A hybrid massive MIMO uplink channel estimation method
A channel estimation and estimated value technology, applied in the direction of digital transmission system, error prevention, electrical components, etc., can solve the problem of channel estimation performance degradation, achieve the effect of improving the scope of application, avoiding the problem of base mismatch, and improving accuracy
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example 1
[0053] Simulation example 1: Select the number of antennas N=63, the number of radio frequency chains M=35, the antenna spacing d=λ / 2, the number of channel paths L=3, and the number of Monte Carlo experiments M c =500. The normalized mean square error was used to evaluate the estimation accuracy of the algorithm. The normalized mean square error is defined as: Among them, h es is the channel estimated by each method, and h is the real channel. The channel path gain is set as a random complex gain, and the angle of arrival of the three channel paths is AOA=(80°, 81°, 85°). The antenna at the base station adopts a uniform linear array, the signal-to-noise ratio ranges from -20dB to 10dB, and the grid density of the compressed sensing algorithm OMP is set to Δθ=2°. The algorithm proposed in the present invention is compared with the channel estimation algorithm based on the compressed sensing algorithm OMP and the atomic norm minimization algorithm ANM.
example 2
[0054] Simulation example 2: Select antenna number N=63, antenna spacing d=λ / 2, channel path number L=3, signal-to-noise ratio is 5dB, channel path gain is random complex gain, and the angle of arrival of the three channel paths is AOA= (80°, 81°, 85°), the number of RF chains varies from 30 to 60. The antenna at the base station adopts a uniform linear array, and the number of Monte Carlo experiments is M c =500. The grid density of the compressive sensing algorithm OMP is set to Δθ=2°. The algorithm proposed in the present invention is compared with the channel estimation algorithm based on the compressed sensing algorithm OMP and the atomic norm minimization algorithm ANM.
[0055] Depend on image 3 It can be seen from the normalized mean square error performance comparison diagram that under different SNR conditions, the performance of the algorithm proposed by the present invention is better than that of other algorithms, reflecting the accuracy of the proposed algori...
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