estimation and feedback joint network CEFnet, the purpose of eliminating errors and noise is achieved by using a brand new network architecture, and the reconstruction precision is improved while the feedback overhead is reduced.

Description

technical field [0001] The invention relates to a massive MIMO channel joint estimation and feedback method based on deep learning, belonging to the technical field of communication. Background technique [0002] Massive Multiple-Input Multiple-Output (Massive MIMO) system is widely considered as a main technology of 5G wireless communication system. This system can greatly reduce multi-user interference by configuring hundreds or even thousands of antennas for the base station to form an antenna array, thereby simultaneously serving multiple users on the same time-frequency resource block and providing double-increased cell throughput. . However, the aforementioned potential benefits are mainly obtained by exploiting the CSI in the base station. Although Time-Division Duplexing (TDD) technology can obtain CSI from the uplink, it requires a complicated calibration process, while Frequency-Division Duplexing (FDD) technology completely needs to obtain CSI through feedback. ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04L25/02H04B7/0417G06F17/16G06N3/04G06N3/08
CPCH04L25/0228H04L25/024H04L25/0242H04B7/0417G06F17/16G06N3/08G06N3/045Y02D30/70
Inventor 金石陈彤郭佳佳陈慕涵
Owner SOUTHEAST UNIV
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