A Study on Propagation Models for 60 GHz Signals in Indoor Environments

The millimeter-waves band will enable multi-gigabit data transmission due to the large available bandwidth and it is a promising solution for the spectrum scarcity below 6 GHz in future generations of mobile networks. In particular, the 60 GHz band will play a crucial role in providing high-capacity data links for indoor applications. In this context, this tutorial presents a comprehensive review of indoor propagation models operating in the 60 GHz band, considering the main scenarios of interest. Propagation mechanisms such as reflection, diffraction, scattering, blockage, and material penetration, as well as large-scale path loss, are discussed in order to obtain a channel model for 60 GHz signals in indoor environments. Finally, comparisons were made using data obtained from a measurement campaign available in the literature in order to emphasize the importance of developing accurate channel models for future wireless communication systems operating in millimeter-waves bands.

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 300 GHz backhaul links using historic weather data

According to the recently published IEEE standard 802.15.3d (2017), THz links operating at 300 GHz are viable to achieve more than 100 Gbit s−1 of data rate. This feature can support a transition of the future backhaul connectivity from the underground fibre connection to the wireless, where fibre links are not available or too costly to install. The EU-Japan Horizon 2020 project “ThoR” is working towards the demonstration of such links. A detailed investigation on the influence of weather conditions will help to derive planning guidelines of 300 GHz backhaul links for forthcoming applications. This paper focuses on the dependency of the THz link on the general weather by using ray-tracing simulation. Simulation is conducted combining ITU-R propagation models for atmospheric attenuation (water vapour and oxygen content of air, droplets of rains, liquid content of clouds or fog), a wind-depending swaying model for the antenna poles, and historical measured climate data for the deployment scenarios considered in the ThoR project. As a result, this research will show the feasibility of THz link in outdoor applications under general weather conditions, defines weather-dependent outage probabilities, and allows us to derive planning guidelines of THz links at a frequency of 300 GHz.