title: A fuzzy reinforcement learning approach to power control in wireless transmitters.
abstract: We address the issue of power-controlled shared channel access in wireless networks supporting packetized data traffic. We formulate this problem using the dynamic programming framework and present a new distributed fuzzy reinforcement learning algorithm (ACFRL-2) capable of adequately solving a class of problems to which the power control problem belongs. Our experimental results show that the algorithm converges almost deterministically to a neighborhood of optimal parameter values, as opposed to a very noisy stochastic convergence of earlier algorithms. 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 ACFRL-2 algorithm achieves significant performance gains over the standard power control approach used in CDMA2000. Such a large improvement is explained by the fact that ACFRL-2 allows transmitters to learn implicit coordination policies, which back off under stressful channel conditions as opposed to engaging in escalating "power wars."
authors.affiliation: Sun Microsystems Laboratories, Sunnyvale, CA 94086, USA. dive1743@yahoo.com
authors.forename: David
authors.initials: D
authors.lastname: Vengerov
authors.forename: Nicholas
authors.initials: N
authors.lastname: Bambos
authors.forename: Hamid R
authors.initials: HR
authors.lastname: Berenji
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D040421:Cell Phone; D003198:Computer Simulation; D017143:Fuzzy Logic; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*