Diffuse scattering model for human brain wave (Alpha) propagation prediction during meditation inside a temple, through ray tracing, a hypothetical study

Electromagnetic radio waves have been propagating for billions of years through the universe since the beginning of time. Electromagnetic radio wave propagation and the communication revolution it spawned, however are products of the twentieth century. Radio propagation in a particular environment is a complex, multipath phenomenon which involves several different mechanisms. According to a traditional, simplified approach, two major urban propagation mechanisms are identified over-roof-top (ORT) or vertical propagation (VP), where one major radial path undergoes multiple diffractions on building tops, and lateral propagation (LP) where several rays reflect/diffract all vertical building walls/edges according to the geometrical Optics (GO) rules before reaching the receiver.

An Expedient, Generalizable and Realistic Data-Driven Indoor Propagation Model

Efficient and realistic indoor radio propagation modelling tools are inextricably intertwined with the design and operation of next generation wireless networks. Machine learning (ML)-based radio propagation models can be trained with simulated or real-world data to provide accurate estimates of the wireless channel characteristics in a computationally efficient way. However, most of the existing research on ML-based propagation models focuses on outdoor propagation modelling, while indoor data-driven propagation models remain site-specific with limited scalability. In this paper we present an efficient and credible ML-based radio propagation modelling framework for indoor environments. Specifically, we demonstrate how a convolutional encoder-decoder can be trained to replicate the results of a ray-tracer, by encoding physics-based information of an indoor environment, such as the permittivity of the walls, and decode it as the path-loss (PL) heatmap for an environment of interest. Our model is trained over multiple indoor geometries and frequency bands, and it can eventually predict the PL for unknown indoor geometries and frequency bands within a few milliseconds. Additionally, we illustrate how the concept of transfer learning can be leveraged to calibrate our model by adjusting its pre-estimate weights, allowing it to make predictions that are consistent with measurement data. <br>

An Expedient, Generalizable and Realistic Data-Driven Indoor Propagation Model

Efficient and realistic indoor radio propagation modelling tools are inextricably intertwined with the design and operation of next generation wireless networks. Machine learning (ML)-based radio propagation models can be trained with simulated or real-world data to provide accurate estimates of the wireless channel characteristics in a computationally efficient way. However, most of the existing research on ML-based propagation models focuses on outdoor propagation modelling, while indoor data-driven propagation models remain site-specific with limited scalability. In this paper we present an efficient and credible ML-based radio propagation modelling framework for indoor environments. Specifically, we demonstrate how a convolutional encoder-decoder can be trained to replicate the results of a ray-tracer, by encoding physics-based information of an indoor environment, such as the permittivity of the walls, and decode it as the path-loss (PL) heatmap for an environment of interest. Our model is trained over multiple indoor geometries and frequency bands, and it can eventually predict the PL for unknown indoor geometries and frequency bands within a few milliseconds. Additionally, we illustrate how the concept of transfer learning can be leveraged to calibrate our model by adjusting its pre-estimate weights, allowing it to make predictions that are consistent with measurement data. <br>

Performance Analysis of Propagation in VHF Military Tactical Communication System

The main challenge of military tactical communication systems is the accessibility of relevant information on the particular operating environment required for the determination of the waveform's ideal use. The existing propagation model focuses mainly on broadcasting and commercial wireless communication with a highs transceiver antenna that is not suitable for numerous military tactical communication systems. This paper presents a study of the path loss model related to radio propagation profile within the suburban in Kuala Lumpur. The experimental path loss modeling for VHF propagation was collected from various suburban settings for the 30-88 MHz frequency range. This experiment was highly affected by ecological factors and existing wave propagation effects such as reflection, diffraction, scattering, and Doppler effect. Radio propagation performance is evaluated by collecting received power at the allocated substation and comparing it against existing propagation models. The existing propagation model also will be tuned close to the measurement value by identifying the best path loss exponent to perform a suitable model for a suburban area. Theoretical assessments and analysis of the initial measurement stage for radio propagation show the extensive contribution of radio field from potential obstacles at lower VHF frequencies for both short and medium ranges around there. The explanation indicates the standard radio propagation prediction models that are generally reasonable for the suburban area. From the general error analysis, it is seen that, the performance of the LDPL with adjusting path loss exponent is the suitable model since it has least value of error metrics.

A Novel Approach for Securing Nodes using Two-Ray Model and Shadow Effects in Flying Ad-Hoc Network

In the last decades, flying ad-hoc networks (FANET) have provided unique features in the field of unmanned aerial vehicles (UAVs). This work intends to propose an efficient algorithm for secure load balancing in FANET. It is performed with the combination of the firefly algorithm and radio propagation model. To provide the optimal path and to improve the data communication of different nodes, two-ray and shadow fading models are used, which secured the multiple UAVs in some high-level applications. The performance analysis of the proposed efficient optimization technique is compared in terms of packet loss, throughput, end-to-end delay, and routing overhead. Simulation results showed that the secure firefly algorithm and radio propagation models demonstrated the least packet loss, maximum throughput, least delay, and least overhead compared with other existing techniques and models.