Self-adaptive double-threshold downlink channel estimation method for large-scale MIMO

Abstract

The invention provides, for an OFDM downlink of a large-scale MIMO system, a threshold-adaptive segmented orthogonal matching pursuit channel estimation method. According to the method, the sparse property of signals is utilized, information sampling is used for replacing signal sampling, effective signals are obtained from observation values of random mapping, meanwhile, by the thought of proportional-integral-differential and residual distribution characteristics, the optimal iteration threshold value can be obtained in a self-adaptive mode on the premise that the signal sparseness is unknown. Simulation results show that the method can adaptively perform channel estimation according to the change of signal sparsity, and under the condition of the same signal-to-noise ratio, the accuracyof signal reconstruction is higher than that of a traditional method.

Description

technical field [0001] The invention belongs to the field of wireless communication, and relates to a channel estimation method of a multiple-input multiple-output (Multi-input Multi-output, MIMO) communication system, in particular to a downlink adaptive method of a massive MIMO communication system based on compressed sensing channel estimation method. Background technique [0002] Massive multiple-input multiple-output (MIMO) systems are equipped with a high number of antennas to improve the energy efficiency of multiplexing, and are widely used in many wireless standards because of their high reliability and the characteristics of significantly improving the capacity of wireless systems. The multi-carrier signal superposition characteristic caused by multiple antennas in a massive MIMO system makes channel estimation difficult, and the CSI of the downlink channel can only be estimated by the receiver. At present, the sparse signal recovery direction can be tried Estimat...

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Problems solved by technology

[0004] The classic greedy reconstruction recovery algorithms include Orthogonal Matching Pursuit (OMP) algorithm, Regularized Orthogonal Matching (ROMP) algorithm and Generalized Orthogonal Matching Pursuit (GOMP) algorithm. Sparsity information; but in actual situations, the sparsity of the channel is an uncertain information, which affects the reconstruction accuracy under the current algorithm

Later, the Segmented Orthogonal Matching Pursuit (StOMP) algorithm was proposed to restore the signal more accurately without determining the signal sparsity. The iteratively reconstructed signal may have a certain error with the original signal, and the accuracy of the reconstruction is therefore reduced

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