Hybrid Graph Signal Processing Deep Learning Framework for Adaptive Network Topology Optimization

Résumé fourni par la source

The optimization of adaptive network topology is one of the significant problems in the contemporary communication systems because network conditions are dynamic, scale is growing, and data patterns are heterogeneous. This article suggests a new Hybrid Graph Signal Processing-Deep Learning (HGSP-DL) architecture of efficient and intelligent topology optimization in complex network settings. The proposed model is a combination of the structural benefit of graph signal processing and the representation learning ability of deep neural networks to attain strong and scalable performance. The network is first represented as a graph, and the features of nodes are calculated together with the connections to provide smoothness in the spectral analysis of the network, based on the normalized graph Laplacian. A trainable spectral filtering method is used to obtain structure-sensitive embeddings, whereas a deep learning encoder learns non-linear correlations between network nodes. These complementary representations are merged to create a single embedding, which is also used to adaptively optimize network topology by an optimization process that is data-driven. The framework uses an iterative feedback process in order to optimize the connectivity pattern and enhance convergence. Ample simulations have shown that the proposed HGSP-DL model has made great gains in accuracy, packet delivery ratio, and energy savings as well as minimizing latency and convergence time in contrast to current approaches. This model is also highly scaled and can withstand dynamic network conditions such as node mobility and node failures. The findings confirm the suitability of the proposed solution as a dependable solution towards intelligent communication networks of the next generation.

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