Fast reinforcement learning algorithm for mobile power control in cellular communication systems

In this paper, a fast reinforcement learning algorithm based on the Muller's method is first proposed. This new algorithm converges much faster than the conventional approach, and therefore is more suitable to be used in on-line applications. We apply the fast reinforcement learning algorithm into the power control of cellular phones. The channel tracking error can be minimized in our mobile power control scheme. Simulation experiments demonstrate that the harmful deep fading is greatly compensated and the response overshoot is small.