Optimizing FBS 3D positions for sum rate maximization in downlink NOMA 6G network

Area of Science:

• Wireless Communication
• Network Engineering
• Optimization Algorithms

Background:

• The proliferation of Internet of Everything (IoE) devices presents significant challenges for next-generation wireless networks, demanding ultra-reliable, high-capacity connectivity.
• Flying Base Stations (FBSs) enhance vehicular communication but their mobility and that of IoE devices cause communication link disruptions, degrading performance.
• Strategic FBS positioning is crucial to mitigate these disruptions and ensure consistent connectivity.

Purpose of the Study:

• To maximize the total sum rate (TSR) in downlink 6G networks by optimizing the three-dimensional (3D) positions of multiple FBSs serving IoE devices.
• To minimize inter/intra-cluster interference through intelligent FBS placement, ensuring FBSs remain centered relative to dynamic IoE device locations.
• To introduce a novel transmission structure employing Non-Orthogonal Multiple Access (NOMA) at each FBS for efficient IoE device service.

Main Methods:

• Developed a transmission structure utilizing multi-FBS with Non-Orthogonal Multiple Access (NOMA) for downlink 6G networks.
• Formulated a non-convex optimization problem to determine the optimal 3D positions of FBSs to maintain centrality to IoE devices.
• Employed a genetic algorithm-based evolutionary approach to solve the complex FBS positioning optimization problem.
• Implemented a successive interference cancellation (SIC) strategy to manage NOMA-SIC among IoE devices.

Main Results:

• The proposed genetic algorithm-based optimization significantly improved the Total Sum Rate (TSR) compared to existing methods.
• Achieved up to 21.03% higher TSR compared to state-of-the-art approaches including Annealing, block coordinate descent, modified gray wolf optimization, and center-of-cluster.
• Demonstrated the effectiveness of optimizing FBS 3D positions for maintaining connectivity and enhancing network performance.

Conclusions:

• Optimizing the 3D positions of FBSs is a highly effective strategy for improving wireless communication performance in 6G networks with numerous IoE devices.
• The proposed multi-FBS NOMA transmission structure, coupled with the genetic algorithm optimization and SIC, offers a robust solution for enhancing TSR and connectivity.
• The findings highlight the potential of intelligent base station placement for overcoming mobility-induced challenges in future wireless systems.