Propagation channel characterization for mobile communication based on measurement campaign and simulation

Predicting the coverage area of mobile networks across various frequency bands is a critical concern in wireless communication. Each frequency band exhibits distinct propagation characteristics that directly influence the network coverage. Therefore, the use of the appropriate propagation models, whether through simulation or measurement-based approaches, is essential for accurate signal strength prediction in different environments and scenarios. In this study, we focus on the analysis and comparison of propagation channel characterization for mobile communication within the sub-6GHz frequency range. Our research involved outdoor measurement campaigns and ray-tracing simulations conducted at the Botswana International University of Science and Technology, covering distances up to 213 m. We then conducted an in-depth investigation and comparative analysis of the measured and simulated data for the propagation channel. Additionally, we derived a characterization model for the closed-in free space reference distance (CI) model based on measured and simulated data. Finally, we employed the root-mean-square error (RMSE) as a quantitative assessment of the performance of the characterized models. Notably, our findings revealed that the derived Close-in model based on measured data outperformed that of the simulations, exhibiting the lowest RMSE values of 3.56 dB, 4.20 dB, and 7.87 dB for location-1 line-of-sight, location-2 line-of-sight, and non-line-of-sight scenarios, respectively. These results hold significant potential for the development of precise path loss models that can effectively predict and optimize the coverage area of mobile communication systems across various real-world scenarios and environments

A comparative analysis of alpha-beta-gamma and close-in path loss models based on measured data for 5G mobile networks

Mobile coverage is crucial for the fifth-generation (5G) network since it affects the network's accessibility and dependability in various locations. With a wider coverage, more people and devices will access the 5G network, allowing them to experience the benefits and capabilities of this important technology. However, the development and construction of the networks need a lot of time and effort to maximize network coverage and service delivery while utilizing the fewest possible infrastructure components. Path loss models are commonly employed to predict network coverage. Therefore, it is expedient to adopt a path loss model that is suitable for the specified geographical area. In this paper, we investigated and analysed two empirical models: the Close-in free space reference distance (CI) model and the alpha-beta-gamma (ABG) model at the low- and mid-spectrum bands. Secondly, the path loss parameters were derived to characterize the two empirical models based on data from the measurement campaign. Finally, Root Mean Square Error (RMSE) was employed for the comparison of the models. It was found that at frequencies 0.9 and 1.8 GHz, the CI model's least RMSE values were 3.6 and 4.1 at location 1 and 4.7 and 4.2 dB at location 2, respectively, outperforming the ABG. However, at 3.4 GHz, the ABG with 3.25 and 1.75 dB outperformed the CI at both locations. For future work, the integration of machine learning techniques to dynamically predict and adapt path loss models in real time based on continuous data collection will be appropriate