The invention discloses an FDD large-scale
MIMO channel
estimation pilot frequency optimization method based on
compressed sensing, and the method comprises the steps: firstly enabling a channel to be modeled into a formula in a large-scale
MIMO system: Y=HX+N, wherein H (shown in the description) is a channel matrix, X (shown in the description) is a
pilot frequency matrix, Y (shown in the description) is a receiving
signal matrix, and N (shown in the description) is channel
noise, M is the number of transmitting antennas, and T is the number of
pilot frequencies; secondly carrying out the conversion of the channel matrix, and solving the conjugate matrix (shown in the description) of Y, wherein the conjugate matrix (shown in the description) of the channel matrix represents the conversion form of the channel matrix, the conjugate matrix (shown in the description) of the pilot
frequency matrix represents the conversion form of the pilot
frequency matrix, and the conjugate matrix (shown in the description) of the receiving
signal matrix represents the conversion form of the receiving signals of a receiving end; and finally solving an optimal pilot frequency matrix. Because the conjugate matrix (shown in the description) of the channel matrix is a
sparse vector, a channel
estimation problem can be modeled into a
compressed sensing reconstruction problem shown in the description, wherein ||*||<1> represents 1-norm, ||*||<2> represents 2-norm, and epsilon is greater than zero and less than one. The method can guarantee that the FDD
MIMO downlink channel
estimation based on
compressed sensing can remarkably reduce the
mean square error of channel estimation, and improves the channel estimation performance.