A modified Elman neural network-based power controller in mobile communications systems

Abstract

It has been well recognized that power control is an important technique to combat with the harmful near-far effect as well as increase the maximum user capacity of direct sequence code division multiple access (DS/CDMA) cellular systems. In this paper, we propose a modified Elman neural network (MENN)-based power control scheme, which can regulate the received power level at the base station. Unlike the conventional “bang–bang” and fuzzy logic power control, our MENN-based controller first identifies the inverse dynamical characteristics of mobile channel by adaptive on-line learning. The inverse channel model is then employed for power regulation to reduce large overshoots and shorten long rise time. Simulations show that the fluctuation of controlled received power levels can be smoothed with small channel tracking errors.