Maximizing Connectivity of Uplink RIS-Assisted UAV Networks

2026-06-09

AI Summary

This work addresses the problem of enhancing network connectivity—quantified by the Fiedler value—while satisfying signal-to-interference-plus-noise ratio (SINR) constraints for user equipment in reconfigurable intelligent surface (RIS)-assisted unmanned aerial vehicle (UAV) uplink networks. To this end, the paper proposes a novel joint optimization framework that integrates RIS link selection, RIS partitioning, and UAV positioning. Specifically, a perturbation-based RIS link selection strategy is devised, closed-form solutions for RIS partitioning are derived to optimize SINR in single-UAV scenarios, and UAV placement is formulated as a low-complexity semidefinite programming (SDP) problem. Simulation results demonstrate that the proposed scheme significantly outperforms existing benchmarks, effectively improving network connectivity while meeting the required SINR constraints.

Abstract

In this paper, we present a new approach for unmanned aerial vehicle (UAV) positioning and reconfigurable intelligent surface (RIS) partitioning to enhance connectivity of uplink RIS-assisted UAV networks. To achieve this, our approach optimizes RIS-aided link selection, RIS partitioning, and UAV positions to maximize network connectivity characterized by its Fiedler value. Meanwhile, it maintains a specific signal-to-interference plus noise ratio (SINR) constraint for user equipment (UE), which is influenced by RIS partitioning and UAV reliability. The network connectivity optimization problem is formulated using the Fiedler value subject to RIS elements allocation and SINR constraints. This problem is a computationally expensive combinatorial optimization, necessitating an efficient iterative approach. In particular, we propose a perturbation method for RIS-aided link selection, and derive a closed-form solution for RIS partitioning, with each partition tailored to optimize SINR for individual UAV. For the given RIS-aided links and RIS partitioning, we then show that the problem of UAV positioning can be formulated as a low complexity semi-definite programming (SDP) optimization problem, which can be solved using off-the-shelf CVX solvers. Our simulations show the potential gain of UAV positioning and RIS partitioning compared to the benchmark schemes from the literature.

Problem

• UAV networks
• Reconfigurable Intelligent Surface (RIS)
• Network connectivity
• Fiedler value
• SINR constraint

Innovation

• RIS partitioning
• UAV positioning
• Fiedler value
• SINR constraint
• semi-definite programming