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Channel estimation method for complex hybrid model based on variational Bayesian inference

A variational Bayesian, mixture model technique, applied in the field of channel estimation of complex mixture models

Active Publication Date: 2018-07-06
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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Problems solved by technology

The main purpose of the EM algorithm is to provide a simple iterative algorithm to calculate the posterior density function. Its biggest advantage is simplicity and stability, but it is easy to fall into local optimum

Method used

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  • Channel estimation method for complex hybrid model based on variational Bayesian inference
  • Channel estimation method for complex hybrid model based on variational Bayesian inference
  • Channel estimation method for complex hybrid model based on variational Bayesian inference

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Embodiment

[0062] In this example, the number of antennas of the transmitting base station is set to N t =32, the number of sub-array groups K=4, the number of antennas in the sub-array M=N t / K=8, the receiver is a single user with one receiving antenna, the equivalent channel length L=64, the sparsity s=10, the signal-to-noise ratio SNR=20dB, and the number of subcarriers N=1024.

[0063] image 3 The flow chart of channel estimation in this example is shown. According to the flow chart, the above parameters can be used to simulate the algorithm.

[0064]S1. Initialization, specifically:

[0065] S11, BS broadcasts pilot signal to MS Transform the pilot signal P in the mathematical model of MIMO channel estimation into a compressed sensing measurement matrix, with Φ n =diag(P n ) F L / ξ , is the channel vector, h n is sparse, and each h n The sparse structures between them are similar.

[0066] S12. The received signal of MS is y=Φh+w, w is additive Gaussian white noise, and...

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Abstract

The invention belongs to the technical field of wireless communication, in particular to a channel estimation method for a complex hybrid model based on variational Bayesian inference. According to the method, a sparse structure of a large-scale MIMO channel and the similarity of the sparse structures of the adjacent channels of the large-scale MIMO channel are utilized, and all antennas are reasonably divided into sub-arrays, so that the mutual relations among the channels are utilized to the largest extent; a sparse model (multi-layer prior model) of the large-scale MIMO channel is innovatively constructed, a probability event is introduced to control the position of the channel to belong to a totally shared position, a share position of the sub-arrays or a non-shared position, and a channel estimation algorithm for the complex hybrid model based on the variational Bayesian inference (abbreviated as Complex-Mixture-VBI) is provided, and meanwhile, compared with the channel estimationmethods such as OMP, ASSP and Geniu-LS, the channel estimation accuracy is greatly improved, and under certain conditions, a channel estimation error can be up to 10% with no need for prior information.

Description

technical field [0001] The invention belongs to the technical field of wireless communication, and in particular relates to a channel estimation method based on a complex mixed model of variational Bayesian inference. Background technique [0002] Massive MIMO (Multiple Input Multiple Output) system is one of the key technologies of the fifth-generation mobile communication system. Its main advantages are: system capacity increases with the number of antennas; transmission signal power is reduced; simple The linear precoder and detector can achieve the optimal performance; the channels tend to be orthogonal, so the co-channel interference in the cell is eliminated. [0003] The prerequisite for realizing these advantages is that the base station (BS) knows the channel state information (CSIT). In a Time Division Duplex (TDD) system, channel estimation is performed at the user end (MS) by utilizing the reciprocity of the uplink and downlink channels. For the FDD massive MIM...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04L25/02H04B7/0413
CPCH04B7/0413H04L25/0204H04L25/024
Inventor 唐超成先涛
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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