Rapid iteration wave beam shaping method based on channel space sparseness

Abstract

The invention belongs to the technical field of wireless communication, and especially relates to a method which reduces the antenna training cost of iteration wave beam shaping by utilizing the channel space sparseness in a wireless MIMO communication system. The rapid iteration wave beam shaping method based on channel space sparseness comprises the steps of initialization, LANCZOS iteration, cycled iteration control and the like. In the time division duplex MIMO system, Uplink and downlink channels can be interchanged, iterated antenna training is carried out by introducing the LANCZOS algorithm, the channel space sparseness is utilized, problems in estimating reception vectors in the iteration process is modeled into a sparse reconstruction problem, theory related to compressed sensing further reduce cost of iteration training, and antenna training is completed at an extremely high speed.

Description

technical field [0001] The invention belongs to the technical field of wireless communication, and in particular relates to a method for reducing the antenna training overhead of iterative beamforming by using the space sparsity of the channel in a wireless MIMO communication system. Background technique [0002] In MIMO systems (such as figure 1 As shown in ), according to the principle of maximizing the signal-to-noise ratio at the receiving end, the method to obtain the optimal beamforming matrix is ​​the eigenbeamforming method. When the channel state information (CSI) is known to both the transmitter and receiver, the optimal transmit and receive beamforming matrices can be obtained by decomposing the channel matrix H through SVD. The specific principles are described as follows: [0003] Assume that the number of transmit and receive antennas in the MIMO system is N T , N R , the channel matrix SVD decomposition can be performed, expressed as H=UΛV H ,in,(·) H ...

AI Technical Summary

Problems solved by technology

As we all know, a basic assumption of antenna training is that the channel state does not change in a reasonable short period of time. If the training time lasts too long, the probability of a large change in the channel state will greatly increase, which will make this assumption invalid. It is inevitable that the expected effect cannot be achieved in practical application.

In addition, in addition to the iteration of the first stage, each subsequent iteration of the stage must perform a zero-space projection operation, which introduces additional errors and overhead.

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