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A fuzzy reinforcement learning approach to power control in wireless transmitters

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3 Author(s)
Vengerov, D. ; Sun Microsystems Labs., Sunnyvale, CA, USA ; Bambos, N. ; Berenji, H.R.

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".

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Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on  (Volume:35 ,  Issue: 4 )