DEPARTMENT OF COMPUTER SCIENCE

DOCTORAL DISSERTATION DEFENSE

Candidate: Ajay Rajkumar

Advisor: Ken Perlin

DOCTORAL DISSERTATION DEFENSE

Candidate: Ajay Rajkumar

Advisor: Ken Perlin

**Efficient Computational Model for Energy Propagation in
Geometrically Defined Large Environments**

11:30 a.m., Wednesday, September 8, 1999

12th floor conference room, 719 Broadway

Abstract

Current radio propagation algorithms are very narrowly focused to specific types of input models and do not scale well to an increase in the number of receiver locations or the number of polygons in an input model. In this dissertation, we look at the problem of efficiently computing energy propagation at radio frequencies in a range of geometrically defined environments from a given transmitter location and for various transmitter and receiver characteristics. To achieve this goal, we propose a unified approach to radio propagation for different types of input models and their combinations as well, by representing the geometry as a binary space partitioning tree and broadcasting energy from the source. The approach is both scalable to large input models as well as dynamically adapts to its scale without incurring unreasonable computational cost. The proposed approach is equally effective for acoustic modeling as well.

We present a new adaptive ray-beam tracing algorithm which initially tessellates the surface of a transmitter into four-sided polygons. Each polygon is cast as a beam which avoids arbitrarily large gaps or overlaps between adjacent beams. For fast intersection computation each beam carries information of its medial ray as well. As the computation proceeds a ray-beam is adaptively subdivided depending on various parameters. The proposed algorithm has sublinear time complexity in terms of the number of receiver locations.

Modeling diffraction off an edge of a wedge is important to compute radio signal that reaches the shadow region of the wedge. Storing these edges explicitly in a data structure can be very expensive for large input models and especially for terrain-based models that have significant elevation variations. We present a new runtime edge-detection algorithm instead of storing the edges statically and its adaptation to binary space partitioning tree represented environments.

We have developed a propagation prediction system called *Propagate* using
these algorithms with good statistical correlation between predicted and measured
results for a number of different input models. The proposed algorithms have been
used to model several other important computations related to a cellular network
of transmitters such as signal strength and path loss, delay spread, angular spread,
carrier-to-interference ratio, and modeling of different antenna diversity schemes.