MIMO System Based-Constrained Quantum optimization Solution

Abdulbasit M. A. Sabaawi, Mohammed R. Almasaoodi, Sara El Gaily, S. Imre
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引用次数: 4

Abstract

The multiple-input multiple-output (MIMO) systems provide high data rates and spectral efficiency performance. However, the fundamental problems with these technologies are their rising computational complexity and power consumption. The aim of this paper is to minimize the total transmit power of the MIMO system subject to the target bit rate of the user. The procedure of assigning different transmit power values to transmiting antennas and selecting the optimum total transmit power with respect to the user’s bit rate constraint is computationally hard, especially when the size of the possible transmit power scenarios arises exponentially. To this end, an efficient quantum strategy called Constrained Quantum optimization Algorithm (CQOA) is proposed in this work, which searches faster (exponentially) for the optimum result. The proposed quantum strategy is compared with the various optimization algorithms such as Genetic Algorithm (GA). Simulation results highlight the fact that the CQOA outperforms the GA in terms of computational complexity.
基于约束量子优化的MIMO系统
多输入多输出(MIMO)系统提供高数据速率和频谱效率性能。然而,这些技术的根本问题是它们不断上升的计算复杂性和功耗。本文的目的是使MIMO系统的总发射功率在用户的目标比特率下最小。为发射天线分配不同的发射功率值并根据用户的比特率约束选择最佳的总发射功率的过程在计算上是困难的,特别是当可能的发射功率场景的大小呈指数增长时。为此,本文提出了一种高效的量子策略,即约束量子优化算法(Constrained quantum optimization Algorithm, CQOA),该算法以指数级的速度搜索最优结果。将提出的量子策略与遗传算法(GA)等各种优化算法进行了比较。仿真结果表明,CQOA在计算复杂度方面优于遗传算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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