Optimization of power control in mobile multimedia communication systems in hospitals using genetic algorithm

The rapid growth in cellular radio communications necessitates more efficient utilisation of spectrum. This is more so in medicine where very large volume of data and information are involved. The increased sharing of spectrum due to this growth translates into a higher likelihood of users interfering with one another. So cell capacity is inherently interference limited, particularly by co-channel interference (CCI) and adjacent channel interference (ACI).

One of the solutions to combat these interferences is to control of power. Two types of power control are used in wireless network communication systems: centralised power control (CPC) and distributed power control (DPC). Centralised power control is computationally very complex for large systems as it assumes that all information about the link gains is available to all mobile devices. DPC, unlike CPC, requires only local information to adjust power levels of each transmitted signal. DPC is therefore more realistic when the number of mobiles grows and will be used as the foundational system model in this work.

Many optimisation techniques have been used for distributed power control system models. However, the techniques have been restricted to the traditional optimisation methods which use the characteristics of the problem to determine the next sampling point. In this work, Genetic Algorithm GA, an evolutionary approach is used. It follows the concept of evolution by stochastically developing generations of power-solution populations based on a fitness score.

It can be seen that the convergence speed of GA is not as fast as that of the conventional method and consequently a higher outage probability. The simulation results show that GA is more robust, more realistic performance, very proactive to noise, fading or shadowing.

A genetic algorithm responds and adapts to this change on the fly but many traditional optimisation procedures must restart afresh which is computationally, expensive.