Downlink channel estimation method for massive MIMO communication system based on real-valued sparse Bayesian learning

A sparse Bayesian, downlink technology, applied in the field of downlink channel estimation of massive MIMO communication systems, can solve problems such as excessive computational load, and achieve the effects of reducing computational load, saving computing time, and improving performance

Inactive Publication Date: 2019-04-16
JIANGSU UNIV
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Problems solved by technology

For example, proposed in the document J.Dai, A.Liu and V.K.N.Lau, FDD Massive MIMOChannel Estimation with Arbitrary 2D-Array Geometry, IEEE Transactions on Signal Processing, vol.66, no.10, pp.2584-2599, 15May

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  • Downlink channel estimation method for massive MIMO communication system based on real-valued sparse Bayesian learning
  • Downlink channel estimation method for massive MIMO communication system based on real-valued sparse Bayesian learning
  • Downlink channel estimation method for massive MIMO communication system based on real-valued sparse Bayesian learning

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Embodiment Construction

[0021] The present invention will be further described below in conjunction with accompanying drawing.

[0022] Such as figure 1 Shown, concrete implementation steps and method of the present invention comprise as follows:

[0023] (1) The base station adopts a uniform linear array with N antennas, and the mobile user in the downlink uses a single antenna. In T time, the base station sends the pilot signal matrix X, and the signal received by the mobile user is y =Φ(β)s+n, where:

[0024] Φ(β)=XA(β) is called the measurement matrix,

[0025] A(β)=[a(θ 1 +β 1 ), a(θ 2 +β 2 ),...,a(θ N +β N )],

[0026]

[0027] φ(θ i +β i )=(2πd / λ)sin(θ i +β i ),

[0028] λ represents the operating wavelength of the electromagnetic wave, d represents the distance between adjacent sensors, Indicates an even division The L grid points of Element β in i represents theta i The angular deviation on

[0029] s is an L-dimensional channel sparsely represented vec...

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Abstract

The invention discloses a downlink channel estimation method for a massive MIMO communication system based on real-valued sparse Bayesian learning. The method comprises the following steps: 1: adopting a uniform linear array with N antennas by the base station, adopting a single antenna by the downlink user, within T moments, transmitting the pilot signal matrix X by the base station, and receiving the signal y=phi(beta)s+n; 2: defining the real-valued matrix QN; 3: defining X=GQN, and constructing the real-valued received signal matrix Y=phi(beta)S+N; 4: setting the number of iterations to count the variable k=1, setting respective elements in the precision vector (FORMULA referred) of the initialization s as 1, setting the initialization noise precision alpha=1, and setting the initialization beta as an all-zero element; 5: fixing delta and beta, and updating alpha by using the SBL principle and expectation maximized criterion; 6: fixing alpha and beta, and updating delta; 7: fixingalpha and delta, and updating beta; 8: determining whether k reaches the upper limit K or whether delta is converged, if not satisfied, k=k+1, and returning to 5; 9: setting a threshold eta, and selecting an effective angle set omega of the channel by using eta; and 10: estimating the final channel according to omega.

Description

technical field [0001] The invention belongs to the field of wireless communication, and relates to a channel estimation method of a multi-input multi-output (MIMO) communication system, specifically a large-scale MIMO communication based on real-valued sparse Bayesian learning Systematic downlink channel estimation method. Background technique [0002] Massive multiple-input multiple-output (Multiple-Input Multiple-Output, MIMO) systems have attracted widespread attention due to their ultra-high spectral efficiency. In a massive MIMO system, the base station is equipped with a large number of antennas, and the number of mobile users served by the base station is far less than the number of base station antennas. The base station uses the same time-frequency resource to serve several mobile users at the same time, fully exploring and utilizing the degree of freedom of space, and improving System spectral efficiency and power efficiency. At present, massive MIMO technology ...

Claims

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Application Information

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IPC IPC(8): H04L25/02
CPCH04L25/0204H04L25/0224H04L25/0242
Inventor 戴继生周磊曹政方忠驰
Owner JIANGSU UNIV
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