OFDM sparse channel estimation method based on adaptive compressed sensing

Abstract

The invention discloses an OFDM sparse channel estimation method based on adaptive compressed sensing. The method comprises the following steps of step (1), modeling an OFDM channel estimation problemas a compressed sensing signal reconstruction problem; and step (2), solving the compressed sensing signal reconstruction problem by utilizing an adaptive compressed sensing channel estimation algorithm, and estimating an OFDM sparse channel. The method has the advantages that the OFDM sparse channel can be quickly estimated under a condition that the OFDM system channel sparsity is unknown; andcompared with an OFDM sparse channel estimation method based on an existing sparsity adaptive algorithm, the OFDM sparse channel estimation method based on the algorithm has lower channel estimation mean square error, lower system error bit rate and shorter estimation time.

Description

technical field [0001] The invention relates to the field of channel estimation of communication systems, in particular to an OFDM sparse channel estimation method based on adaptive compressed sensing. Background technique [0002] In a mobile communication system, multipath propagation causes inter-symbol interference (Inter Symbol Interference, ISI) in the received signal, which seriously affects the reliability of the system. OFDM technology is widely used in 4G mobile communications and wireless local area networks due to its strong ability to resist frequency selective fading and ISI and low complexity. In an OFDM system, the acquisition of channel state information (Channel State Information, CSI) is very important, so it is necessary to estimate the CSI. Least squares estimation, least mean square error estimation [2] Although the complexity of these pilot-based channel estimation methods is low, the pilot sequence overhead is very large, and a lot of pilots are nee...

AI Technical Summary

Problems solved by technology

However, since the step size s of this algorithm is fixed, the value of the step size will affect the reconstruction accuracy and reconstruction quality. Therefore, how to improve the reconstruction speed while ensuring a high reconstruction accuracy is a problem to be solved.

The Variable Step Size stagewise Adaptive Matching Pursuit (VSStAMP) algorithm proposed in the literature identifies the stage of variable step size by judging the number of candidate atomic sets, and increases the step size at different stages. The length of the support set is used to approximate the real sparsity. Although it can effectively reduce the reconstruction time of the algorithm and ensure high reconstruction accuracy, in the atom primary selection stage, the atoms are always deleted according to the length G of the support set to expand the candidate set. , it is easy to introduce a large number of unideal atoms and cause a decrease in reconstruction accuracy

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