Generalized Propagation Channel Model for 2GHz Low Elevation Links Using a Ray-tracing Method

Pechač, Pavel; Zeleny, J.; Pérez-Fontán, F.

doi:10.13164/re.2015.1044

Cited by 5 publications

(4 citation statements)

References 8 publications

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“…As the temperature is lowered, the SA algorithm reduces the range and copes with its global search space to converge and attain a global minimum. To implement the SA algorithm in MATLAB, we use the solver caller technique, which is given with sol SA = simulannealbnd(objFun, po,lob,upb, options) (25) where objFun designates the objective function in Equation (25), Po indicates the initial guess parameters, and lob and upb define the lower and upper bound implementation constraints. The options constitute the temperature information, simulannealbnd, the iteration number, and the plotting function.…”

Section: Simulated Annealingmentioning

confidence: 99%

“…The interaction of the transmitted waves with different propagation mediums and terrain features around the receiver causes its strength to attenuate and degrade, thus resulting in what is known as signal propagation loss. During the cellular radio frequency (RF) network design or optimization phase of an existing one, the RF engineer uses signal propagation models to estimate the characteristics of signal attenuation losses that occur between the transmitting stations and the receiver stations [24][25][26]. These signal propagation models usually contain some unknown parameters that must be accurately determined in correspondence with experimental data from the terrain of interest.…”

Section: Introductionmentioning

confidence: 99%

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Optimal Radio Propagation Modeling and Parametric Tuning Using Optimization Algorithms

Isabona,

Imoize,

Akinwumi

et al. 2023

Information

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Benchmarking different optimization algorithms is tasky, particularly for network-based cellular communication systems. The design and management process of these systems involves many stochastic variables and complex design parameters that demand an unbiased estimation and analysis. Though several optimization algorithms exist for different parametric modeling and tuning, an in-depth evaluation of their functional performance has not been adequately addressed, especially for cellular communication systems. Firstly, in this paper, nine key numerical and global optimization algorithms, comprising Gauss–Newton (GN), gradient descent (GD), Genetic Algorithm (GA), Levenberg–Marguardt (LM), Quasi-Newton (QN), Trust-Region–Dog-Leg (TR), pattern search (PAS), Simulated Annealing (SA), and particle swam (PS), have been benchmarked against measured data. The experimental data were taken from different radio signal propagation terrains around four eNodeB cells. In order to assist the radio frequency (RF) engineer in selecting the most suitable optimization method for the parametric model tuning, three-fold benchmarking criteria comprising the Accuracy Profile Benchmark (APB), Function Evaluation Benchmark (FEB), and Execution Speed Benchmark (ESB) were employed. The APB and FEB were quantitatively compared against the measured data for fair benchmarking. By leveraging the APB performance criteria, the QN achieved the best results with the preferred values of 98.34, 97.31, 97.44, and 96.65% in locations 1–4. The GD attained the worst performance with the lowest APE values of 98.25, 95.45, 96.10, and 95.70 in the tested locations. In terms of objective function values and their evaluation count, the QN algorithm shows the fewest function counts of 44, 44, 56, and 44, and the lowest objective values of 80.85, 37.77, 54.69, and 41.24, thus attaining the best optimization algorithm results across the study locations. The worst performance was attained by the GD with objective values of 86.45, 39.58, 76.66, and 54.27, respectively. Though the objective values achieved with global optimization methods, PAS, GA, PS, and SA, are relatively small compared to the QN, their function evaluation counts are high. The PAS, GA, PS, and SA recorded 1367, 2550, 3450, and 2818 function evaluation counts, which are relatively high. Overall, the QN algorithm achieves the best optimization, and it can serve as a reference for RF engineers in selecting suitable optimization methods for propagation modeling and parametric tuning.

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Section: Simulated Annealingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimal Radio Propagation Modeling and Parametric Tuning Using Optimization Algorithms

Isabona,

Imoize,

Akinwumi

et al. 2023

Information

View full text Add to dashboard Cite

show abstract

“…In [86], efficient multielement ray tracing was recorded, and the results were compared with site-specific measurements using calculated MIMO channel data. The ray-tracing algorithm has recently been used for links at 2 GHz in built-up areas [87]. Unfortunately, ray tracing can be too computationally expensive and lacks flexibility since its effects are fundamentally related to the particular site in which they were extracted [88].…”

Section: Physical Modelsmentioning

confidence: 99%

Standard Propagation Channel Models for MIMO Communication Systems

Imoize

Ibhaze

Atayero

et al. 2021

Wireless Communications and Mobile Computing

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The field of wireless communication networks has witnessed a dramatic change over the last decade due to sophisticated technologies deployed to satisfy various demands peculiar to different data-intensive wireless applications. Consequently, this has led to the aggressive use of the available propagation channels to fulfill the minimum quality of service (QoS) requirement. A major barometer used to gauge the performance of a wireless communication system is the spectral efficiency (SE) of its communication channels. A key technology used to improve SE substantially is the multiple input multiple output (MIMO) technique. This article presents a detailed survey of MIMO channel models in wireless communication systems. First, we present the general MIMO channel model and identified three major MIMO channel models, viz., the physical, analytical, and standardized models. The physical models describe the MIMO channel using physical parameters. The analytical models show the statistical features of the MIMO channel with respect to the measured data. The standardized models provide a unified framework for modern radio propagation architecture, advanced signal processing, and cutting-edge multiple access techniques. Additionally, we examined the strengths and limitations of the existing channel models and discussed model design, development, parameterization, implementation, and validation. Finally, we present the recent 3GPP-based 3D channel model, the transitioning from 2D to 3D channel modeling, discuss open issues, and highlight vital lessons learned for future research exploration in MIMO communication systems.

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“…Zeleny et al [20] presented a semi-deterministic model combining 2D RT and stochastic methods. The canyon model was applied to built-up areas.…”

Section: Introductionmentioning

confidence: 99%

New Modified Urban Canyon Models for Satellite Signal Propagation Prediction

Hadidianmoghadam

Kouki

2019

IEEE Access

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The effects of high-rise buildings on satellite propagation in the vicinity of urban canyons are investigated. A comparison between a conventional canyon model and the two modified canyon models, which take into account the presence of high-rise buildings, is presented for both narrow-band and wideband signal cases. The narrow band is developed using ray tracing (RT) and includes the direct wave, the specular reflection from building walls and ground, and the diffracted waves. In addition, multiple shadow boundaries are defined and used to carry out the uniform theory of diffraction calculations. The incident shadow boundary is the dominant boundary and is used to determine the line-of-sight region for all cases, while wall and ground reflection shadow boundaries are used to obtain higher precision due to multiple reflections. The wideband model is developed by applying a channel transfer function to the data obtained from the RT method. The proposed models are used to predict the received signal in a realistic urban environment from satellites. The models are applicable to any satellite link application, such as global navigation satellite systems, low Earth-orbiting, and high-altitude platform systems, and the results are obtained for a satellite transmitting two linearly polarized signals at a frequency of 1.625 GHz. It is found that the presence of highrise buildings next to a street canyon can significantly alter the visibility of satellites, which, in turn, lead to an increase in path loss. Consequently, ignoring high-rise buildings in the proximity of a street canyon can lead to a path loss difference of as much as 30 dB. INDEX TERMS Street canyon, conventional canyon model (CCM), non-line of sight (NLOS), ray tracing (RT), uniform theory of diffraction (UTD).

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Generalized Propagation Channel Model for 2GHz Low Elevation Links Using a Ray-tracing Method

Cited by 5 publications

References 8 publications

Optimal Radio Propagation Modeling and Parametric Tuning Using Optimization Algorithms

Optimal Radio Propagation Modeling and Parametric Tuning Using Optimization Algorithms

Standard Propagation Channel Models for MIMO Communication Systems

New Modified Urban Canyon Models for Satellite Signal Propagation Prediction

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