论文标题
在密集的社区中优化Wi-Fi渠道选择
Optimizing Wi-Fi Channel Selection in a Dense Neighborhood
论文作者
论文摘要
在茂密的社区中，通常有数十所房屋紧邻。这可以是一个带有许多单户住宅（SFHS）的紧身城市街区，也可以是一个多个住宅（MDU）综合体（例如大公寓楼或公寓）。这样一个社区中的每个房屋（SFH或MDU中心中的一个单元）都有其自己的Wi-Fi接入点（AP）。由于Wi-Fi的非重叠无线电频道（通常为2或3）很少，因此邻近的房屋可能会发现自己共享一个频道并在通话时间上竞争，这可能会导致较差的互联网经历（长期延迟，在流式传输电影等时进行缓冲等）。高度希望提供最佳用户体验的密集社区中所有APS的Wi-Fi优化。 我们提出了一种以集中式方式选择Wi-Fi通道的方法，以适用于密集的社区中的所有AP。我们描述了如何使用最近的观测值来估计潜在的pain矩阵 - 对于每对AP，如果它们在同一通道上，它们会互相造成多少Wi-Fi pain。我们制定了一个优化问题 - 查找通道分配（每个房屋应使用的通道），以最大程度地减少附近的Wi-Fi-Pain。我们设计了一种优化算法，该算法使用神经网络上的梯度下降来解决优化问题。我们描述了离线实验的初始结果，将我们的优化求解器与现成的混合组编程求解器进行了比较。在我们的实验中，我们表明，现成的求解器设法在火车数据（从最近几天开始）中找到了更好的（较低的总疼痛）解决方案，但是我们的神经网络求解器可以更好地概括 - 它发现了一种解决测试数据（明天）的总疼痛的解决方案。
In dense neighborhoods, there are often dozens of homes in close proximity. This can either be a tight city-block with many single-family homes (SFHs), or a multiple dwelling units (MDU) complex (such as a big apartment building or condominium). Each home in such a neighborhood (either a SFH or a single unit in a MDU complex) has its own Wi-Fi access point (AP). Because there are few (typically 2 or 3) non-overlapping radio channels for Wi-Fi, neighboring homes may find themselves sharing a channel and competing over airtime, which may cause bad experience of slow internet (long latency, buffering while streaming movies, etc.). Wi-Fi optimization over all the APs in a dense neighborhood is highly desired to provide the best user experience. We present a method for Wi-Fi channel selection in a centralized way for all the APs in a dense neighborhood. We describe how to use recent observations to estimate the potential-pain matrix - for each pair of APs, how much Wi-Fi-pain would they cause each other if they were on the same channel. We formulate an optimization problem - finding a channel allocation (which channel each home should use) that minimizes the total Wi-Fi-pain in the neighborhood. We design an optimization algorithm that uses gradient descent over a neural network to solve the optimization problem. We describe initial results from offline experiments comparing our optimization solver to an off-the-shelf mixed-integer-programming solver. In our experiments we show that the off-the-shelf solver manages to find a better (lower total pain) solution on the train data (from the recent days), but our neural-network solver generalizes better - it finds a solution that achieves lower total pain for the test data (tomorrow).
