A Novel Theoretical Model for Cellular Base Station Radiation Prediction

Sallomi, Adheed Hasan; Hashem, Seham Ahmed; Mezaal, Yaqeen S.

doi:10.5013/ijssst.a.19.06.35

Cited by 5 publications

(7 citation statements)

References 7 publications

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“…In Figure 1, the electric field in the far field near 5G base stations can be calculated using the following equation [26–28]:

E=\sqrt{377\frac{{P}_{T}{G}_{T}(\theta ,\varphi )}{4\pi {R}^{2}}},

where the distance R we consider refers to the horizontal distance between the monitoring point and the 5G base station, P T is the transmit power of antenna and G T ( θ , φ ) is the gain of the 5G MIMO antenna in the direction specified by R coordinates.…”

Section: Methods Descriptionmentioning

confidence: 99%

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Electromagnetic radiation estimation at the ground plane near fifth‐generation base stations in China by using machine learning method

Shi,

Li,

Cui

et al. 2024

IET Microwaves Antenna & Prop

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A novel method based on machine learning is proposed to estimate the electromagnetic radiation level at the ground plane near fifth‐generation (5G) base stations. The machine learning model was trained using data from various 5G base stations, enabling it to estimate the electric field intensity at any arbitrary radiation point when the base station provides service to different numbers of 5G terminals which are in different service modes. The inputs required for the model include the transmit power of the antenna, the antenna gain, the distance between the 5G base station and 5G terminals, terminal service modes, the number of 5G terminals and the environmental complexity around the 5G base station. Experimental results demonstrate the feasibility and effectiveness of the estimation method, with the mean absolute percentage error of the machine learning model being approximately 5.98%. This level of accuracy showcases the reliability of the approach. Moreover, the proposed method offers low costs when compared with on‐site measurements. The estimated results can be utilised to reduce test costs and provide valuable guidance for optimal site selection, thereby facilitating radio wave coverage or electromagnetic radiation regulation of 5G base stations.

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“…In Figure 1, the electric field in the far field near 5G base stations can be calculated using the following equation [26–28]:

E=\sqrt{377\frac{{P}_{T}{G}_{T}(\theta ,\varphi )}{4\pi {R}^{2}}},

Section: Methods Descriptionmentioning

confidence: 99%

“…In Figure 1, the electric field in the far field near 5G base stations can be calculated using the following equation [26][27][28]: E ¼ ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi…”

Section: Technology and Measurement Of 5g Base Stationmentioning

confidence: 99%

Electromagnetic radiation estimation at the ground plane near fifth‐generation base stations in China by using machine learning method

Shi,

Li,

Cui

et al. 2024

IET Microwaves Antenna & Prop

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show abstract

“…Assuming that all base stations emit signals with equal powers through omnidirectional antennas, the signal to co-channel interference ratio at the mobile unit located at the central cell can be given as [7][8][9].…”

Section: Co-channel Interference Formulationmentioning

confidence: 99%

“…The frequency reuse technique must be used efficiently to provide the required quality of service for each mobile subscriber and minimize the co-channel interference caused by other subscribers from other distant links operating at the same channel. The quality of service of any wireless communication network is measured by the signal-to-interference ratio (SIR); therefore, the concept of reuse distance has been introduced [6][7][8]. The propagation channel models are fundamental tools used to estimate signal attenuation and frequency reuse distance in wireless communication systems.…”

Section: Introductionmentioning

confidence: 99%

Maximizing Signal Quality for One Dimensional Cells In Mobile Communications

Adheed H. Sallomi,

Sazan Kamiran Hasan,

Jaafar Qassim Kadhim

2023

WJCMS

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In this work, the cellular network performance based on other cell interference predictions is presented. It presents a mathematical model of co-channel interference analysis in hexagonal and linear cell shapes through a log-distance propagation model to investigate the effect of path loss exponent value on the received signal quality of the downlink. Simulation results obtained show that as the power exponent value increase, the interfering signals attenuation is increased resulting in received signal quality improvement. The signal-to-interference ratio (SIR) received by subscribers close to the cell edge will be less due to the contribution of the near-interfering cells especially when multiple layers of interfering cells are considered. The simulations confirmed that the impact of multi tiers of interfering cells cannot be ignored in systems of small cluster size as they may contribute to system performance degradation.

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“…Finally, in Clarinet [27], a fully end-to-end text-to-wave model is proposed, a fully convolutional-based model on the original Wavenet model. Despite all the high results achieved by the presently mentioned works, the TTS field is still an open field for research that requires improving the synthesized audio quality and reducing the training and inference time and can be used in low computer power devices [28][29][30][31][32].…”

Section: Related Workmentioning

confidence: 99%

Text to speech using Mel-Spectrogram with deep learning algorithms

Karim¹,

Saleh²

2022

PEN

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The purpose of text to speech (TTS), sometimes called speech synthesis, is to synthesize a natural and intelligible speech for a given text. A wide range of applications uses TTS technologies in media, chatbots, and entertainment, among other fields, making it a hot topic for the research community. Recently, the progress achieved by artificial intelligence, especially in deep learning and neural networks, enables TTS to produce a high-quality synthesized speech. However, despite the success achieved, currently, available works suffer from the need for very long training and inference time, which makes it dominated by big tech companies. This paper proposes a model based on convolutional neural networks (CNN) and gated recurrent units (GRU). The proposed model can work even in low computational environments and requires low training time. The MOS achieved is 4.26, higher than the MOS performed by state-of-the-art methods.

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A Novel Theoretical Model for Cellular Base Station Radiation Prediction

Cited by 5 publications

References 7 publications

Electromagnetic radiation estimation at the ground plane near fifth‐generation base stations in China by using machine learning method

Electromagnetic radiation estimation at the ground plane near fifth‐generation base stations in China by using machine learning method

Maximizing Signal Quality for One Dimensional Cells In Mobile Communications

Text to speech using Mel-Spectrogram with deep learning algorithms

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