An Overview of Packet Error Rate Models for Wireless Communications

Masnikosa, Ina; Zogovic, Nikola; Nešković, Nataša

doi:10.1109/telfor51502.2020.9306615

Cited by 3 publications

(1 citation statement)

References 9 publications

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“…As an indicator to evaluate the transmission quality of wireless communication, PER plays a decisive role in the throughput of the channel. Although there has been a lot of research on how PER is evaluated [1][2][3][4][5], considering the combination of different modulation and coding strategies, accurate evaluation of PER under fading informative is still an important and difficult task. Some early works have evaluated the PER for some special modulation schemes over Rayleigh fading channels, e.g., for frequency-shift keying (FSK) non-coherent demodulation [1].…”

Section: Introductionmentioning

confidence: 99%

Analysis of the asymptotical average packet error rate over Rician fading channels

Wu,

Ji,

Yuan

et al. 2023

J. Phys.: Conf. Ser.

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In the paper, we develop a simple and rather accurate method to analyze and research the average packet error rate (PER) in the high SNR region for the packet transmission system over Rician channels. By introducing the concept of inverse coding gain, the calculation expression of the average PER under the Rician fading channel can be obtained. Numerical calculations and simulations validate our theoretical analysis and approximation.

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Section: Introductionmentioning

confidence: 99%

Analysis of the asymptotical average packet error rate over Rician fading channels

Wu,

Ji,

Yuan

et al. 2023

J. Phys.: Conf. Ser.

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Frame Error Rate Prediction for Non-Stationary Wireless Vehicular Communication Links

Dakić,

Rainer,

Hofer

et al. 2023

2023 IEEE 34th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

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A Fast and Accurate Approximation of IEEE 802.11 Physical Layer Models for Network Simulators

et al. 2022

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Network simulators are used for the research and development of several types of networks. However, one of the limitations of these simulators is the usage of simplified theoretical models of the Packet Error Rate (PER) at the Physical Layer (PHY) of the IEEE 802.11 family of wireless standards. Although the simplified PHY model can significantly reduce the simulation time, the resulting PER can differ considerably from other more realistic results. In this work, we first study several PER theoretical models. Then, we propose a curve fitting algorithm, which is able to obtain a fast and accurate approximation of other PER models. The curve fitting algorithm uses simulated data as input and outputs the coefficients of a simple model that offers a very accurate approximation of the original PER. Finally, we implemented this approximation in the ns-3 network simulator, thus obtaining high realism since now we can select several theoretical PER models or even a more realistic scenario with the effect of the high Peak-to-Average Power Ratio (PAPR) in the signal. The ns-3 results show how the selection of the PER model at the PHY can significantly impact the Packet Loss Rate (PLR) of a scenario composed of a linear chain of several nodes, one of the simplest multi-hop scenarios.

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An Overview of Packet Error Rate Models for Wireless Communications

Cited by 3 publications

References 9 publications

Analysis of the asymptotical average packet error rate over Rician fading channels

Analysis of the asymptotical average packet error rate over Rician fading channels

Frame Error Rate Prediction for Non-Stationary Wireless Vehicular Communication Links

A Fast and Accurate Approximation of IEEE 802.11 Physical Layer Models for Network Simulators

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