Maximizing Network Connectivity for UAV Communications via Reconfigurable Intelligent Surfaces

TL;DR

This paper proposes a method to enhance network connectivity in UAV-assisted networks using reconfigurable intelligent surfaces (RISs), optimizing phase shifts to improve communication paths and robustness.

Contribution

It introduces a novel approach to maximize network connectivity in RIS-assisted UAV networks using algebraic connectivity and semi-definite programming optimization.

Findings

Optimized RIS phase shifts improve network connectivity.

Proposed SDP approach efficiently handles multiple UEs.

Simulation shows superior performance over existing methods.

Abstract

It is anticipated that integrating unmanned aerial vehicles (UAVs) with reconfigurable intelligent surfaces (RISs), resulting in RIS-assisted UAV networks, will offer improved network connectivity against node failures for the beyond 5G networks. In this context, we utilize a RIS to provide path diversity and alternative connectivity options for information flow from user equipment (UE) to UAVs by adding more links to the network, thereby maximizing its connectivity. This paper employs the algebraic connectivity metric, which is adjusted by the reflected links of the RIS, to formulate the problem of maximizing the network connectivity in two cases. First, we consider formulating the problem for one UE, which is solved optimally using a linear search. Then, we consider the problem of a more general case of multiple UEs, which has high computational complexity. To tackle this problem, we formulate the problem of maximizing the network connectivity as a semi-definite programming (SDP) optimization problem that can be solved efficiently in polynomial time. In both cases, our proposed solutions find the best combination between UE(s) and UAVs through the RIS. As a result, it tunes the phase shifts of the RIS to direct the signals of the UEs to the appropriate UAVs, thus maximizing the network connectivity. Simulation results are conducted to assess the performance of the proposed solutions compared to the existing solutions.