Transform domain quadratic estimation method based on combination of time domain clustering denoising with equalization decision making

Abstract

The invention discloses a transform domain quadratic estimation method based on combination of time domain clustering denoising with equalization decision making. A harmonic sequence is designed at atransmitting terminal and weighted overlapping is carried out on the designed sequence and a training sequence to generate a transmitting sequence; with an SVD channel estimation method of the existing overlapped training sequence, rough estimation of a channel frequency domain response is obtained; a threshold is set and channel impulse responses are classified into an initial signal class training sample and a noise class training sample, and time domain clustering denoising processing is completed; and transform domain quadratic estimation is carried out on the denoised frequency domain response by using windowed DCT / IDCT interpolation, wherein an equalization decision making method is introduced into the quadratic estimation process. According to the transform domain quadratic estimation method disclosed by the invention, the influences on channel estimation performance by a data sequence and a training sequence are eliminated and the existing SVD channel estimation performance isimproved; noises can be eliminated effectively; and implementation complexity is low. The transform domain quadratic estimation method can be applied in multi-carrier communication well.

Description

technical field [0001] The invention belongs to the technical field of digital communication, and in particular relates to a transformation domain secondary estimation method for joint time domain clustering denoising and equalization judgment, which is applicable to the fields of wireless and power line multi-carrier communication. Background technique [0002] In recent years, wireless communication technology has developed rapidly. Among them, the time-varying characteristics of wireless channels have always been a research hotspot of this technology, and are also the key to improving the capacity and reliability of wireless communication systems. At the same time, the propagation of the signal in the wireless channel is a complex process: including signal scattering, reflection and diffraction, accompanied by the influence of the surrounding environment and the mobile station on the signal transmission. Therefore, fast real-time channel estimation is one of the keys of ...

AI Technical Summary

Problems solved by technology

However, the existing channel estimation methods based on pilots or training sequences have low implementation complexity and better estimation performance, but due to channel time-varying, selective fading, etc., it is necessary to continuously send pilots to quickly track the channel state, which is easy to implement. Cause bandwidth and power loss; based on the channel estimation method of superimposed training sequence, the low-power training sequence is superimposed on the data sequence at the sending end, which does not occupy additional spectrum resources, improves the system bandwidth utilization, and can quickly track channel state changes, but The training sequence and data sequence will have a certain impact on the channel estimation effect

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