Graph Representation Learning for Contention and Interference Management
  in Wireless Networks
Zhouyou Gu Branka Vucetic Kishore Chikkam Pasquale Aliberti Wibowo Hardjawana
Restricted access window (RAW) in Wi-Fi 802.11ah networks manages contention
and interference by grouping users and allocating periodic time slots for each
group's transmissions. We will find the optimal user grouping decisions in RAW
to maximize the network's worst-case user throughput. We review existing user
grouping approaches and highlight their performance limitations in the above
problem. We propose formulating user grouping as a graph construction problem
where vertices represent users and edge weights indicate the contention and
interference. This formulation leverages the graph's max cut to group users and
optimizes edge weights to construct the optimal graph whose max cut yields the
optimal grouping decisions. To achieve this optimal graph construction, we
design an actor-critic graph representation learning (AC-GRL) algorithm.
Specifically, the actor neural network (NN) is trained to estimate the optimal
graph's edge weights using path losses between users and access points. A graph
cut procedure uses semidefinite programming to solve the max cut efficiently
and return the grouping decisions for the given weights. The critic NN
approximates user throughput achieved by the above-returned decisions and is
used to improve the actor. Additionally, we present an architecture that uses
the online-measured throughput and path losses to fine-tune the decisions in
response to changes in user populations and their locations. Simulations show
that our methods achieve $30\%\sim80\%$ higher worst-case user throughput than
the existing approaches and that the proposed architecture can further improve
the worst-case user throughput by $5\%\sim30\%$ while ensuring timely updates
of grouping decisions.