Context-aware Vertical Handover for Reliable Beyond 5G Wireless Heterogeneous Networks with Embedded Generative Adversarial Network and Reinforced Bayesian Network
Abstract
Wireless communication technologies are advancing to provide the exponentially increasing mobile users with higher data rates and to support new applications that incorporate not only human but also machine-to-machine communications. Machine learning and its counterpart, the deep-learning have become essential techniques to be adopted to solve the more complex nature of the communication world. The focused solutions are intelligent and self-adaptive systems to address the growing number of connected heterogeneous devices and technologies. However, compared to other applications, intelligent networking and communication systems still are not fully materialized due to the various complexities such as the quality and size of data to represent the heterogeneous scenarios. To materialize the intelligence demand of the future systems beyond 5G, compatibility of chosen intelligent algorithms and systems are important to be set up from the training using a reliable dataset. In mobility management specifically, the focus of this project will be synthetic dataset generation and the optimized vertical handover selection.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Nov 17, 2022
- Source ID
- FA23862114073
Entities
People
- Asma Abu-Samah
Organizations
- Air Force Office of Scientific Research
- National University of Malaysia
- United States Air Force