This article develops a quantum bacterial foraging optimization (QBFO) algorithm, a quantum intelligence algorithm based on quantum computing and bacterial foraging optimization (BFO), with application in MIMO system optimization designs. In QBFO, a multiqubit is used to represent a bacterium, and a quantum rotation gate is used to mimic chemotaxis. Because the quantum bacterium with multiqubit has the advantage that it can represent a linear superposition of states (binary solutions) in search space probabilistically, the proposed QBFO algorithms shows better performance on solving combinatorial optimization problems than its classical counterpart BFO and Quantum Genetic Algorithm (QGA), especially for parallel non-gradient optimization. A sparse channel estimation scheme based on QBFO with adaptive phase rotation (AQBFO) in 3D MIMO system is proposed, and simulation results show that AQBFO achieved a better performance than existing algorithms including least squares (LS), iteratively reweighted least squares (IRLS), matching pursuit (MP), and orthogonal matching pursuit (OMP). We further improve some critical aspects such as reproduction and dispersal processes of AQBFO, propose an improved IQBFO algorithm, and apply it for interference coordination in 3D multi-cell multi-user MIMO systems, aiming to maximize the spectral efficiency. It considers user fairness and jointly optimizes cell-center and cell-edge user specific antenna downtilts and power to maximize each user’s sum rate. This problem is a combinatorial non-convex optimization problem that cannot be solved by the traditional Karush-Kuhn-Tucker Lagrangian algorithm whereas the IQBFO algorithm solves it effectively.

中文翻译：

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量子细菌觅食优化：从理论到MIMO系统设计

本文开发了一种量子细菌觅食优化（QBFO）算法，一种基于量子计算和细菌觅食优化（BFO）的量子智能算法，并将其应用于MIMO系统优化设计中。在QBFO中，多量子位用于表示细菌，量子旋转门用于模拟趋化性。因为具有多量子位的量子细菌的优势在于它可以概率地表示搜索空间中状态的线性叠加（二元解），所以与经典的BFO和量子遗传算法（QGA）相比，所提出的QBFO算法在解决组合优化问题上表现出更好的性能。 ），特别是用于并行非梯度优化。提出了一种3D MIMO系统中基于QBFO和自适应相位旋转的稀疏信道估计方案，仿真结果表明，AQBFO的性能优于现有算法，包括最小二乘，迭代加权最小二乘，匹配追踪（MP）和正交匹配追踪（OMP）。我们进一步改进了一些关键方面，例如AQBFO的再现和扩散过程，提出了一种改进的IQBFO算法，并将其应用于3D多小区多用户MIMO系统中的干扰协调，旨在最大程度地提高频谱效率。它考虑了用户公平性，并共同优化了小区中心和小区边缘用户特定的天线下倾角和功率，以最大化每个用户的总速率。