时变环境下的动态频谱接入：分布式学习超越期望优化
Dynamic Spectrum Access in Time-varying Environment: Distributed Learning Beyond Expectation Optimization
摘要：
研究了信道状态时变的规范无线网络中的动态频谱接入问题。在大多数现有的工作中，常用的优化目标是最大化某个度量的期望（例如，吞吐量或可达到的速率）。然而，人们认识到，仅仅期望是不够的，因为一些应用程序对波动很敏感。有效容量是一个很有前途的度量时变服务过程，因为它表征的分组延迟违反概率（被视为一个重要的统计QoS指标），通过考虑不仅期望，但也考虑其他高阶统计。因此，我们将时变环境中用户之间的交互表示为非合作博弈，其中效用函数定义为所达到的有效容量。证明了这是一个序势博弈，它至少有一个纯策略纳什均衡。基于一个近似效用函数，我们提出了一个多智能体学习算法，证明了该算法可以在动态和不完全信息约束下获得稳定解。仿真结果验证了所提出的学习算法的收敛性。此外，它表明，所提出的多智能体学习算法达到令人满意的性能。
英文摘要：
This article investigates the problem of dynamic spectrum access for canonical wireless networks, in which the channel states are time-varying. In the most existing work, the commonly used optimization objective is to maximize the expectation of a certain metric (e.g., throughput or achievable rate). However, it is realized that expectation alone is not enough since some applications are sensitive to fluctuations. Effective capacity is a promising metric for time-varying service process since it characterizes the packet delay violating probability (regarded as an important statistical QoS index), by taking into account not only the expectation but also other high-order statistic. Therefore, we formulate the interactions among the users in the time-varying environment as a non-cooperative game, in which the utility function is defined as the achieved effective capacity. We prove that it is an ordinal potential game which has at least one pure strategy Nash equilibrium. Based on an approximated utility function, we propose a multi-agent learning algorithm which is proved to achieve stable solutions with dynamic and incomplete information constraints. The convergence of the proposed learning algorithm is verified by simulation results. Also, it is shown that the proposed multi-agent learning algorithm achieves satisfactory performance.
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