Wavelet domani value denoising method for maximum likelihood estimator based on wavelet denoising algorithm

Abstract

Disclosed is a wavelet domain value de-noising method for the maximum likelihood estimator based on the wavelet de-noising algorithm, comprising the following: a known signal is inserted into a fixed pilot; through IDFT, a data is parallel-serial transformed, and after a cyclic prefix is added, the data is discretized and molded and is sent into a radio frequency channel; a filter wave is matched with the received data, and a received pilot data is extracted; a frequency response of a pilot point is obtained, then a channel time domain and a frequency domain shock response are estimated to complete the maximum likelihood estimation, and hMLE is obtained; the maximum likelihood estimation is decomposed by the wavelet; the threshold value of the wavelet coefficient decomposed in each grade is quantified and reconstructed; when the difference value of the maximum likelihood estimation of the wavelet and the traditional maximum likelihood estimation approximately obeys the gaussian distribution at utmost, the maximum likelihood channel estimation of the wavelet; and the channel estimation value is applied to the space-time decoding. The invention does not need the distribution function of the known channel, the improvement of the estimation accuracy brings the decline of the error rate of the STBC decode, and the invention is easy to be realized without the prior statistical information of the channel, and is not limited to a certain channel environment.

Description

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AI Technical Summary

Benefits of technology

The inventors have developed an improved way for removing unwanted signals from data streams that are being transmitted over communication networks such as wireless local area network (WLAN) or cellular phone systems. This can improve signal quality and make it easier to decode these types of transmissions more accurately than previously possible due to their ability to estimate unknown parameters with fewer assumptions compared to existing methods like LMS algorithms. Additionally, this new approach allows for efficient use of resources within different environments while maintaining good performance across multiple channels.

Problems solved by technology

This patents discusses different methods used during data compression or demodulation processes such as quadrature phase shift keyed modulus coding techniques like Quadratic Phase Shift Keying Code (QLC), Generalized Multitrasonic Broadcast Signals with Time Division Multiplex Interference Techniques called Orthogonal Frequency Multiple Access Methods (Orthogonal Acoustic Group Delay Artificial Intelligence Network). These techniques aim to improve upon accuracy when transmitting analogue channels through digital precoding/demodulators.

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