RMS Delay Spread vs. Coherence Bandwidth from 5G Indoor Radio Channel Measurements at 3.5 GHz Band Our society has become fully submersed in fourth generation (4G) technologies, setting constant connectivity as the norm. Together with selfdriving cars, augmented reality, and upcoming technologies, the new generation of Internet of Things (IoT) devices is pushing the development of fifth generation (5G) communication systems. In 5G architecture, increased capacity, improved data rate, and decreased latency are the objectives. In this paper, a measurement campaign is proposed; we focused on studying the propagation properties of microwaves at a center frequency of 3.5 GHz, commonly used in 5G cellular networks. Wideband measurement data were gathered at various indoor environments with different dimensions and characteristics. A raytracing analysis showed that the power spectrum is dominated by the line of sight component together with reflections on two sidewalls, indicating the practical applicability of our results. Two wideband parameters, root mean square delay spread and coherence bandwidth, were estimated for the considered scenarios, and we found that they are highly dependent on the physical dimension of the environment rather than on furniture present in the room. The relationship between both parameters was also investigated to provide support to network planners when obtaining the bandwidth from the delay spread, easily computed by a raytracing tool.

In Situ Wireless Channel Visualization Using Augmented Reality and Ray Tracing This article presents a novel methodology for predicting wireless signal propagation using raytracing algorithms, and visualizing signal variations in situ by leveraging Augmented Reality (AR) tools. The proposed system performs a special type of spatial mapping, capable of converting a scanned indoor environment to a vector facet model. A raytracing algorithm uses the facet model for wireless signal predictions. Finally, an AR application overlays the signal strength predictions on the physical space in the form of holograms. Although some indoor reconstruction models have already been developed, this paper proposes an image to a facet algorithm for indoor reconstruction and compares its performance with existing AR algorithms, such as spatial understanding that are modified to create the required facet models. In addition, the paper orchestrates AR and raytracing techniques to provide an in situ network visualization interface. It is shown that the accuracy of the derived facet models is acceptable, and the overall signal predictions are not significantly affected by any potential inaccuracies of the indoor reconstruction. With the expected increase of densely deployed indoor 5G networks, it is believed that these types of AR applications for network visualization will play a key role in the successful planning of 5G networks.