In this thesis we have proposed a variable-length multiobjective genetic algorithm to solve heterogeneous wireless transmitter placement with multiple constraints. Among many factors that may affect the result of placement, we focus on four major requirements, coverage, cost, data rate demand, and overlap. In the proposed algorithm we release the need for the upper bound number of transmitters that is a major constraint in the existing methods and achieve better wireless transmitter placement while considering the transmitter position and design requirement simultaneously. In experiments, we use the free space propagation model, the large scale propagation model which considers the shadowing effect, and the extended Hata-Okumura model to predict the path loss in a real two dimensional indoor environment, and an outdoor environment and even a real three dimensional outdoor environment. Experimental results show that the proposed algorithm can find many feasible solutions for all test cases under four objectives.

Wireless network placement of transmitters, such as base stations for 2G and 3G, access points for WLAN, is a NP-hard problem, since many factors have to be considered, like QoS, coverage, cost, etc. In wireless network placement problem, the goal is to find a set of transmitters which achieves the widest coverage on a given map and spends the minimal cost. In this thesis, we propose a novel variable-length genetic algorithm for solving this problem. Most of existing methods for solving wireless network placement problem, to our best knowledge, users must assign an upper bound or a total number of transmitters for placement. Unlike these existing methods, the proposed algorithm can search the optimal number of transmitters automatically. In addition, the proposed algorithm can find near optimal solutions even in heterogeneous transmitters placement problem, i.e., transmitters with different power radius or cost. The results on several benchmarks are very close to the optimal solutions, which validate the capability of the proposed method in finding the numbers, the types, are the positions of transmitters in heterogeneous wireless network environment.

The DIRECT (DIviding RECTangles) algorithm JONESJOTi, a variant of Lipschitzian methods for bound constrained global optimization, has been applied to the optimal transmitter placement for indoor wireless systems. Power coverage and BER (bit error rate) are considered as two criteria for optimizing locations of a specified number of transmitters across the feasible region of the design space. The performance of a DIRECT implementation in such applications depends on the characteristics of the objective function, the problem dimension, and the desired solution accuracy. Implementations with static data structures often fail in practice because of unpredictable memory requirements. This is especially critical in S⁴W (Site-Specific System Simulator for Wireless communication systems), where the DIRECT optimization is just one small component connected to a parallel 3D propagation ray tracing modeler running on a 200-node Beowulf cluster of Linux workstations, and surrogate functions for a WCDMA (wideband code division multiple access) simulator are also used to estimate the channel performance. Any component failure of this large computation would abort the entire design process. To make the DIRECT global optimization algorithm efficient and robust, a set of dynamic data structures is proposed here to balance the memory requirements with execution time, while simultaneously adapting to arbitrary problem size. The focus is on design issues of the dynamic data structures, related memory management strategies, and application issues of the DIRECT algorithm to the transmitter placement optimization for wireless communication systems. Results for two indoor systems are presented to demonstrate the effectiveness of the present work. / Master of Science