Understanding Cooperative Data Exchange Problem in Multi-Hop Wireless Network

Luo, Guiyang; Liu, Zhihan; Li, Jinglin; Yang, Fangchun

doi:10.1109/lwc.2020.3012441

Cited by 1 publication

(1 citation statement)

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“…A significant step in this strategy involves finding a maximal connected subgraph within the network, giving priority to live media streaming dissemination. An investigation [20] into the code cooperative data exchange (CCDE) problem in multi-channel multi-hop wireless networks adopts the time-expanded graph approach. The CCDE addresses the recovery of desired packets in a connected network [21] and has been proven NP-hard for general topologies [22].…”

Section: Graph Representation and Graph Neural Networkmentioning

confidence: 99%

Unmanned Aerial Vehicle Cooperative Data Dissemination Based on Graph Neural Networks

Xing,

Zhang,

Wang

et al. 2024

Sensors

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Unmanned Aerial Vehicles (UAVs) have critical applications in various real-world scenarios, including mapping unknown environments, military reconnaissance, and post-disaster search and rescue. In these scenarios where communication infrastructure is missing, UAVs will form an ad hoc network and perform tasks in a distributed manner. To efficiently carry out tasks, each UAV must acquire and share global status information and data from neighbors. Meanwhile, UAVs frequently operate in extreme conditions, including storms, lightning, and mountainous areas, which significantly degrade the quality of wireless communication. Additionally, the mobility of UAVs leads to dynamic changes in network topology. Therefore, we propose a method that utilizes graph neural networks (GNN) to learn cooperative data dissemination. This method leverages the network topology relationship and enables UAVs to learn a decision policy based on local data structure, ensuring that all UAVs can recover global information. We train the policy using reinforcement learning that enhances the effectiveness of each transmission. After repeated simulations, the results validate the effectiveness and generalization of the proposed method.

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Section: Graph Representation and Graph Neural Networkmentioning

confidence: 99%