arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2402.00879cs.NIcs.LGeess.SP
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无线网络中用于竞争与干扰管理的图表示学习

Graph Representation Learning for Contention and Interference Management in Wireless Networks

the University of Sydney(悉尼大学);Morse Micro(莫尔斯微公司)

Zhouyou Gu, Branka Vucetic, Kishore Chikkam, Pasquale Aliberti, Wibowo Hardjawana

AI总结:

针对Wi-Fi 802.11ah RAW用户分组问题,本文将其建模为图最大割并提出AC-GRL算法及在线微调架构,显著提升最坏情况用户吞吐量。

摘要:

Wi-Fi 802.11ah网络中的限制接入窗口(RAW)通过对用户分组并为每组传输分配周期性时隙来管理竞争和干扰。我们旨在找到RAW中的最优用户分组决策,以最大化网络的最坏情况用户吞吐量。我们回顾了现有的用户分组方法,并指出它们在上述问题中的性能局限。我们提出将用户分组表述为一个图构建问题,其中顶点表示用户,边权重表示竞争和干扰。该表述利用图的最大割对用户进行分组,并优化边权重以构建最优图,其最大割可产生最优分组决策。为实现这一最优图构建,我们设计了一种actor-critic图表示学习(AC-GRL)算法。具体而言,actor神经网络(NN)经过训练,利用用户与接入点之间的路径损耗来估计最优图的边权重。图割过程使用半定规划高效求解最大割,并针对给定权重返回分组决策。critic NN近似由上述返回决策实现的用户吞吐量,并用于改进actor。此外,我们提出一种架构,利用在线测量的吞吐量和路径损耗对决策进行微调,以响应用户数量及其位置的变化。仿真表明,与现有方法相比,我们的方法实现了30%~80%更高的最坏情况用户吞吐量,并且所提出的架构可在确保分组决策及时更新的同时,将最坏情况用户吞吐量进一步提高5%~30%。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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