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Channel estimation method based on variational Bayesian inference

A technique of variational Bayesian and channel estimation, applied in the field of channel estimation based on variational Bayesian inference

Active Publication Date: 2018-06-01
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 based on variational Bayesian inference
  • Channel estimation method based on variational Bayesian inference
  • Channel estimation method based on variational Bayesian inference

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Embodiment

[0056] In this example, the number of antennas of the transmitting base station is set to N t =32, the receiving party is a single user, has a receiving antenna, the equivalent channel length L=64, and the channel sparsity s=10 (the sparse structure is: the total number of positions s c =6, the number of non-shared positions s p =4), the noise ratio SNR=20dB, and the number of subcarriers N=4096.

[0057] image 3 Massive MIMO channel estimation flow chart, according to the flow chart, the algorithm can be simulated using the above parameters.

[0058] S1. Initialization, specifically:

[0059] 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.

[0060] S12. The received signal of MS is y=Φh+w, w is additive Gaussian white no...

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Abstract

The invention belongs to the technical field of wireless communications, and in particular relates to a channel estimation method based on variational Bayesian inference. According to the method provided by the invention, the similarity between a sparse structure of a large-scale MIMO channel and a structure thereof is utilized, a sparse model (hierarchical prior model) of the large-scale MIMO channel is innovatively constructed, a probability event is imported to control the location of the channel to be completely common or not, a channel estimation algorithm based on variational Bayesian inference (abbreviated as Mixture_VBI) is proposed, and compared with OMP, ASSP, Geniu-LS, and other channel estimation methods, the channel estimation method provided by the invention has the advantages of improving the accuracy of channel estimation, and the channel estimation error can reach 10-3 under certain conditions.

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 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 MIMO system, the channel est...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04L25/02H04B7/0413H04L27/26
CPCH04B7/0413H04L25/0202H04L25/024H04L27/2602
Inventor 唐超成先涛
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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