We propose a distributed learning algorithm for the resource allocation problem in Device-to-Device (D2D) wireless networks that takes into account the throughput estimation noise. We first formulate a stochastic optimization problem with the objective of maximizing the generalized alpha-fair function of the network. In order to solve it distributively, we then define and use the framework of noisy-potential games. In this context, we propose a Binary Log-linear Learning Algorithm (BLLA), which is distributed across cells and converges to a Nash equilibrium of the resource allocation game. This equilibrium is also an optimal for the resource allocation optimization problem. A key enabler for the analysis of the convergence are the proposed rules for computation of resistance of trees of perturbed Markov chains. The convergence of BLLA is proved for bounded and unbounded noise, with fixed and decreasing temperature parameter. A sufficient number of estimation samples is also provided that guarantees the convergence to an optimal state in a single cell scenario and close to an optimal state in a multi-cell scenario. We assess the performance of BLLA by extensive simulations by considering both bounded and unbounded noise cases and show that BLLA achieves higher sum data rate compared to the state-of-the-art.

中文翻译：

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D2D 网络中用于资源分配的噪声潜在博弈中的分布式学习

我们针对设备到设备 (D2D) 无线网络中的资源分配问题提出了一种分布式学习算法，该算法考虑了吞吐量估计噪声。我们首先制定一个随机优化问题，目标是最大化网络的广义 alpha 公平函数。为了分布式地解决这个问题，我们定义并使用了潜在噪声博弈的框架。在这种情况下，我们提出了一种二元对数线性学习算法 (BLLA)，它分布在多个单元格中并收敛到资源分配博弈的纳什均衡。这种均衡也是资源分配优化问题的最优解。收敛性分析的一个关键推动因素是用于计算扰动马尔可夫链树的阻力的拟议规则。证明了 BLLA 的收敛性适用于有界和无界噪声，具有固定和递减的温度参数。还提供了足够数量的估计样本，保证在单小区场景下收敛到最优状态，在多小区场景下接近最优状态。我们通过考虑有界和无界噪声情况的广泛模拟来评估 BLLA 的性能，并表明与最先进的技术相比，BLLA 实现了更高的总数据速率。