Cyber-enabled RF propagation modeling for advanced wireless communications systems

Propagation and channel modeling are crucial for optimized design of advanced wireless communications systems which have been evolving at a dramatic speed in the last decade. The ever smaller cell sizes, wider bandwidth, and higher data rate constitute challenges for the design of wireless systems. The recent introduction of multiple antenna systems requires channel information in angular domains in order to exploit the multipaths to enhance the data rate and system reliability. The cognitive radio concept prescribes even greater challenges on propagation modeling in which real time capabilities are needed.

To meet these challenges and provide accurate propagation predictions, numerical simulations should be able to tackle the complex propagation environments such as urban areas, indoor environments, and indoor/outdoor interfaces with satisfactory computational speed and accuracy. Although traditional propagation models such as Hata model are easy to use and are fast in terms of simulation, they are not applicable to small cells where individual structures such as buildings in urban areas and rooms in indoor environments affect the propagation characteristics significantly and have to be taken into account to achieve realistic propagation modeling. It is now well known that ray-tracing is the best numerical tool to provide satisfying propagation prediction results.

Our group has developed sophisticated ray-tracing algorithms and software packages which provide path loss, angle of arrival/departure, and delay profile/spread for indoor and outdoor propagation environments. To further improve the accuracy of our ray-tracing tool, we developed complex wall models (such as the metal framed windows) which fit the ray tracing engine and can be integrated with it. Sample result from the "Site Planning Software" package developed at the University of Hawaii is shown in Fig. 1 [12].

Recently, as the geospatial data are accumulating explosively in the cyberspace, we developed methods to reconstruct three-dimensional (3D) building structures based on high resolution images and visualizations available in Google Earth. This unique capability extends the application of our ray-tracing software to an ever wider scenario [9]. We are approaching the ultimate goal of viewing the radio propagation prediction on a typical image of the urban environment of interest.

Also, we are continuously doing researches to improve the computational speed and accuracy of the ray-tracing tool to achieve the real time capability. We are proposing an intelligent ray-tracing scheme which exploits the spatial coherence of the rays to accelerate the computational speed. Significant speedup is expected in the near future.

Fig. 1. Added capabilities to our site-planning software: the radiation patterns of transmit and receive antennas can be loaded into the software for simulation of realistic antennas. The software can provide path loss, delay spread, angle of arrival/departure, and others.

All these capabilities and research topics are tightly related to the industries and many of our research activities are actually motivated and sponsored by the industry partners and their needs. For example, via the NSF Center (Connection One), we have worked with Kyocera for providing angular propagation information such as angle of arrival/departure; with BAE Systems and L-3 Communications, we investigated real time capabilities and 3D reconstruction of building structures by exploiting available geospatial resources in Cyberspace. Some results under the industrial sponsorship are illustrated in Figs. 2 and 3.

Fig. 2. Comparison between original (top) and reconstructed (bottom) 3D building structures in Cape Town, South Africa. Based on 60 building structures we reconstructed, it is found that for more than 95% cases the footprint and height errors are less than 0.5 meters which is excellent in terms of terms of propagation prediction. Our simulation results show that the reconstruction errors/inaccuracies will cause an average standard deviation of 5.1 dB compared with a reference one.

Fig. 3. Received power along five routes in Rosslyn, VA, using the 3D reconstructed model. The simulation results were compared with the published ones and good agreement between them is observed.

We also reconstructed part of Rosslyn City, VA, based on Google Earth visualization, built the 3D building models, and simulated the path gains along five measurement routes. The results were compared with the published ones and good match between them was observed. The path gain can be rendered back in Google Earth together with the 3D buildings as shown in Fig. 3.

Objectives: Develop algorithms for the automatic extraction of 3D models from images and/or visualizations available in cyberspace; develop intelligent ray-tracing schemes with real-time capabilities; add new functions or capabilities such as the time variant channel characterization including Doppler effect calculations; investigate the application of genetic algorithms for the optimal site planning such as the determination of best base station locations for a given propagation environment.

Deliverables: Software modules that can be used stand alone or integrated into sponsors’ legacy product.

Business or industry needs: Automation is highly sought by the industries and customized modular software will best fit their need. We understand that cost is a key component for industrial development and commercialization. By directly working with the industry sponsors we are able to address their concerns and meet their needs in an cost effective manner.

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