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.

Wi-Fi 网络中，对于 Restricted access window（RAW），通过用户分组和周期性时隙分配来管理竞争和干扰。该研究提出了将用户分组建模为图构建问题，通过优化图的最大切断来获得最优的分组决策，并设计了一种用于构建最优图的 actor-critic 图表示学习（AC-GRL）算法，以提高网络的最坏情况用户吞吐量。

无线网络中争用和干扰管理的图表示学习

Graph Representation Learning for Contention and Interference Management  in Wireless Networks

Interference among concurrent transmissions in a wireless network is a key
factor limiting the system performance. One way to alleviate this problem is to
manage the radio resources in order to maximize either the average or the
worst-case performance. However, joint consideration of both metrics is often
neglected as they are competing in nature. In this article, a mechanism for
radio resource management using multi-agent deep reinforcement learning (RL) is
proposed, which strikes the right trade-off between maximizing the average and
the $5^{th}$ percentile user throughput. Each transmitter in the network is
equipped with a deep RL agent, receiving partial observations from the network
(e.g., channel quality, interference level, etc.) and deciding whether to be
active or inactive at each scheduling interval for given radio resources, a
process referred to as link scheduling. Based on the actions of all agents, the
network emits a reward to the agents, indicating how good their joint decisions
were. The proposed framework enables the agents to make decisions in a
distributed manner, and the reward is designed in such a way that the agents
strive to guarantee a minimum performance, leading to a fair resource
allocation among all users across the network. Simulation results demonstrate
the superiority of our approach compared to decentralized baselines in terms of
average and $5^{th}$ percentile user throughput, while achieving performance
close to that of a centralized exhaustive search approach. Moreover, the
proposed framework is robust to mismatches between training and testing
scenarios. In particular, it is shown that an agent trained on a network with
low transmitter density maintains its performance and outperforms the baselines
when deployed in a network with a higher transmitter density.

提出了一种使用多智能体深度强化学习的机制来管理无线电资源，从而在最大化用户吞吐量和保证公平分配资源之间取得平衡。