Timing Estimation Method for Burst Communication

Abstract

The invention discloses a timing estimation method for burst communication. The method is carried out based on a leader sequence. The leader sequence is composed of a binary system m sequence with good correlated characteristics. The method comprises the steps of sending a signal frame with the leader sequence at a communication modulation end, wherein the leader sequence is filled with the m sequence with the good correlated characteristics; sampling a base band receiving signal at a communication demodulation end at the sampling rate four times the symbol rate, and conducting timing estimation based on the sampling rate; sending the received sampling signal into a correlator, and conducting correlation operation on the local m sequence, wherein the correlation operation is completed through addition and subtraction, and the correlator outputs an appearance steep peak value; conducting norm processing on the output of the correlator, finding the maximum point of the norm, and determining the maximum peak value; selecting eight values nearby the peak value to be used for timing estimation, solving the frequency spectrum component of the sequence at the symbol rate, calculating the phase position of the frequency spectrum component, and conducting the normalization processing on the phase position to obtain time domain timing deviation.

Description

technical field [0001] The invention relates to the communication field, in particular to a timing estimation method for burst communication. Background technique [0002] Many communication systems provide preambles (such as GSM systems and WIFI systems) for fast synchronization and channel estimation at the receiving end, so data-assisted timing estimation has important practical significance. Most of these data-assisted timing estimation methods are derived based on the maximum likelihood criterion, which is a quasi-best estimator in the sense of maximum likelihood. [0003] A representative search method is a data-assisted forward timing algorithm that estimates the timing offset by searching for the maximum value of the likelihood function. The non-data-assisted timing method is also called "blind forward timing estimation". This method does not require a preamble sequence, and because it is a forward structure, it does not require a feedback link, so it converges fast...

AI Technical Summary

Problems solved by technology

Data-assisted maximum likelihood estimation methods, such as the search method, have high precision and fast convergence, but the search for the maximum value of the likelihood function consumes a lot of calculations, and the complexity is very high, so it is not easy to transplant into engineering practice; the square ring method has a large amount of computation Less, easy to implement, but nonlinear processing will lead to performance degradation when the signal-to-noise ratio is low, and the "self-noise" is obvious when the shaping factor is small, and the performance will be significantly deteriorated

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