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Quantum Annealing-Based Sum Rate Maximization for Multi-UAV-Aided Wireless Networks

Seon-Geun Jeong, Pham Dang Anh Duc, Quang Vinh Do, Dae-Il Noh, Nguyen Xuan Tung, Trinh Van Chien, Quoc-Viet Pham, Mikio Hasegawa, Hiroo Sekiya, Won-Joo Hwang

TL;DR

The paper tackles the NP-hard problem of maximizing sum rate in a multi-UAV wireless network by jointly optimizing user clustering, sub-channel assignment, and power allocation. It introduces a two-stage QA-based approach that first performs QA-driven user clustering and then optimizes sub-channel/power decisions for the resulting clustering, with each subproblem cast as a QUBO/Ising model solved on a D-Wave quantum annealer using a constrained quadratic model. A novel MILFP-based method derives optimal scaling parameters and penalty factors to ensure feasible, near-global solutions, validated by simulations showing improved clustering accuracy, higher sum rates, and consistently low QA runtimes. The work demonstrates the potential of quantum-enabled optimization to address complex resource allocation in wireless networks, while also acknowledging hardware limitations and practical challenges for near-term deployment.

Abstract

In wireless communication networks, it is difficult to solve many NP-hard problems owing to computational complexity and high cost. Recently, quantum annealing (QA) based on quantum physics was introduced as a key enabler for solving optimization problems quickly. However, only some studies consider quantum-based approaches in wireless communications. Therefore, we investigate the performance of a QA solution to an optimization problem in wireless networks. Specifically, we aim to maximize the sum rate by jointly optimizing clustering, sub-channel assignment, and power allocation in a multi-unmanned aerial vehicle-aided wireless network. We formulate the sum rate maximization problem as a combinatorial optimization problem. Then, we divide it into two sub-problems: 1) a QA-based clustering and 2) sub-channel assignment and power allocation for a given clustering configuration. Subsequently, we obtain an optimized solution for the joint optimization problem by solving these two sub-problems. For the first sub-problem, we convert the problem into a simplified quadratic unconstrained binary optimization (QUBO) model. As for the second sub-problem, we introduce a novel QA algorithm with optimal scaling parameters to address it. Simulation results demonstrate the effectiveness of the proposed algorithm in terms of the sum rate and running time.

Quantum Annealing-Based Sum Rate Maximization for Multi-UAV-Aided Wireless Networks

TL;DR

The paper tackles the NP-hard problem of maximizing sum rate in a multi-UAV wireless network by jointly optimizing user clustering, sub-channel assignment, and power allocation. It introduces a two-stage QA-based approach that first performs QA-driven user clustering and then optimizes sub-channel/power decisions for the resulting clustering, with each subproblem cast as a QUBO/Ising model solved on a D-Wave quantum annealer using a constrained quadratic model. A novel MILFP-based method derives optimal scaling parameters and penalty factors to ensure feasible, near-global solutions, validated by simulations showing improved clustering accuracy, higher sum rates, and consistently low QA runtimes. The work demonstrates the potential of quantum-enabled optimization to address complex resource allocation in wireless networks, while also acknowledging hardware limitations and practical challenges for near-term deployment.

Abstract

In wireless communication networks, it is difficult to solve many NP-hard problems owing to computational complexity and high cost. Recently, quantum annealing (QA) based on quantum physics was introduced as a key enabler for solving optimization problems quickly. However, only some studies consider quantum-based approaches in wireless communications. Therefore, we investigate the performance of a QA solution to an optimization problem in wireless networks. Specifically, we aim to maximize the sum rate by jointly optimizing clustering, sub-channel assignment, and power allocation in a multi-unmanned aerial vehicle-aided wireless network. We formulate the sum rate maximization problem as a combinatorial optimization problem. Then, we divide it into two sub-problems: 1) a QA-based clustering and 2) sub-channel assignment and power allocation for a given clustering configuration. Subsequently, we obtain an optimized solution for the joint optimization problem by solving these two sub-problems. For the first sub-problem, we convert the problem into a simplified quadratic unconstrained binary optimization (QUBO) model. As for the second sub-problem, we introduce a novel QA algorithm with optimal scaling parameters to address it. Simulation results demonstrate the effectiveness of the proposed algorithm in terms of the sum rate and running time.

Paper Structure

This paper contains 15 sections, 2 theorems, 45 equations, 9 figures, 2 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Problem Description
  3. System Model
  4. Problem Formulation
  5. Quantum Annealing-Based Optimization Solution
  6. The QUBO and Ising Models
  7. Quantum Annealing-Based Approach
  8. QA-based User Clustering
  9. Joint Optimization of Sub-channel Assignment and Power Allocation for a Given Clustering Configuration
  10. Deriving The Optimal Scaling Parameter and Appropriate Penalty Factor
  11. Simulation Results
  12. Complexity Analysis
  13. Performance Analysis
  14. Practicality and Challenges
  15. Conclusion

Key Result

Lemma 1

Given the penalty term $H_{\text{C7}}$ from eq:QUBOH10, and assuming $H_{\text{C7}} \leq (M-1)^2N$, with $H'_{cost}=\sum\limits _{m\in \mathcal{M}}\sum\limits _{n\in \mathcal{N}}X'_{m,n}d_{m,n} \ \forall X'_{m,n} \neq X_{m,n}$ and $X^*$ denotes the optimal solution while $\mathcal{G}$ denotes the in

Figures (9)

  • Figure 1: System model.
  • Figure 2: The QA-based joint optimization problem of clustering, sub-channel assignment, and transmit power allocation.
  • Figure 3: Preplacement configuration of the UAVs in a two-dimensional space.
  • Figure 4: The solution and energy from the QAM in the case of scenario 2 and the number of UAV is 7.
  • Figure 5: Clustering comparison of SD, SA, K-means++, and QA.
  • ...and 4 more figures

Theorems & Definitions (5)

  • Remark 1
  • Lemma 1
  • proof
  • Lemma 2
  • proof