In radio wave propagation simulations there is a need for modeling antenna patterns. Both the transmitting and the receiving antenna influence the wireless link. We use spherical harmonics to compress the amount of measured data needed for complex antenna patterns. We present a method to efficiently incorporate these patterns into a ray tracing framework for radio wave propagation. We show how to efficiently generate rays according to the transmitting antenna pattern. The ray tracing simulation computes a compressed irradiance field for every point in the scene. The receiving antenna pattern can then be applied to this field for the final estimation of signal strength.
Simulating Radio Wave Propagation using geometrical optics is a well known method. We introduce and compare a simplified 2D beam tracing and a very general 3D ray tracing approach, called photon path tracing. Both methods are designed for outdoor, urban scenarios. The 2D approach is computationally less expensive and can still model an important part of propagation effects. The 3D approach is more general, and not limited to outdoor scenarios, and does not impose constraints or assumptions on the scene geometry. We develop methods to adapt the simulation parameters to real measurements and compare the accuracy of both presented algorithms.
The display of complex 3D scenes in real-time on mobile devices is difficult due to the insufficient data throughput and a relatively weak graphics performance. Hence, we propose a client-server system, where the processing of the complex scene is performed on a server and the resulting data is streamed to the mobile device. In order to cope with low transmission bitrates, the server sends new data only with a framerate of about 2 Hz. However, instead of sending plain framebuffers, the server decomposes the geometry represented by the current view's depth profile into a small set of textured polygons. This processing does not require the knowledge of geometries in the scene, i.e. the outputs of Time-of-flight camera can be handled as well. The 2.5D representation of the current frame allows the mobile device to render plausibly distorted views of the scene at high frame rates as long as the viewing direction does not change too much before the next frame arrives from the server. In order to further augment the visual experience, we use the mobile device's built-in camera or gyroscope to detect the spatial relation between the user's face and the device, so that the camera view can be changed accordingly. This produces a pseudo-immersive visual effect. Besides designing the overall system with a render-server, 3D display client, and real-time face/pose detection, our main technical contribution is a highly efficient algorithm that decomposes a frame buffer with per-pixel depth and normal information into a small set of planar regions which can be textured with the current frame. This representation is simple enough for realtime display on today's mobile devices.
We present a framework which enables the combination of different mobile devices into one multi-display such that visual content can be shown on a larger area consisting, e.g., of several mobile phones placed arbitrarily on the table. Our system allows the user to perform multi-touch interaction metaphors, even across different devices, and it guarantees the proper synchronization of the individual displays with low latency. Hence from the user’s perspective the heterogeneous collection of mobile devices acts like one single display and input device. From the system perspective the major technical and algorithmic challenges lie in the co-calibration of the individual displays and in the low latency synchronization and communication of user events. For the calibration we estimate the relative positioning of the displays by visual object recognition and an optional manual calibration step.
Conventional beam tracing can be used for solving global illumination problems. It is an efficient algorithm, and performs very well when implemented on the GPU. This allows us to apply the algorithm in a novel way to the problem of radio wave propagation. The simulation of radio waves is conceptually analogous to the problem of light transport. We use a custom, parallel rasterization pipeline for creation and evaluation of the beams. We implement a subset of a standard 3D rasterization pipeline entirely on the GPU, supporting 2D and 3D framebuffers for output. Our algorithm can provide a detailed description of complex radio channel characteristics like propagation losses and the spread of arriving signals over time (delay spread). Those are essential for the planning of communication systems required by mobile network operators. For validation, we compare our simulation results with measurements from a real world network. Furthermore, we account for characteristics of different propagation environments and estimate the influence of unknown components like traffic or vegetation by adapting model parameters to measurements.
Beam tracing can be used for solving global illumination problems. It is an efficient algorithm, and performs very well when implemented on the GPU. This allows us to apply the algorithm in a novel way to the problem of radio wave propagation. The simulation of radio waves is conceptually analogous to the problem of light transport. However, their wavelengths are of proportions similar to that of the environment. At such frequencies, waves that bend around corners due to diffraction are becoming an important propagation effect. In this paper we present a method which integrates diffraction, on top of the usual effects related to global illumination like reflection, into our beam tracing algorithm. We use a custom, parallel rasterization pipeline for creation and evaluation of the beams. Our algorithm can provide a detailed description of complex radio channel characteristics like propagation losses and the spread of arriving signals over time (delay spread). Those are essential for the planning of communication systems required by mobile network operators. For validation, we compare our simulation results with measurements from a real world network.
We present a novel method for efficient computation of complex channel characteristics due to multipath effects in urban microcell environments. Significant speedups are obtained compared to state-of-the-art ray-tracing algorithms by tracing continuous beams and by using parallelization techniques. We optimize simulation parameters using on-site measurements from real world networks. We formulate the adaption of model parameters as a constrained least-squares problem where each row of the matrix corresponds to one measurement location, and where the columns are formed by the beams that reach the respective location.
Interactive global illumination for fully deformable scenes with dynamic relighting is currently a very elusive goal in the area of realistic rendering. In this work we propose a highly efficient and scalable system that is based on explicit visibility calculations. The rendering equation defines the light exchange between surfaces, which we approximate by subsampling. By utilizing the power of modern parallel GPUs using the CUDA framework we achieve interactive frame rates. Since we update the global illumination continuously in an asynchronous fashion, we maintain interactivity at all times for moderately complex scenes. We show that we can achieve higher frame rates for scenes with moving light sources, diffuse indirect illumination and dynamic geometry than other current methods, while maintaining a high image quality.
Updated paper: Small technical fix.
In wireless network planning, much effort is spent on the improvement of the network and transport layer -- especially for Mobile Ad Hoc Networks. Although in principle real-world measurements are necessary for this, their setup is often too complex and costly. Hence good and reliable simulation tools are needed. In this work we present a new physical layer simulation algorithm based on the extension and adaptation of recent techniques for global illumination simulation. By combining and improving these highly efficient algorithms from the field of Computer Graphics, it is possible to build a fast and flexible utility to be used for wireless network simulation. We compute a discrete sampling of the volumetric electromagnetic field by tracing stochastically generated photon paths through the scene. This so called Photon Path Map is then used to estimate the field density at any point in space and also provides local information about the delay spread. The algorithm can be applied to three dimensional indoor as well as outdoor scenarios without any changes and the path-tracing costs scale only logarithmically with the growing complexity of the underlying scene geometry.
The simulation of wireless networks has been an important tool for researchers and the industry in the last years. Especially in the field of Mobile Ad Hoc Networking, most current results have been achieved using simulators. The need for reproducible results and easy to observe environments limits the use of real world measurements for those kind of networks. It is stated here that the radio wave propagation model has a strong impact on the results of the simulation run. This work shows the limitations of current simulation environments and describes a high accuracy propagation model based on the use of a ray-tracer. By using a parallelized preprocessing step we made this propagation model feasible for usage in network simulators. Based on two examples, the effects on characteristic performance properties in Mobile Ad Hoc Networks are shown. We found that the physical layer simulation has a great impact on routing protocol efficiency.
In this work we present a method to visualize the wave propagation mechanisms of wireless networks at interactive rates. The user can move around transmitting nodes and immediately sees the resulting field strength for the complete scenario. This can be used for rapid optimization of antenna placement, or for visualizing the coverage of mobile stations, as they move through a simulation. In a preprocessing step we compute the wave propagation for a distinct set of transmitter positions. Whereas in the visualization phase we use these precomputed maps to do a fast interpolation using a current graphics card.