Use of evolutionary computational algorithms for optimization of indoor wireless communication network layouts.

摘要

The current explosive growth in Radio Frequency Data Communications (RFDC) devices and applications has given rise to a number of challenging implementation issues. A predominant problem in successful implementation of these applications is the quick and efficient determination of transceiver placement such that effective radio communication can take place. This research addresses this problem through the development of a computerized layout simulation system incorporating heuristic optimization methods as the placement engine for transceivers. This research is the first known use of circle covering heuristics and genetic algorithms to incorporate the concepts of active interference and capacity in the calculation of transceiver placement.; The theoretical roots of the wireless network layout problem lie in the domain of geometry and the study of circle coverings, in that, barring obstructions and interference, the coverage region of a transmitter unit using an isotropic antennae source is circular or spherical in nature. Current placement of transceivers relies on methods that are either entirely manual or computationally prohibitive to perform in a timely manner. Using evolutionary computation techniques, information characterizing the facility to be covered with transmitters is entered into the system and analyzed. Facility characterizations can then be analyzed to provide suggested solutions. The goal of this research is to demonstrate that evolutionary computational techniques utilizing first order principles of radio propagation are a viable method by which solutions to transceiver placement problems can be generated, and that solutions generated by this tool and methodology are better than current methods of transceiver placement. Further, the use of this tool will result in a reduction in the time and effort needed to effectively locate transceivers in a particular environment and an ability to generate multiple solutions of near equal viability.