基于QAOA的免小区大规模MIMO系统导频分配

中文摘要 AI 辅助

导频污染是免小区大规模多输入多输出（MIMO）系统中的一个主要挑战，其中正交导频数量有限使得导频重用不可避免。由于导频分配的解决方案空间随用户数量呈指数增长，在经典计算机上找到全局最优解在计算上变得具有挑战性。量子计算的最新进展为解决此类大规模组合优化问题提供了一种有前景的方法。在本文中，我们将分配问题重新表述为量子兼容的优化问题，使其能够直接由量子近似优化算法（QAOA）求解。具体而言，导频污染目标和分配约束被纳入QAOA公式中。这使得量子搜索能够专注于具有低污染成本的合法导频分配。仿真结果表明，所提出的方法实现了接近穷举搜索的性能，展示了量子辅助优化在未来无线系统中导频分配方面的潜力。

英文摘要

Pilot contamination is a major challenge in cell-free massive multiple-input multiple-output (MIMO) systems, where the limited number of orthogonal pilots makes pilot reuse unavoidable. Since the pilot assignment solution space grows exponentially with the number of users, finding the global optimum becomes computationally challenging on classical computers. Recent advances in quantum computing provide a promising approach for solving such large-scale combinatorial optimization problems. In this paper, we reformulate the assignment problem as a quantum-compatible optimization problem, enabling it to be directly solved by the Quantum Approximate Optimization Algorithm (QAOA). Specifically, the pilot contamination objective and assignment constraints are incorporated into the QAOA formulation. This allows the quantum search to focus on valid pilot assignments with low contamination cost. Simulation results show that the proposed method achieves performance close to exhaustive search, demonstrating the potential of quantum-assisted optimization for pilot assignment in future wireless systems.

发表机构

• Western University（韦仕敦大学）

机构由 AI 辅助整理，请以论文原文为准。