6.1 Introduction

In Chapter 4, we discussed the viability of GC as a low-cost, high-performance computing strategy. An FDTD analysis of human-antenna interaction problems was the field trial. In Chapter 5, we moved towards a more complex and challenging goal, the CAE of aperture-antenna arrays. The development of such an environment casts problems of supercomputing as well as cooperative engineering. It has been demonstrated that GC is an answer suited to both requirements.

In the current chapter, we move toward a third area of application the design, management, and planning of wireless radio base station (BS) networks. Indeed, the impressive progress in wireless systems and services is compelling operators to reduce the time needed to design the network, improve the quality of service, and better control human exposure to EM fields. This implies a strong demand for automatic tools that optimize the critical parameters (e.g., base station locations, BS power levels, and antenna tilting), allow a rigorous prediction of the fields radiated by base station antennas, and provide an easy interaction with the end user. These goals require a multidisciplinary approach involving the use of radio propagation (RP) models, optimization methods, and sophisticated software technologies. Once again, several needs arise: supercomputing strategies, cooperative engineering (already addressed in Chapter 4 and 5, respectively), and real-time data communication and management in a geographical distributed system.

In this chapter it is shown that GC is the suitable answer to these three needs. As supercomputing and cooperative engineering with GC have...