Here, the authors study the novel problem of optimising access point (AP) association by maximising the network throughput, subject to the degree bound of AP. The formulated problem is a combinatorial optimisation. They resort to the Markov Chain approximation technique to design a distributed algorithm. They first approximate their optimal objective via Log-Sum-Exp function. Thereafter, they construct a special class of Markov Chain with steady-state distribution specify to their problem to yield a distributed solution. Furthermore, they extend the static problem setting to a dynamic environment where the users can randomly leave or join the system. Their proposed algorithm has provable performance, achieving an approximation gap of $({1}/{\eta})\log |\mathscr {F}|$(1/η)log⁡|F| . It is simple and can be implemented in a distributed manner. Their extensive simulation results show that the proposed algorithm can converge very fast, and achieve a close-to-optimal performance with a guaranteed loss bound.

中文翻译：

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用于用户随机到达和离开的AP关联的分布式算法

在这里，作者研究了根据接入点的程度限制，通过最大化网络吞吐量来优化接入点（AP）关联的新问题。提出的问题是组合优化。他们诉诸于马尔可夫链近似技术来设计分布式算法。他们首先通过Log-Sum-Exp函数逼近最佳目标。此后，他们构造一类特殊的马尔可夫链，并针对其问题指定稳态分布，以产生分布式解。此外，他们将静态问题设置扩展到动态环境，使用户可以随意离开或加入系统。他们提出的算法具有可证明的性能，达到了$（{{1} / {\ eta}）\ log | \ mathscr {F} | $（1个/η）日志⁡|F| 。它很简单，可以以分布式方式实现。他们广泛的仿真结果表明，所提出的算法可以非常快速地收敛，并在保证损失边界的情况下达到接近最佳的性能。