Millimeter wave channel estimation method based on Bayesian compressive sensing

A Bayesian compression and channel estimation technology, applied in the field of millimeter-wave MIMO communication, can solve problems such as sparse solution solution errors and noise interference, and achieve the effect of reducing estimation errors and avoiding noise interference

Inactive Publication Date: 2018-01-09
DONGHUA UNIV
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Problems solved by technology

Compared with open-loop channel estimation, closed-loop channel estimation is simpler and faster. Compressed sensing algorithm is used for sparse channel estimation. However, traditional compressed sensing algorithm is susceptible to noise interference, which will lead to errors in the solution of sparse solutions.

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  • Millimeter wave channel estimation method based on Bayesian compressive sensing
  • Millimeter wave channel estimation method based on Bayesian compressive sensing

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Embodiment Construction

[0020] Below in conjunction with specific embodiment, further illustrate the present invention. It should be understood that these examples are only used to illustrate the present invention and are not intended to limit the scope of the present invention. In addition, it should be understood that after reading the teachings of the present invention, those skilled in the art can make various changes or modifications to the present invention, and these equivalent forms also fall within the scope defined by the appended claims of the present application.

[0021] Such as figure 1 As shown, a method for mm-wave MIMO channel estimation based on Bayesian compressed sensing disclosed in the embodiment of the present invention mainly includes the following steps:

[0022] Step 1: In the millimeter wave communication system, repeat the training sequence P times to obtain the observation matrix Z. Among them, the time-domain property of the millimeter-wave channel is: the gain will ch...

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Abstract

The invention relates to a millimeter wave channel estimation method based on Bayesian compressive sensing, which comprises the following steps: fixing a sensing matrix, and repeatedly training a sequence to get a multi-measurement-channel matrix and an observation matrix; getting a complex model of multiple measurement vectors according to the multi-measurement-channel matrix, separating the virtual and real parts of the complex model of multiple measurement vectors, and converting the complex model of multiple measurement vectors into a real model of multiple measurement vectors, and vectorizing the real model of multiple measurement vectors to get a real model of single measurement vector, wherein the real model of single measurement vector satisfies the Gauss likelihood distribution; setting the initial values of hyper parameters in the Bayesian learning process and an iteration stop condition; calculating the channel posterior probability distribution containing hyper parameters and the mean and covariance; and solving the hyper parameters through an expectation maximization algorithm until the iteration stop condition is satisfied. The error of millimeter wave channel estimation can be reduced.

Description

technical field [0001] The present invention relates to the technical field of millimeter wave MIMO communication, in particular to a millimeter wave channel estimation method based on Bayesian compressed sensing. Background technique [0002] With the increasing number of wireless devices and the increasing demand for data transmission rates of mobile terminals, people are gradually shifting their targets to undeveloped higher frequency bands—millimeter wave frequency bands. Millimeter wave communication is a hot spot in 5G communication research in recent years. one. In the millimeter wave frequency band, the potentially large frequency bandwidth can provide greater wireless communication transmission rate. However, high-frequency carriers are also accompanied by greater propagation loss during wireless propagation; on the other hand, due to the shorter wavelength of millimeter waves, in order to achieve effective communication, multiple large-scale antenna arrays can be ...

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Application Information

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IPC IPC(8): H04L25/02H04B7/0413
Inventor 左双左吴贇
Owner DONGHUA UNIV
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