Wet Antenna Attenuation Model of E-Band Microwave Links Based on the LSTM Algorithm

Pu, Kang; Liu, Xichuan; He, Huaiyu

doi:10.1109/lawp.2020.3011463

Cited by 11 publications

(8 citation statements)

References 13 publications

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“…According to the results, the effects of rain attenuation on the performance of CF mmWave networks are negligible. However, it is worth noting that rainy events can still affect these networks by means of other mechanisms, such as the wet antenna effect and depolarization [76], [77]. Additionally, other weather conditions, like snow and fog, may also impact the performance of these networks when operating in the mmWave spectrum.…”

Section: Discussionmentioning

confidence: 99%

Energy and Spectral Efficiencies of Cell-Free Millimeter-Wave Massive MIMO Systems Under Rain Attenuation Based on Ray Tracing Simulations

Silva

Alencar³

et al. 2023

IEEE Access

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Future wireless communication systems depend on the network's ultra-densification, on the application of massive multiple-input multiple-output (mMIMO) techniques and on the use of higher frequency bands to satisfy the ever-increasing demands for capacity. The operation of cell-free (CF) networks in the millimeter wave (mmWave) spectrum combines those principles, because they are composed of multiple access points (APs) distributed over a geographic region which serve a small number of users. Despite the extensive available bandwidth, the mmWave spectrum imposes high path losses and significant atmospheric molecular absorption to the links. In addition, in this frequency range, rain attenuation can notably degrade communications. Therefore, this article presents a study of the impact of rain attenuation on CF networks operating in the mmWave spectrum, this is in the 26 GHz, 38 GHz and 73 GHz frequency bands, based on site-specific ray tracing simulations. The propagation simulation is characterized under the effects of reflection, diffraction, diffuse scattering, atmospheric molecular absorption, vegetation losses and rain attenuation. The channel model is characterized using a hybrid approach, with the large-scale parameters determined by ray tracing in an environment subject to random Rician small-scale fading. The system performance is measured by the sum spectral efficiency (SSE) and energy efficiency (EE). According to the results, it was observed that AP densification protects the network against the effect of rain attenuation. Furthermore, even in sparse networks, the CF system has low sensitivity with respect to the precipitation rate, resulting in relatively small reductions in the average SSE and EE.INDEX TERMS Cell-free, energy efficiency, massive MIMO, millimeter wave, rain attenuation, ray tracing, spectral efficiency.

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Section: Discussionmentioning

confidence: 99%

Energy and Spectral Efficiencies of Cell-Free Millimeter-Wave Massive MIMO Systems Under Rain Attenuation Based on Ray Tracing Simulations

Silva

Alencar³

et al. 2023

IEEE Access

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“…The most possible reason was that the wet surfaces of the antennas of the ML during and after the rain lead to the wet antenna attenuation [23,24]. Wet antenna attenuation is related to the frequency of the ML, surface material, and the amount of water accumulated on the surfaces, which causes rapid and deep fluctuations in the attenuation of the ML [24][25][26][27]. Thus, it leads to the phenomenon in the experiment.…”

Section: Estimating the Water Vapor Density On Rainy Daysmentioning

confidence: 99%

“…Rain-induced attenuation may be calculated because the ML could estimate the rain in previous studies [6][7][8][9][10][11]. The wet antenna attenuation should also be modeled by some methods such as machine learning [27]. Then we will estimate the water vapor based on the correction RSL by using the SVM method.…”

Section: Estimating the Water Vapor Density On Rainy Daysmentioning

confidence: 99%

Estimating Water Vapor Using Signals from Microwave Links below 25 GHz

et al. 2021

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Water vapor is a key element in both the greenhouse effect and the water cycle. However, water vapor has not been well studied due to the limitations of conventional monitoring instruments. Recently, estimating rain rate by the rain-induced attenuation of commercial microwave links (MLs) has been proven to be a feasible method. Similar to rainfall, water vapor also attenuates the energy of MLs. Thus, MLs also have the potential of estimating water vapor. This study proposes a method to estimate water vapor density by using the received signal level (RSL) of MLs at 15, 18, and 23 GHz, which is the first attempt to estimate water vapor by MLs below 20 GHz. This method trains a sensing model with prior RSL data and water vapor density by the support vector machine, and the model can directly estimate the water vapor density from the RSLs without preprocessing. The results show that the measurement resolution of the proposed method is less than 1 g/m3. The correlation coefficients between automatic weather stations and MLs range from 0.72 to 0.81, and the root mean square errors range from 1.57 to 2.31 g/m3. With the large availability of signal measurements from communications operators, this method has the potential of providing refined data on water vapor density, which can contribute to research on the atmospheric boundary layer and numerical weather forecasting.

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“…[ For WAA quantization, Pu et al [83] obtained the WAA values based on LSTM, which has a good correlation with the measured WAA, and the performance of the model in the 72.75 GHz link is better than that in the 82.75 GHz link. For accurate rain rate calculation, Pudashine et al [84] designed and trained a DL model by using RGs data and applied it to CML-based rainfall measurement, and the result is better than the constant weighted average method.…”

Section: Application Of Machine Learningmentioning

confidence: 99%

A Review on Rainfall Measurement Based on Commercial Microwave Links in Wireless Cellular Networks

Lian

Wei

Sun

et al. 2022

Sensors

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As one of the most critical elements in the hydrological cycle, real-time and accurate rainfall measurement is of great significance to flood and drought disaster risk assessment and early warning. Using commercial microwave links (CMLs) to conduct rainfall measure is a promising solution due to the advantages of high spatial resolution, low implementation cost, near-surface measurement, and so on. However, because of the temporal and spatial dynamics of rainfall and the atmospheric influence, it is necessary to go through complicated signal processing steps from signal attenuation analysis of a CML to rainfall map. This article first introduces the basic principle and the revolution of CML-based rainfall measurement. Then, the article illustrates different steps of signal process in CML-based rainfall measurement, reviewing the state of the art solutions in each step. In addition, uncertainties and errors involved in each step of signal process as well as their impacts on the accuracy of rainfall measurement are analyzed. Moreover, the article also discusses how machine learning technologies facilitate CML-based rainfall measurement. Additionally, the applications of CML in monitoring phenomena other than rain and the hydrological simulation are summarized. Finally, the challenges and future directions are discussed.

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Wet Antenna Attenuation Model of E-Band Microwave Links Based on the LSTM Algorithm

Cited by 11 publications

References 13 publications

Energy and Spectral Efficiencies of Cell-Free Millimeter-Wave Massive MIMO Systems Under Rain Attenuation Based on Ray Tracing Simulations

Energy and Spectral Efficiencies of Cell-Free Millimeter-Wave Massive MIMO Systems Under Rain Attenuation Based on Ray Tracing Simulations

Estimating Water Vapor Using Signals from Microwave Links below 25 GHz

A Review on Rainfall Measurement Based on Commercial Microwave Links in Wireless Cellular Networks

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