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RIS Partitioning and User Clustering for Resilient Non-Orthogonal Multiple Access UAV Networks

Mohammed Saif, Shahrokh Valaee

TL;DR

This work tackles resilient, high-connectivity RIS-assisted UAV networks by jointly clustering UEs to RISs, associating RISs to reliable UAVs, and partitioning RISs into virtual sections to support multiple UEs via NOMA. It develops exact and approximate SINR expressions for aligned RIS beams, and formulates a graph-based optimization to maximize algebraic connectivity under UAV reliability constraints, solved with a two-step iterative RIS-aided NOMA algorithm. The approach combines UE-RIS-UAV clustering with a closed-form RIS partitioning (Proposition 1), yielding up to 30–40% connectivity gains over traditional UAV schemes and providing a pragmatic path to robust, scalable future wireless networks. The results underscore the potential of RIS partitioning and NOMA in enhancing resilience against UAV failures while maintaining tractable complexity for centralized control at the network base station.

Abstract

The integration of reconfigurable intelligent surfaces (RISs) and unmanned aerial vehicles (UAVs) has emerged as a promising solution for enhancing connectivity in future wireless networks. This paper designs well-connected and resilient UAV networks by deploying and virtually partitioning multiple RISs to create multiple RIS-aided links, focusing on a link-layer perspective. The RIS-aided links are created to connect user equipment (UE) to blocked and reliable UAVs, where multiple UEs can transmit to same UAV via RIS using non-orthogonal multiple access (NOMA), granting access to UEs and maximizing network connectivity. We first derive exact and approximated closed-form expressions for signal-to-interference plus noise ratio (SINR) based on aligned and non-aligned RIS-aided beams. Then, we propose to formulate the problem of maximizing network connectivity that jointly considers (i) UE NOMA clustering, (ii) RIS-aided link selection, and (ii) virtual RIS partitioning. This problem is a computationally expensive combinatorial optimization. To tackle this problem, a two-step iterative approach, called RIS-aided NOMA, is proposed. In the first step, the UEs are clustered to the RISs according to their channel gains, while UAVs are associated to those generated clusters based on their reliability, which measures the criticality of UAVs. The second step optimally partitions the RISs to support each of the cluster members. In this step, we derive the closed-form equations for the optimal partitioning of RISs within the clusters. Simulation results demonstrate that the proposed RIS-aided NOMA yields a gain of 30% to 40%, respectively, compared to UAV traditional scheme. The finding emphasizes the potential of integrating RIS with UAV communications as a robust and reliable connectivity solution for future wireless communication systems.

RIS Partitioning and User Clustering for Resilient Non-Orthogonal Multiple Access UAV Networks

TL;DR

This work tackles resilient, high-connectivity RIS-assisted UAV networks by jointly clustering UEs to RISs, associating RISs to reliable UAVs, and partitioning RISs into virtual sections to support multiple UEs via NOMA. It develops exact and approximate SINR expressions for aligned RIS beams, and formulates a graph-based optimization to maximize algebraic connectivity under UAV reliability constraints, solved with a two-step iterative RIS-aided NOMA algorithm. The approach combines UE-RIS-UAV clustering with a closed-form RIS partitioning (Proposition 1), yielding up to 30–40% connectivity gains over traditional UAV schemes and providing a pragmatic path to robust, scalable future wireless networks. The results underscore the potential of RIS partitioning and NOMA in enhancing resilience against UAV failures while maintaining tractable complexity for centralized control at the network base station.

Abstract

The integration of reconfigurable intelligent surfaces (RISs) and unmanned aerial vehicles (UAVs) has emerged as a promising solution for enhancing connectivity in future wireless networks. This paper designs well-connected and resilient UAV networks by deploying and virtually partitioning multiple RISs to create multiple RIS-aided links, focusing on a link-layer perspective. The RIS-aided links are created to connect user equipment (UE) to blocked and reliable UAVs, where multiple UEs can transmit to same UAV via RIS using non-orthogonal multiple access (NOMA), granting access to UEs and maximizing network connectivity. We first derive exact and approximated closed-form expressions for signal-to-interference plus noise ratio (SINR) based on aligned and non-aligned RIS-aided beams. Then, we propose to formulate the problem of maximizing network connectivity that jointly considers (i) UE NOMA clustering, (ii) RIS-aided link selection, and (ii) virtual RIS partitioning. This problem is a computationally expensive combinatorial optimization. To tackle this problem, a two-step iterative approach, called RIS-aided NOMA, is proposed. In the first step, the UEs are clustered to the RISs according to their channel gains, while UAVs are associated to those generated clusters based on their reliability, which measures the criticality of UAVs. The second step optimally partitions the RISs to support each of the cluster members. In this step, we derive the closed-form equations for the optimal partitioning of RISs within the clusters. Simulation results demonstrate that the proposed RIS-aided NOMA yields a gain of 30% to 40%, respectively, compared to UAV traditional scheme. The finding emphasizes the potential of integrating RIS with UAV communications as a robust and reliable connectivity solution for future wireless communication systems.
Paper Structure (18 sections, 1 theorem, 22 equations, 9 figures, 2 tables, 2 algorithms)

This paper contains 18 sections, 1 theorem, 22 equations, 9 figures, 2 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Related Works
  3. Motivations and Contributions
  4. System Model
  5. Network Model
  6. Channel Models and Signal Reception
  7. RIS Phase Shift and SINR Formulation
  8. Problem Modeling
  9. Graph Modeling
  10. Node Reliability
  11. Problem Formulation
  12. Problem Methodology
  13. RIS-aided Link Selection: UE-RIS-UAV Clustering
  14. Optimal RIS Partitioning
  15. Numerical Results
  16. ...and 3 more sections

Key Result

Proposition 1

The optimal RIS$_r$ partitioning that provides the maximized network connectivity via maximizing the sum SINR for the RIS$_r$ cluster is given by where $A=K^2m\hat{\gamma}_{u}$, $B=K^2m\hat{\gamma}_{u'}$, and $[x]^+=\max\{x,0\}$.

Figures (9)

  • Figure 1: RIS-assisted NOMA UAV system with two RISs, each utilizes RIS partitioning technique to aid the signals of two UEs.
  • Figure 2: Rate versus the number of elements $K$ for $U=4$, $U_r=2$, $A=2$, and $R=2$ for (a) perfect phase shift and (b) imperfect phase shift ($b=3$).
  • Figure 3: Rate versus the number of elements $K$ for $U=4$, $A=2$, and $R=2$ for (a) $U_r=1$ and (b) $U_r=2$.
  • Figure 4: Network connectivity versus RIS cluster size $U_r$ for $U=15$, $A=8$, $K=200$, and $R=3$.
  • Figure 5: Network connectivity versus number of UAVs $A$ for $U=15$, $U_r=3$, $K=200$, and $R=3$.
  • ...and 4 more figures

Theorems & Definitions (2)

  • Proposition 1
  • proof