Machine Learning in Wireless Networks
Machine learning (ML) is revolutionizing wireless network design and optimization. This track explores how ML techniques such as supervised learning, reinforcement learning, and deep neural networks are applied to manage wireless resources, optimize routing, predict traffic patterns, and enhance Quality of Service (QoS). Special emphasis will be placed on real-time decision-making and autonomous network management.
Case studies will demonstrate applications in predictive maintenance, dynamic spectrum access, and user behavior modeling. The session also considers the challenges of integrating ML into low-power and latency-sensitive environments. Researchers and developers are encouraged to present innovations that bridge the gap between AI theory and practical wireless communication solutions.
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Machine Learning in Wireless Networks Conference Speakers
Recommended Sessions
- 5G and Beyond: Innovations & Challenges
- 6G Wireless Systems and Emerging Standards
- Aerospace Communication Technologies
- AI-Driven Network Optimization
- Cognitive Radio and Spectrum Sharing
- Emergency Communication and Disaster Response Networks
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- Satellite-Ground Station Integration and Management
- Telecommunication Infrastructure and Cloud Integration
- Underwater Wireless Communication
- Vehicular Ad-Hoc Networks (VANETs)
- Wireless Communication for Smart Cities
- Wireless Network Security and Privacy
- Wireless Power Transmission and Energy Harvesting
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