Performance assessment of a novel prediction-based resource borrowing strategy suitable for high capacity mobile cellular networks

In this paper a novel and efficient resource assignment scheme for cellular mobile networks is presented, which is dynamically adapted with time and spatial variations of offered traffic. Resource borrowing in advance (RBIA), without requiring system-wide information and only by relying on local information about near co-channels of the resource-requesting cell, selects the optimum choice among candidate resources for borrowing through utilizing a cost function based on estimation of future variation rate of offered traffic. Also, usage of resource reassignment concept has further enhanced teletraffic performance of RBIA. In order to obtain as realistic as possible numerical results regarding teletraffic performance of RBIA, an initiative simulation environment using discrete-event model traffic simulator notion has been developed and exploited, which takes into account users´ mobility effect on performance evaluation process.