What are the common RF propagation models used in mobile communications?

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Mobile communications rely on radio frequency (RF) waves to transmit and receive information between devices. However, RF waves are affected by various factors, such as distance, obstacles, reflection, diffraction, scattering, and fading. To predict and analyze the behavior of RF waves in different environments, engineers use RF propagation models. These are mathematical formulas that describe how RF signals attenuate, or lose strength, as they travel from a transmitter to a receiver. In this article, you will learn about the common RF propagation models used in mobile communications, and how they can help you design and optimize wireless networks.

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MOHAMMAD KALIM

Radio Network Planning, Optimization and Automation (5G-NR | NB-IoT | LTE | VoLTE | WCDMA | WIMAX | GSM )

1 Free space model

The simplest RF propagation model is the free space model, which assumes that there is no obstacle or interference between the transmitter and the receiver. The free space model uses the inverse square law, which states that the power of the RF signal decreases proportionally to the square of the distance from the source. The free space model also accounts for the antenna gain, which is a measure of how well an antenna can focus or capture RF signals. The free space model is useful for estimating the maximum range and minimum power requirements of a wireless link in an ideal scenario.

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MOHAMMAD KALIM

Radio Network Planning, Optimization and Automation (5G-NR | NB-IoT | LTE | VoLTE | WCDMA | WIMAX | GSM )
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Moving to next Generations of Mobile Communication (1G?2G? … 4G? ?5G) , frequency increases and environment (digital terrain, clutter, building height and density etc.) have more and more impact on RF propagation which increases prediction error. Time to time various suitable propagation models (Free space model, Empirical Path loss models, 3D Ray Tracing Models) with minimum prediction error has been adopted in RF network planning. Moving toward future technology where very high frequency will be used for narrow beam transmission, prediction error will be huge. Minimizing prediction error will play crucial role in planning of future RF networks. New AI/ML based models will be required for upcoming Mobile Networks planning.

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2 Path loss models

The free space model is not very realistic, because in most cases there are obstacles and interferences that affect the RF signals. For example, buildings, trees, hills, and other objects can block, reflect, or scatter the RF waves, causing them to lose more power than in free space. To account for these effects, engineers use path loss models, which are empirical formulas that are derived from measurements and experiments in specific environments. Some examples of path loss models are the Hata model, the COST-231 model, the Okumura model, and the Lee model. Path loss models can help you estimate the average signal strength and coverage area of a wireless network in a given environment.

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3 Fading models

Path loss models are still not very accurate, because they assume that the RF signals have a constant attenuation over distance. However, in reality, the RF signals can vary randomly and rapidly due to small-scale effects, such as multipath propagation, Doppler shift, and shadowing. Multipath propagation occurs when the RF waves bounce off different surfaces and arrive at the receiver with different phases and amplitudes. Doppler shift occurs when the relative motion between the transmitter and the receiver causes a change in the frequency of the RF waves. Shadowing occurs when the RF signals are temporarily blocked by large obstacles, such as buildings or vehicles. These effects cause the RF signals to fluctuate in strength and quality, which is called fading. To model fading, engineers use statistical models, such as the Rayleigh model, the Rician model, the Nakagami model, and the Log-normal model. Fading models can help you evaluate the performance and reliability of a wireless link in a dynamic scenario.

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4 Channel models

The ultimate goal of RF propagation modeling is to characterize the channel, which is the medium through which the RF signals travel from the transmitter to the receiver. The channel can be described by various parameters, such as the path loss, the fading, the delay, the coherence, and the correlation. The channel can also be represented by a transfer function, which relates the input and output signals of the wireless link. Channel models can be either deterministic or stochastic. Deterministic channel models are based on physical principles and geometrical information, such as the location, shape, and material of the obstacles and reflectors. Stochastic channel models are based on random variables and probability distributions, such as the fading and delay statistics. Channel models can help you simulate and test the performance of different wireless technologies and protocols in various scenarios.

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MOHAMMAD KALIM

Radio Network Planning, Optimization and Automation (5G-NR | NB-IoT | LTE | VoLTE | WCDMA | WIMAX | GSM )
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High resolution geographical maps along with proper propagation model are essential for RF network planning of next Generations of Mobile Communication (beyond 4G). Using these high resolution maps (terrain, clutter and buildings), cell-wise best antenna pattern, height, azimuth and tilts can be planned, while considering neighbor cells configuration, clutter type, clutter height, building density, spacing between buildings etc. This help in maximising the RF coverage with minimum overlap. Path loss calculation and prediction done at 2*2m resolution using digital maps of 2*2m resolution with proper planning gives more accurate results.

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5 Model selection

When selecting an RF propagation model, you need to consider your objectives, requirements, and constraints. Factors such as the frequency range and bandwidth of the RF signals, the type and configuration of the antennas, the distance and topology of the wireless link, the environment and terrain of the wireless network, the availability and quality of the measurement data, and the complexity and computational cost of the model can all influence your decision. By making an informed choice, you can improve your understanding of RF propagation behavior and optimize your mobile communication system.

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MOHAMMAD KALIM

Radio Network Planning, Optimization and Automation (5G-NR | NB-IoT | LTE | VoLTE | WCDMA | WIMAX | GSM )
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Many advance propagation models are available now a days but still there will a chance of huge prediction error while doing RF planning of future technology where very high frequency will be used for narrow beam transmission. It’s time to develop AI/ML based radio propagation models. Empirical models are popular due to the simplicity and easy to use, as they requires less computational complexities, but are prone to high prediction errors. Whereas deterministic models are computationally expansive, as they require detailed information of the environment. AI/ML based model will overcome both of the above issues. Accuracy of AI/ML model will be more than an empirical models and computationally less expensive than deterministic one.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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What are the common RF propagation models used in mobile communications?

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1 Free space model

2 Path loss models

3 Fading models

4 Channel models

5 Model selection

6 Here’s what else to consider

Mobile Communications

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What are the common RF propagation models used in mobile communications?

1

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3

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6

1 Free space model

2 Path loss models

3 Fading models

4 Channel models

5 Model selection

6 Here’s what else to consider

Mobile Communications

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