  In this paper, we revisit the widely known performance anomaly that results
in severe network utility degradation in WiFi networks when nodes use diverse
modulation and coding schemes. The proportional-fair allocation was shown to
mitigate this anomaly and provide a good throughput to the stations. It can be
achieved through the selection of contention window values based on the
explicit solution of an optimization problem or, as proposed recently, by
following a learning-based approach that uses a centralized gradient descent
algorithm. In this paper, we leverage our recent theoretical work on
asynchronous distributed optimization and propose a simple algorithm that
allows WiFi nodes to independently tune their contention window to achieve
proportional fairness. We compare the throughputs and air-time allocation that
this algorithm achieves to those of the standard WiFi binary exponential
back-off and show the improvements.
