Optimizing Wi-Fi Channel Selection in a Dense Neighborhood (2022)

By Yonatan Vaizman, Hongcheng Wang; Comcast

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 (like 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.). Existing APs sometimes have smart channel selection features, but because they work independently (the APs do not coordinate), this can cause a cascade of neighboring APs constantly switching channels, which is disruptive to the connectivity of the homes. 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”).

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