Adaptive learning scheme for power control in wireless transmitters

We address the issue of power-controlled shared channel access in future wireless networks supporting packetized data traffic. We formulate this problem using the dynamic programming framework and present a distributed approximate dynamic programming algorithm capable of finding the optimal policy. The main tradeoff facing a transmitter is to balance its current power level with future backlog in the presence of stochastically changing interference. Simulation experiments demonstrate that the algorithm can achieve significant performance gains over the standard power control approach.