Prediction of material property using optimized augmented graph-attention layer in GNN

Sathana, V.; Mathumathi, M.; Makanyadevi, K.

doi:10.1016/j.matpr.2022.09.500

Cited by 10 publications

(1 citation statement)

References 16 publications

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“…Within a GNN, each graph node is linked with a feature vector that encapsulates the node's characteristics. These vectors are processed through multiple layers of the neural network, each layer modifying the vectors according to the attributes of the neighboring nodes in the graph [14]. This mechanism empowers GNNs to gather and disseminate information across the graph, thereby facilitating reasoning about inter-entity relationships.…”

Section: Wireless Networkmentioning

confidence: 99%

DQN-GNN-Based User Association Approach for Wireless Networks

Alablani,

Alenazi

2023

Mathematics

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In the realm of advanced mobile networks, such as the fifth generation (5G) and beyond, the increasing complexity and proliferation of devices and unique applications present a substantial challenge for User Association (UA) in wireless systems. The problem of UA in wireless networks is multifaceted and requires comprehensive exploration. This paper presents a pioneering approach to the issue, integrating a Deep Q-Network (DQN) with a Graph Neural Network (GNN) to enhance user-base station association in wireless networks. This novel approach surpasses recent methodologies, including Q-learning and max average techniques, in terms of average rewards, returns, and success rate. This superiority is attributed to its capacity to encapsulate intricate relationships and spatial dependencies among users and base stations in wireless systems. The proposed methodology achieves a success rate of 95.2%, outperforming other methodologies by a margin of up to 5.9%.

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Section: Wireless Networkmentioning

confidence: 99%