Radio Map-Based Cognitive Satellite-UAV Networks Towards 6G On-Demand Coverage

Feng, W.; Lin, Y.; Wang, Y.; Wang, J.; Chen, Y.; Ge, N.; Jin, S.; Zhu, H.

Abstract

The sixth generation (6G) network is envisioned to cover remote areas, with the help of satellites and unmanned aerial vehicles (UAVs). Considering the vastness of remote areas and the sparsity of users therein, we investigate a cognitive satellite-UAV network, where satellites and UAVs coordinately share spectrum to provide low-rate and high-rate services in a complementary manner. Multiple UAVs form a virtual antenna array to serve unevenly distributed users via multiple-input-multiple-output (MIMO) non-orthogonal multiple access (NOMA). An on-demand coverage framework is proposed so as to dynamically focus the communication resources on target users. In the framework, a radio map recording the slowly-varying large-scale channel state information (CSI) is utilized. Different from traditional pilot-based approaches, the large-scale CSI is obtained by a lookup in the radio map per the position information of users and UAVs, during the online optimization of the network. In this way, the system overhead could be largely reduced. To explore the potential gain of such a framework, we formulate a joint power allocation problem to maximize the minimum user rate, which is not only non-convex but also with implicit expressions. We recast the problem after uncovering its mathematical characteristics, and derive its locally-optimal solution in an iterative manner. Simulation results corroborate that the proposed framework can significantly improve the coverage performance at a low cost.

Citation

Feng, W., Lin, Y., Wang, Y., Wang, J., Chen, Y., Ge, N., Jin, S., & Zhu, H. (2024). Radio Map-Based Cognitive Satellite-UAV Networks Towards 6G On-Demand Coverage. IEEE Transactions on Cognitive Communications and Networking, 10(3), 1075-1089. https://doi.org/10.1109/TCCN.2023.3345857

• Journal Article Type: Article
• Acceptance Date: Dec 13, 2023
• Online Publication Date: Dec 22, 2023
• Publication Date: 2024-06
• Deposit Date: Dec 20, 2023
• Publicly Available Date: Jan 10, 2024
• Journal: IEEE Transactions on Cognitive Communications and Networking
• Electronic ISSN: 2332-7731
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 10
• Issue: 3
• Pages: 1075-1089
• DOI: https://doi.org/10.1109/TCCN.2023.3345857
• Public URL: https://durham-repository.worktribe.com/output/2048656