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Neural approximation of open-loop feedback rate control in satellite networks

机译:卫星网络中开环反馈速率控制的神经近似

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A resource allocation problem for a satellite network is considered, where variations of fading conditions are added to those of traffic load. Since the capacity of the system is finite and divided in finite discrete portions, the resource allocation problem reveals to be a discrete stochastic programming one, which is typically NP-Hard. In practice, a good approximation of the optimal solution could be obtained through the adoption of a closed-form expression of the performance measure in steady-state conditions. Once we have summarized the drawbacks of such optimization strategy, we address two novel optimization approaches. The first one derives from Gokbayrak and Cassandras and is based on the minimization over the discrete constraint set using an estimate of the gradient, obtained through a "relaxed continuous extension" of the performance measure. The computation of the gradient estimation is based on infinitesimal perturbation analysis (IPA). Neither closed forms of the performance measures, nor additional feedbacks concerning the state of the system and very mild assumptions about the stochastic environment are requested. The second one is the main contribution of the present work, and is based on an open-loop feedback control (OLFC) strategy, aimed at providing optimal reallocation strategies as functions of the state of the network. The optimization approach leads us to a functional optimization problem, and we investigate the adoption of a neural network-based technique, in order to approximate its solution. As is shown in the simulation results, we obtain near-optimal reallocation strategies with a small real time computational effort and avoid the suboptimal transient periods introduced by the IPA gradient descent algorithm.
机译:考虑了卫星网络的资源分配问题,其中衰落条件的变化被添加到业务负载的变化中。由于系统的容量是有限的,并且分为有限的离散部分,因此资源分配问题揭示出是一个离散的随机编程问题,通常是NP-Hard。在实践中,可以通过采用稳态条件下性能度量的封闭形式来获得最佳解决方案的良好近似。一旦我们总结了这种优化策略的弊端,就可以解决两种新颖的优化方法。第一个是从Gokbayrak和Cassandras派生的,它基于使用性能评估的“松弛连续扩展”获得的梯度估计值对离散约束集的最小化。梯度估计的计算基于无穷小扰动分析(IPA)。既不需要封闭形式的性能度量,也不需要有关系统状态的额外反馈以及对随机环境的非常温和的假设。第二个是当前工作的主要贡献,它基于开环反馈控制(OLFC)策略,旨在提供作为网络状态函数的最佳重新分配策略。优化方法导致我们遇到功能优化问题,并且我们研究了基于神经网络的技术的采用,以便对其求解进行近似。如仿真结果所示,我们以较小的实时计算工作量获得了接近最佳的重新分配策略,并避免了IPA梯度下降算法引入的次优瞬态周期。

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