Large-scale MIMO downlink channel estimation method based on reconstructed Hankel matrix

A Hankel matrix and channel estimation technology, which is applied in the field of massive MIMO downlink channel estimation based on reconstructed Hankel matrix, can solve problems such as mismatch, and achieve the effect of improving accuracy and reducing pilot overhead

Pending Publication Date: 2022-03-22
HANGZHOU DIANZI UNIV
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  • Description
  • Claims
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Problems solved by technology

However, the above methods all need to preset the grid point angle, and the angle can be any value in reality, so the base mismatch problem will inevitably occur

Method used

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  • Large-scale MIMO downlink channel estimation method based on reconstructed Hankel matrix
  • Large-scale MIMO downlink channel estimation method based on reconstructed Hankel matrix
  • Large-scale MIMO downlink channel estimation method based on reconstructed Hankel matrix

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example 1

[0046] Simulation example 1: Select the number of base station antennas N=123, the number of snapshots T=120, the antenna spacing d=λ2, use the LOS channel, and scatter clusters N c = 3, the sub-path N of each scattering cluster s =10, then the total channel number L=N c N s = 30, Angular expansion Δ θ = 1°, the number of Monte Carlo experiments M c =500. The channel path gain is set as a random complex gain, and the angles of arrival AOD at the center of the three channel paths are uniformly distributed within [-π / 3, π / 3]. The antenna at the base station adopts a uniform linear array, and the number of grid points varies from 120 to 200. The algorithm proposed in the present invention is compared with the channel estimation algorithm based on the compressed sensing algorithm OMP and the offgrid-Bayesian algorithm.

example 2

[0047] Simulation example 2: Select the number of base station antennas N=123, the number of snapshots T=120, the antenna spacing d=λ2, use the LOS channel, and scatter clusters N c = 3, the sub-path N of each scattering cluster s =10, then the total channel number L=N c N s =30, Angular expansion Δ θ = 1°, the number of Monte Carlo experiments M c =500. The channel path gain is set as a random complex gain, and the angles of arrival AOD at the center of the three channel paths are uniformly distributed within [-π / 3, π / 3]. The antenna at the base station adopts a uniform linear array, and the signal-to-noise ratio ranges from -10dB to 10dB. The algorithm proposed in the present invention is compared with the channel estimation algorithm based on the compressed sensing algorithm OMP and the offgrid-Bayesian algorithm.

example 3

[0048] Simulation example 3: Select base station antenna number N=123, SNR=-5dB, antenna spacing d=λ2, use LOS channel, scattering cluster N c = 3, the sub-path N of each scattering cluster s =10, then the total channel number L=N c N s =30, Angular expansion Δ θ = 1°, the number of Monte Carlo experiments M c =500. The channel path gain is set as a random complex gain, and the angles of arrival AOD at the center of the three channel paths are uniformly distributed within [-π / 3, π / 3]. The antenna at the base station adopts a uniform linear array, and the number of pilots varies from 60 to 110. The algorithm proposed in the present invention is compared with the channel estimation algorithm based on the compressed sensing algorithm OMP and the offgrid-Bayesian algorithm.

[0049] Depend on image 3 It can be seen from the normalized mean square error performance comparison diagram that the reconstructed Hankel-based method is significantly better than the other two metho...

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Abstract

The invention discloses a large-scale MIMO (Multiple Input Multiple Output) downlink channel estimation method based on a reconstructed Hankel matrix. The method comprises the following implementation steps of: firstly, modeling a channel; the base station side sends a plurality of pilot signals and the user side receives data; the base station side does not send the pilot signal and the user side receives data; designing an optimization problem based on the reconstructed Hankel matrix; and solving an optimization problem and performing channel estimation. The method belongs to a meshless method, and the problem of base mismatching of a traditional compressed sensing method can be avoided; secondly, compared with the traditional method, the pilot frequency overhead can be obviously reduced; finally, according to the method provided by the invention, when a user side receives data, noise parameters of a base station receiver are obtained, so that the model has a de-noising capability, and the accuracy of channel estimation is improved.

Description

technical field [0001] The invention belongs to the technical field of wireless communication, in particular to the field of channel estimation of a massive MIMO (Massive MIMO) structure communication system, in particular to a massive MIMO downlink channel estimation method based on reconstructed Hankel matrix. Background technique [0002] Massive MIMO (Massive Multi-input Multi-output) is one of the key technologies of the 5G mobile communication system, which has the advantages of high spectral efficiency and high energy efficiency. The prerequisite for obtaining the above advantages is to obtain accurate channel state information (CSI). [0003] In modern wireless communication systems, the acquisition of CSI in massive MIMO has always been a research hotspot in the industry. Since the uplink and downlink in a time division duplex (TDD) system use the same frequency point, the uplink and downlink channel state information is reciprocal, that is, the downlink channel st...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04L25/02H04B7/0413
CPCH04L25/0202H04L25/0242H04B7/0413
Inventor 潘玉剑王锋
Owner HANGZHOU DIANZI UNIV
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