  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).

Networking and Internet Architecture;Signal Processing