Data model dual-driven MIMO receiver

A data model and dual-drive technology, applied in the field of MIMO receivers, to improve network performance, achieve dynamic updates, and achieve significant performance gains

Active Publication Date: 2019-02-26
SOUTHEAST UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The technical problem to be solved by the present invention is to overcome the deficiencies in the prior art, provide a data model dual-drive MIMO receiver, and solve the problem of how to combine wireless transmission with deep learning to improve network self-adaptation

Method used

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  • Data model dual-driven MIMO receiver
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  • Data model dual-driven MIMO receiver

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Embodiment Construction

[0020] Embodiments of the present invention will be described below with reference to the accompanying drawings.

[0021] like figure 1 As shown, an embodiment of the present invention provides a system block diagram of a data model dual-driven MIMO receiver. The present invention introduces deep learning on the basis of traditional iterative receivers to improve receiver performance. By developing the traditional iterative receiver, using the traditional algorithm as the initial value, and then optimizing the receiver through the deep learning technology, the invention realizes dynamic update and network adaptation, and can obtain significant performance gain on the basis of the traditional iterative receiver. . The method of the present invention specifically comprises the following steps:

[0022] (1) Since deep learning networks usually deal with real-number domain data, the equivalent real-number domain MIMO system model needs to be considered as follows:

[0023] y=Hx...

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Abstract

The invention discloses a data model dual-driven MIMO receiver which is formed by a series of networks of the same T-layer structure. The network of each layer comprises a minimum mean square error denoiser and a linear estimator. Channel state information and a reception signal are used as the input of the network of each layer. The network of the t-th layer is combined with the output of the network of the (t-1) layer to calculate an error variance vector. The network of the t-th layer calculates external information according to input parameters to be trained, the error variance estimationvector and a linear estimator. According to the external information, the minimum mean square error denoiser is used to calculate the posterior probability mean value, and at the same time the mean value is output and transmitted to the network of the next layer. The network of the T-th layer outputs the estimated value of a transmitted symbol. According to the invention, the network performance can be greatly improved; dynamic update and network adaptation are realized; the receiver performance is improved; and significant performance gain is acquired on the basis of a traditional iterative receiver.

Description

technical field [0001] The invention relates to a MIMO receiver with dual driving of data models, and belongs to the technical field of wireless communication. Background technique [0002] The MIMO system has become the mainstream technology of a large number of wireless communication standards because it can improve the spectral efficiency and link reliability of the network. MIMO receiver is an important link in MIMO system design. In recent years, iterative receivers have become a research hotspot in MIMO systems. Due to its excellent performance and appropriate computational complexity, a large number of iterative receiver algorithms have been proposed. However, with the development of wireless communication and service requirements, future wireless communication systems are required to be intelligent, capable of dynamic updating and network adaptation. However, the existing iterative reception algorithms cannot meet the requirements. [0003] In recent years, machi...

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Application Information

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IPC IPC(8): H04B7/08
CPCH04B7/0854
Inventor 金石何恒涛温朝凯
Owner SOUTHEAST UNIV
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