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首页> 外文期刊>IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics >A fuzzy reinforcement learning approach to power control in wireless transmitters
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A fuzzy reinforcement learning approach to power control in wireless transmitters

机译：无线发射机功率控制的模糊强化学习方法

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摘要

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".
机译：我们解决了支持分组数据流量的无线网络中功率控制的共享信道访问的问题。我们使用动态规划框架来表述此问题，并提出一种新的分布式模糊强化学习算法（ACFRL-2），该算法能够充分解决功率控制问题所属的一类问题。我们的实验结果表明，该算法几乎确定性地收敛到最佳参数值附近，而不是早期算法的非常嘈杂的随机收敛。发射机面临的主要折衷是在存在随机变化干扰的情况下平衡其当前功率水平与将来的积压。仿真实验表明，与CDMA2000中使用的标准功率控制方法相比，ACFRL-2算法可显着提高性能。 ACFRL-2允许发射机学习隐式协调策略这一事实解释了如此大的改进，该隐式协调策略在紧张的信道条件下会退缩，而不是参与不断升级的“大战”。

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