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An improved method to detect coherence time in wireless communications channel based on auto correlation functions

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Abstract

Coherence Time of a wireless communication channel is needed to determine the duration of time during which the channel will be static. If the receiving end is aware of this Coherence Time parameter, another Coherence Time related parameter, Doppler Shift, will be recognized. Very few of the numerous Doppler Shift studies use Coherence Time as the basis for detecting Doppler Shift. This is because the Coherence Time parameters for each channel model will have different values and be hard to detect. Our previous studies have been carried out to determine the Coherence Time detection method on a V2V channel but its correlation coefficient was below 0.5, which is not compatible with the theory. Therefore, this study aims to enhance the method with better correlation coefficient value above 0.5. This method was applied on V2V channel with moving scatterers and Correlated Double-Ring (CDR) channel models. This study can be therefore utilized as a guide for detecting the duration of Coherence Time on wireless communications channels, with the findings of Coherence Time detection being compared to the Coherence Time value calculated theoretically to get the accuracy rate. The accuracy rate on the V2V channel with moving scatterer has an accuracy value of up to 80.2%, while the accuracy rate on the CDR channel has an average accuracy value of up to 90.2%, according to the test results of the Coherence Time detection method that we present. Further, our proposed method could be implemented to determine the data symbol sent in a single frame in order to obtain a higher BER value in wireless communications such as Wi-fi, Cellular or V2V.

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Acknowledgements

The authors are grateful to Institut Teknologi Telkom Purwokerto for sponsoring this research.

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Correspondence to Wahyu Pamungkas.

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Pamungkas, W., Suryani, T., Wirawan et al. An improved method to detect coherence time in wireless communications channel based on auto correlation functions. J Ambient Intell Human Comput 14, 4039–4055 (2023). https://doi.org/10.1007/s12652-022-04470-z

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