Review of Wireless Personal Communications Radio Propagation Models in High Altitude Mountainous Areas at 2.6 GHz

Li, Huafu; He, Xianguo; He, Wei

doi:10.1007/s11277-018-5713-6

Cited by 17 publications

(8 citation statements)

References 17 publications

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“…In practice, interpolation methods based on empirical formulas and correlation-based interpolation methods are usually adopted. [1][2][3][4][5] The interpolation method based on empirical formula is to estimate the RSRPs of the region of interest by building a wireless propagation model that describes the slow change of the received signal power over a long distance. The wireless propagation model usually uses the measured data to build a mathematical model of the electromagnetic environment of the cell, and the RSRP within the coverage area of the cell base station can be determined by the path loss calculated by the wireless propagation model.…”

Section: Related Work 21 Rsrp Estimation Methodsmentioning

confidence: 99%

“…Since empirical models are simple to model and have good generalization in similar scenarios, they are widely used in wireless communication systems for large outdoor environments. COST-231 Hata model [1] and SPM model [2] are the most commonly used wireless propagation models for cell coverage estimation and RSRP estimation worldwide. In recent years, since many physical features affecting wireless transmission are difficult to be represented explicitly by empirical formulas, machine learning techniques have been introduced in several researches for RSRP estimation.…”

Section: Related Work 21 Rsrp Estimation Methodsmentioning

confidence: 99%

“…Currently, two types of interpolation-based RSRP estimation has been widely applied. First, empirical formula-based interpolation methods, such as Cost-231 Hata [1] and standard propagation model (SPM) [2], which are based on statistical theory established by a large amount of measured data to obtain the radio propagation flux in a cell with specific environment. Due to the low computational complexity of this method, it is widely used in the estimation of RSRP for practical outdoor large-area cells.…”

Section: Introductionmentioning

confidence: 99%

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RadioCycle: Deep Dual Learning based Radio Map Estimation

2022

KSII TIIS

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The estimation of radio map (RM) is a fundamental and critical task for the network planning and optimization performance of mobile communication. In this paper, a RM estimation method is proposed based on a deep dual learning structure. This method can simultaneously and accurately reconstruct the urban building map (UBM) and estimate the RM of the whole cell by only part of the measured reference signal receiving power (RSRP). Our proposed method implements UBM reconstruction task and RM estimation task by constructing a dual U-Net-based structure, which is named RadioCycle. RadioCycle jointly trains two symmetric generators of the dual structure. Further, to solve the problem of interference negative transfer in generators trained jointly for two different tasks, RadioCycle introduces a dynamic weighted averaging method to dynamically balance the learning rate of these two generators in the joint training. Eventually, the experiments demonstrate that on the UBM reconstruction task, RadioCycle achieves an F1 score of 0.950, and on the RM estimation task, RadioCycle achieves a root mean square error of 0.069. Therefore, RadioCycle can estimate both the RM and the UBM in a cell with measured RSRP for only 20% of the whole cell.

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Section: Related Work 21 Rsrp Estimation Methodsmentioning

confidence: 99%

Section: Related Work 21 Rsrp Estimation Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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RadioCycle: Deep Dual Learning based Radio Map Estimation

2022

KSII TIIS

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“…Based on the Okumura–Hata model and free space path loss, Vithanawasam et al, in [ 2 ], are calculated the data rates for varying scenarios in different environments (urban, suburban, and rural). The authors in [ 3 ] present five used radio propagation models: Okumura–Hata model, Hata short-range devices model, Cost231Hata model, extended Stanford University interim model, and standard propagation model. Okumura–Hata, Ericsson 9999, Lee propagation, and ECC-33 models are used in [ 4 ] for the urban traffic noise monitoring system.…”

Section: Introductionmentioning

confidence: 99%

“…We speak of propagation in free space when there is direct visibility between receiving and transmitting antennas in the absence of any obstacle. The following formula provides the attenuation [ 1 , 2 , 3 ]: L = 32.4 + 20log(f) + 20 log(d), …”

Section: Introductionmentioning

confidence: 99%

Evaluation of Radio Communication Links of 4G Systems

Ayad

Alkanhel

Saoudi

et al. 2022

Sensors

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Radio communication systems are very widely present in our current smart lifestyles. It consists of two ends, which can carry the transmitter’s information to the receptor. Before installing any radio communication system, it is necessary to analyze the link resources. Hence, this analysis allows the determination of the received radio communication strength to prove if it is sufficient for the link to work correctly and assure a high quality of service. For this reason, new services and technologies are integrated. The objective of the present work is to improve the performance of the radio communication link of 4G systems. The study is based on real measurements using the drive test. The data collected by the drive test are analyzed to increase the performance of the radio communication. Based on this data analysis, recommendations and suggestions are issued for improving the radio communication link. The obtained results indicate a significant amelioration in the performance of the radio communication link.

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