Transform domain quadratic estimation method uniting weighted threshold de-noising and balanced decision

Abstract

The invention discloses a transform domain quadratic estimation method uniting weighted threshold de-noising and balanced decision. The method comprises the following steps: step 1), uniting a superimposed training sequence and a harmonic sequence and generating a sending sequence; step 2), in an OFDM (Orthogonal Frequency Division Multiplexing) baseband system, through an estimation method of an LS channel of the superimposed training sequence, obtaining coarse estimation of a channel frequency response; step 3), performing amplitude phase compensation on the coarse estimation of the channel frequency response, adding a window function with a width of M and performing DCT (Discrete Cosine Transformation) of an M point, performing low-pass filtering, time domain de-noising and sequence zero padding extension processing on the obtained sequence, performing N point IDCT (Inverse Discrete Cosine Transform), window removing and secondary amplitude phase compensation on an extended N point sequence, as the following formula, and obtaining the following formula; step 4), performing balanced decision on the obtained result and obtaining the following formula, repeating M point DCT, low-pass filtering, time domain de-noising, interpolation zero padding and N point IDCT, and obtaining a final estimation of a frequency domain response, as the following formula. The transform domain quadratic estimation method uniting the weighted threshold de-noising and the balanced decision provided by the invention eliminates the influence of a data sequence and the training sequence on the channel estimation performance, improves the performance of the existing LS (Least Squares) channel estimation, and has lower implementation complexity.

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 weighted threshold 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 wire...

AI Technical Summary

Problems solved by technology

There are two methods for channel estimation based on pilots or training sequences. Among them, channel estimation based on pilots has low complexity and better estimation performance. However, due to channel time-varying and selective fading, it needs Continuously sending pilots can easily cause bandwidth and power loss; based on the channel estimation method of superimposed training sequences, low-power training sequences and data sequences are superimposed at the sending end, which does not occupy other spectrum resources, improves system bandwidth utilization, and can quickly track The channel state changes, but the training sequence and data sequence will have a certain impact on the channel estimation effect

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