The Effect of Frequency Offset on the Probability of Bit and Packet Errors in Processing Signals with Chirp Spread Spectrum Modulation

Mukhamadiev, Semen; Dmitriyev, Edgar; Rogozhnikov, Evgeny V.; Duplishcheva, Natalia V.; Kryukov, Yakov V.

doi:10.1109/sibcon50419.2021.9438858

Cited by 4 publications

(6 citation statements)

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“…This is due to the sign ambiguity of the frequency estimation, as stated in Section 2. Based on [16], when using the chirp parameters given in Table 1, a bit error occurs when the frequency offset exceeds 488 Hz. Therefore, if there are cases when the frequency estimation error exceeds the value of 488 Hz, then it is concluded that the method works incorrectly.…”

Section: Discussion Of Modeling Resultsmentioning

confidence: 99%

“…1. The Ghanaatian method provided the correct frequency offset estimation because according to [16], when the 12 SF = , demodulation errors occur if frequency error estimation exceeds 61 Hz 2. The proposed method provided the most accurate frequency estimation.…”

Section: Discussion Of Modeling Resultsmentioning

confidence: 99%

“…The description for the GSS me given below in Figure 7. The use of the GSS method allows one to quickly find the extremum of MLF (16) and does not require computations to be performed for all residual frequency offsets [25]. An example of GSS functioning is shown in Figure 7.…”

Section: Input Chirps Containing Frequency Offset δFmentioning

confidence: 99%

“…In such communication systems, developers are forced to increase the duration of the signals to fight against negative effects and increase the communication range. Large signal duration makes it more vulnerable to frequency offset [16].…”

Section: Introductionmentioning

confidence: 99%

“…The calculation (15) for all possible residual frequency offsets is the MLF (16). MLF has the maximum value when f Δ equals f ′ Δ .…”

mentioning

confidence: 99%

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Compensation of the Frequency Offset in Communication Systems with LoRa Modulation

2022

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This study focused on methods estimation and compensation frequency offset in communication systems with LoRa modulation. LoRa being used in IoT applications rapidly expands its markets. The article discusses three methods for estimation and compensation frequency offset as well as details of the comparative analysis made. Aside from the well-known methods by Ghanaatian and Golden Section Search (GSS), the third method is offered by the authors of the article and represents an improved version of the method proposed by Ghanaatian. The proposed method is based on a symmetric processing signal that is divided into two parts. The advantages and drawbacks of the considered methods are described. As a result of the performed study, a software model was developed, the dependence of the root mean-square error (RMSE) estimation of the frequency offset, and cumulative density function (CDF) of the frequency estimation error was calculated. Modeling was performed under identical conditions. The frequency offset accuracy provided by the proposed method exceeded the accuracy of the considered methods.

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Section: Discussion Of Modeling Resultsmentioning

confidence: 99%

Section: Discussion Of Modeling Resultsmentioning

confidence: 99%