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Global Convergence of An Iterative Gradient Algorithm for The Nash Equilibrium in An Extended OSNR Game

机译:扩展OSNR游戏中纳什均衡迭代梯度算法的全局融合

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This paper considers the problem of optical signal-to-noise ratio (OSNR) optimization with link capacity constraints within a Nash game framework. In optical wavelength-division multiplexed (WDM) networks, all wavelength-multiplexed channels share the optical fiber. Even when individually channel parameters are adjusted, the total launched power has to be limited below the nonlinearity threshold. This can be regarded as the optical link capacity constraint. In our previous work in [1], we proposed an extended OSNR Nash game. Channel utility has been related to OSNR and the status of the optical link has been considered directly in channel cost function. The difficulty is that the unique Nash equilibrium (NE) solution of this OSNR Nash game is highly nonlinear and thus analytically intractable. The main contribution of this paper is to develop an iterative, distributed gradient algorithm towards finding the NE solution. The algorithm uses only local measurements and the current load of the network (or link). We prove that the iterative gradient algorithm converges globally to this NE solution under sufficient conditions.
机译:本文认为光学信噪比(OSNR)优化与NASH游戏框架中的链路容量约束的问题。在光波频多路复用(WDM)网络中,所有波长多路复用信道共享光纤。即使在调整单独的频道参数时,总发射功率也必须受到低于非线性阈值的限制。这可以被视为光链路容量约束。在我们以前的工作中[1]中,我们提出了一个扩展的OSNR NASH游戏。通道实用程序与OSNR相关,并且光链路的状态已直接考虑在信道成本函数中。难度是,这种OSNR NASH游戏的独特纳什均衡(NE)解决方案是高度非线性的,因此是分析性难以相容的。本文的主要贡献是开发迭代分布式梯度算法,朝向查找网元解决方案。该算法仅使用本地测量和网络(或链接)的当前负载。我们证明,在充分条件下,迭代梯度算法将全局收敛到该NE解决方案。

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