Stochastic power control for wireless networks: Probabilistic QoS measures

Abstract

For wireless network systems, iterative power control algorithms have been proposed to minimize the transmission power, while maintaining reliable communication and base stations. However, since the measurements are random, the channel characteristics always are described by Stochastic Differential Equations (SDE). Based on the stochastic approximation methods, and using time-varying step size sequences, we can get an approximation algorithm to reach an optimal power allocation. After the study of optimal power allocation, the probabilistic Quality of Service (QoS) measures are introduced to evaluate the performance of any control strategy. It provides tight bounds that relate to the probability of failure in achieving the desired QoS requirements. This thesis addresses mobile systems consisting of M transmitters and M receivers, which are subject to motion, and their power is described by SDE. The optimal power control problem is formulated, and the outage probability corresponding to a desired QoS requirements is computed using Moment Generating Function (MGF). Numerical results show that each user needs only to know its own channel gain and its own output assigned by the base station to update the transmitter power in order to maintain a desired Signal to Interference Ratio (SIR) and QoS requirement at the receiver.