Recent Advances in RF Propagation Modeling for 5G Systems. Stefanovic, M., Panic, S. R., De Souza, R. A., & Reig, J. International Journal of Antennas and Propagation, 2017. ![Recent Advances in RF Propagation Modeling for 5G Systems [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Continuously increasing demand for higher data rates, larger network capacity, higher energy efficiency, and higher mobility has motivated research within fifth-generation (5G) communication systems modeling. 5G is generally agreed for a set of new requirements for wireless communications systems. These requirements will need to address several critical performance areas including cost constraints, traffic latency, reliability, security, availability, heterogeneous structure of networks, multicast/broadcast requirements, the requirement to serve a variety of different devices, and reduced energy consumption. Accurate 5G indoor and outdoor channel characterization and modeling are crucial for determining the system performance and thus for system and for 5G network realization. Namely, 5G radio frequency (RF) propagation is affected by various phenomena that more or less deteriorate the original transmitted signal arriving at the receiver (free-space propagation, object penetration, reflection, scattering, diffraction, and absorption caused by atmospheric gases, fog, and precipitation). To generate reliable propagation models for 5G systems and further to determine standard performance measurements of 5G systems, corresponding path loss models must be built for link budget evaluation and signal strength prediction , with the inclusion of directional and beamforming antenna arrays and cochannel interference, while temporal dispersion caused by multipath propagation (impacting the timing, packet and frame sizes, and other air interface design parameters) should also be characterized. Therefore, general statistical models could not be sufficient in order to assess the performance of system and specific models related to real-world reference scenarios with fine classification of terms will be required. For the development of new 5G systems to operate in millimeter bands, there is a need for accurate propagation modeling at these bands. Exploitation of unused millimeter wave (mmWave) band spectrum (spectrum between 6 and 300 GHz) is an efficient solution for meeting the standards for 5G networks enormous data demand growth explosion. Measurements provided at 38 GHz (Base Station-to-Mobile Access Scenario [1] and Peer-to-Peer Scenario [2]), 60 GHz (Peer-to-Peer Scenario and Vehicular Scenario [3]), and 73 GHz [4] have clearly identified the existence of non-line-of-sight (NLOS) conditions. One of the most intensively used statistical models for characterizing the complex behavior and random nature of NLOS fading envelope is the Nakagami-m distribution. In [5-7] for the purpose of modeling observed 5G system propagation properties, the Nakagami-m parameter is directly computed from the measured data. Two most well-known procedures used for the estimation of the Nakagami-m fading parameter, m, are (1) maximum likelihood (ML) estimation and (2) moment-based estimation. However, it is known that sample moments are often subjected to the effects of outliers (even a small portion of extreme values, outliers, can affect the Gaussian parameters, especially the higher order moments). Moreover, occurrence of outliers is especially problematic when higher order sample moments are used for estimation, since estimation inaccuracy arises in such cases. Providing the best
@article{Stefanovic2017,
title = {Recent {Advances} in {RF} {Propagation} {Modeling} for {5G} {Systems}},
volume = {2017},
issn = {16875877},
url = {https://doi.org/10.1155/2017/4701208},
doi = {10.1155/2017/4701208},
abstract = {Continuously increasing demand for higher data rates, larger network capacity, higher energy efficiency, and higher mobility has motivated research within fifth-generation (5G) communication systems modeling. 5G is generally agreed for a set of new requirements for wireless communications systems. These requirements will need to address several critical performance areas including cost constraints, traffic latency, reliability, security, availability, heterogeneous structure of networks, multicast/broadcast requirements, the requirement to serve a variety of different devices, and reduced energy consumption. Accurate 5G indoor and outdoor channel characterization and modeling are crucial for determining the system performance and thus for system and for 5G network realization. Namely, 5G radio frequency (RF) propagation is affected by various phenomena that more or less deteriorate the original transmitted signal arriving at the receiver (free-space propagation, object penetration, reflection, scattering, diffraction, and absorption caused by atmospheric gases, fog, and precipitation). To generate reliable propagation models for 5G systems and further to determine standard performance measurements of 5G systems, corresponding path loss models must be built for link budget evaluation and signal strength prediction , with the inclusion of directional and beamforming antenna arrays and cochannel interference, while temporal dispersion caused by multipath propagation (impacting the timing, packet and frame sizes, and other air interface design parameters) should also be characterized. Therefore, general statistical models could not be sufficient in order to assess the performance of system and specific models related to real-world reference scenarios with fine classification of terms will be required. For the development of new 5G systems to operate in millimeter bands, there is a need for accurate propagation modeling at these bands. Exploitation of unused millimeter wave (mmWave) band spectrum (spectrum between 6 and 300 GHz) is an efficient solution for meeting the standards for 5G networks enormous data demand growth explosion. Measurements provided at 38 GHz (Base Station-to-Mobile Access Scenario [1] and Peer-to-Peer Scenario [2]), 60 GHz (Peer-to-Peer Scenario and Vehicular Scenario [3]), and 73 GHz [4] have clearly identified the existence of non-line-of-sight (NLOS) conditions. One of the most intensively used statistical models for characterizing the complex behavior and random nature of NLOS fading envelope is the Nakagami-m distribution. In [5-7] for the purpose of modeling observed 5G system propagation properties, the Nakagami-m parameter is directly computed from the measured data. Two most well-known procedures used for the estimation of the Nakagami-m fading parameter, m, are (1) maximum likelihood (ML) estimation and (2) moment-based estimation. However, it is known that sample moments are often subjected to the effects of outliers (even a small portion of extreme values, outliers, can affect the Gaussian parameters, especially the higher order moments). Moreover, occurrence of outliers is especially problematic when higher order sample moments are used for estimation, since estimation inaccuracy arises in such cases. Providing the best},
journal = {International Journal of Antennas and Propagation},
author = {Stefanovic, Mihajlo and Panic, Stefan R. and De Souza, Rausley A.A. and Reig, Juan},
year = {2017},
}
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To generate reliable propagation models for 5G systems and further to determine standard performance measurements of 5G systems, corresponding path loss models must be built for link budget evaluation and signal strength prediction , with the inclusion of directional and beamforming antenna arrays and cochannel interference, while temporal dispersion caused by multipath propagation (impacting the timing, packet and frame sizes, and other air interface design parameters) should also be characterized. Therefore, general statistical models could not be sufficient in order to assess the performance of system and specific models related to real-world reference scenarios with fine classification of terms will be required. For the development of new 5G systems to operate in millimeter bands, there is a need for accurate propagation modeling at these bands. Exploitation of unused millimeter wave (mmWave) band spectrum (spectrum between 6 and 300 GHz) is an efficient solution for meeting the standards for 5G networks enormous data demand growth explosion. Measurements provided at 38 GHz (Base Station-to-Mobile Access Scenario [1] and Peer-to-Peer Scenario [2]), 60 GHz (Peer-to-Peer Scenario and Vehicular Scenario [3]), and 73 GHz [4] have clearly identified the existence of non-line-of-sight (NLOS) conditions. One of the most intensively used statistical models for characterizing the complex behavior and random nature of NLOS fading envelope is the Nakagami-m distribution. In [5-7] for the purpose of modeling observed 5G system propagation properties, the Nakagami-m parameter is directly computed from the measured data. Two most well-known procedures used for the estimation of the Nakagami-m fading parameter, m, are (1) maximum likelihood (ML) estimation and (2) moment-based estimation. However, it is known that sample moments are often subjected to the effects of outliers (even a small portion of extreme values, outliers, can affect the Gaussian parameters, especially the higher order moments). Moreover, occurrence of outliers is especially problematic when higher order sample moments are used for estimation, since estimation inaccuracy arises in such cases. 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