Burst frame header detection method and system based on cyclic sequence type lead code

Abstract

The invention provides a burst frame header detection method and system based on a cyclic sequence type lead code. The method comprises the following steps: configuring the length and cycle index of alocal training sequence; starting frame header monitoring; receiving data after monitoring the frame header; performing segmented cross-correlation operation and accumulation on the received data anda local training sequence; and judging whether the accumulated value is greater than a threshold: if so, indicating that the data frame arrives, otherwise, continuing to receive data. According to the method, the advantages of self-correlation and cross-correlation of the training sequence are combined, so the method not only has good frequency offset resistance, but also has high recognition rate for burst frame header detection under the condition of low signal-to-noise ratio; meanwhile, the complexity of calculation is not increased. Compared with an existing cross-correlation detection mode, the method has the advantages that local correlation and signal autocorrelation can be detected, and the detection accuracy is higher.

Description

technical field [0001] The invention relates to the communication field, in particular to a method and system for detecting a frame header based on a cyclic sequence preamble. Background technique [0002] The frame structure design of the burst communication system generally has a specific training sequence in the front end. Conventional frame detection methods include energy-based frame header detection algorithms, training sequence-based autocorrelation frame header detection algorithms, and training sequence-based cross-correlation frame header detection algorithms. In order to improve the communication performance of the communication system, how to improve the frame detection capability of the burst communication system has become an important research direction. [0003] Energy-based frame header detection algorithm: This method is simple and easy to implement, but it is almost impossible to identify the frame header when the signal-to-noise ratio is poor (SNR<0dB...

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Problems solved by technology

[0003] Energy-based frame header detection algorithm: This method is simple and easy to implement, but it is almost impossible to identify the frame header in the case of poor signal-to-noise ratio (SNR<0dB)

[0004] Autocorrelation frame header detection algorithm based on training sequence: This method can resist the carrier frequency offset of the communication transceiver very well, and has good anti-frequency offset performance, but in the environment with poor signal-to-noise ratio (such as SNR<- 3dB) frame header recognition rate is very low

[0005] Cross-correlation frame header detection algorithm based on training sequence: This method has good anti-noise ability, but this method is more sensitive to carrier frequency offset

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