Robust Multidimensional Graph Neural Networks for Signal Processing in Wireless Communications With Edge-Graph Information Bottleneck

Signal processing is crucial for satisfying the high data rate requirements of future sixth-generation (6G) wireless networks. However, the rapid growth of wireless networks has brought about massive data traffic, which hinders the application of traditional optimization theory-based algorithms. Mea...

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Veröffentlicht in:	IEEE transactions on signal processing Jg. 73; S. 2688 - 2703
Hauptverfasser:	Liu, Ziheng, Zhang, Jiayi, Zhu, Yiyang, Shi, Enyu, Ai, Bo
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Apexes Communications traffic Edge-graph information bottleneck Graph neural networks Graph theory Interference multidimensional graph neural networks Neural networks Optimization Representations robust Robustness Signal processing Signal processing algorithms Silicon Task complexity Training Wireless communication Wireless communications Wireless networks
ISSN:	1053-587X, 1941-0476
Online-Zugang:	Volltext
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