Machine Learning for Wireless Networks

Dear Colleagues,

The fusion of machine learning with wireless networks has ushered in a new era of innovation and optimization in wireless communication. Machine learning technologies are increasingly being employed to tackle the multifaceted challenges associated with wireless networks, such as spectrum management, interference mitigation and quality of service enhancement. Machine learning equips networks with the capability to adapt and learn from dynamically changing network conditions, leading to more efficient and flexible resource allocation. Machine learning is also instrumental in predicting and preempting network failures and anomalies. This is especially critical for applications like autonomous vehicles and the Internet of Things (IoT). In summary, the integration of machine learning into wireless networks offers a promising solution to the contemporary challenges of wireless communication. As wireless networks continue to evolve, the amalgamation of machine learning and wireless technologies is poised to shape the future of wireless communication.

Therefore, this Special Issue seeks to identify key enabling technologies to support machine learning for wireless networks. These technologies include resource allocation and optimization, interference mitigation, quality of service (QoS) improvement, predictive maintenance, security and anomaly detection, energy-efficient wireless communication, edge computing and so on.

Topics of interests include but are not limited to the following:

1. Resource Allocation and Optimization
2. Spectrum Management and Cognitive Radios
3. Interference Mitigation
4. Quality of Service (QoS) Enhancement
5. Predictive Maintenance and Anomaly Detection
6. Energy-Efficient Wireless Networks
7. Communication Security and Threat Detection
8. Dynamic Network Slicing
9. Adaptive Beamforming and MIMO
10. Network Traffic Analysis and Prediction

Dr. Yong Niu
Guest Editor

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• machine learning
• wireless networks
• wireless communication