Model-Knowledge Mutual Reinforcement for Few-Shot Cellular Network Fault Diagnosis

IF 7.4 1区 计算机科学 Q1 TELECOMMUNICATIONS IEEE Transactions on Cognitive Communications and Networking Pub Date : 2024-11-11 DOI:10.1109/tccn.2024.3494742
Chengyong Liu, Kun Zhu, Jianpeng Li, Yang Zhang, Dusit Niyato
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模型知识相互强化技术用于少镜头蜂窝网络故障诊断
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来源期刊
IEEE Transactions on Cognitive Communications and Networking
IEEE Transactions on Cognitive Communications and Networking Computer Science-Artificial Intelligence
CiteScore
15.50
自引率
7.00%
发文量
108
期刊介绍: The IEEE Transactions on Cognitive Communications and Networking (TCCN) aims to publish high-quality manuscripts that push the boundaries of cognitive communications and networking research. Cognitive, in this context, refers to the application of perception, learning, reasoning, memory, and adaptive approaches in communication system design. The transactions welcome submissions that explore various aspects of cognitive communications and networks, focusing on innovative and holistic approaches to complex system design. Key topics covered include architecture, protocols, cross-layer design, and cognition cycle design for cognitive networks. Additionally, research on machine learning, artificial intelligence, end-to-end and distributed intelligence, software-defined networking, cognitive radios, spectrum sharing, and security and privacy issues in cognitive networks are of interest. The publication also encourages papers addressing novel services and applications enabled by these cognitive concepts.
期刊最新文献
An Intelligent Game-Based Anti-Jamming Solution Using Adversarial Populations for Aerial Communication Networks Federated Learning-Aided Beam Prediction for Multi-User Millimeter Wave Communications Model-Knowledge Mutual Reinforcement for Few-Shot Cellular Network Fault Diagnosis A Novel Neural Network Approach to Proactive 3-D Beamforming CRissNet: An Efficient and Lightweight Network for CSI Feedback in Massive MIMO Systems
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