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45th International Conference on Telecommunications and Signal Processing (TSP)

作者： Sabaawi, Abdulbasit M. A. Almasaoodi, Mohammed Rabeea El Gaily, Sara Imre, Sandor Budapest Univ Technol & Econ Dept Networked Syst & Serv Budapest Hungary Ninevah Univ Coll Elect Engn Mosul Iraq Kerbala Univ Kerbala Iraq

ISBN: (纸本)9781665469487

Multiple-input multiple-output (MIMO) has acquired significant interest as the demand for high data rates has increased and, it is regarded as a key technique in 5G wireless technology. Previously, the water filling algorithm (WFA) has been found as an effective method for power allocation channels in (MIMO). However, this solution is computationally intractable owing to the increasing size of channels. To address this problem, this paper applies the constrained quantum optimization algorithm (CQOA) for reducing the overall transmit power and the computational complexity of the MIMO downlink system. An analytical comparison has been performed between the CQOA and water filling algorithm-based binary searching algorithm (WFA-BSA). Numerical results show the efficiency of the CQOA against the WFA-BSA in terms of power and computation.

关键词： constrained quantum optimization algorithm MIMO quantum computing water filling algorithm

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MIMO System Based-constrained quantum optimization Solution 13

MIMO System Based-Constrained Quantum Optimization Solution

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13th International Symposium on Communication Systems, Networks and Digital Signal Processing (CSNDSP)

作者： Sabaawi, Abdulbasit M. A. Almasaoodi, Mohammed Rabeea El Gaily, Sara Imre, Sandor Budapest Univ Technol & Econ Dept Networked Syst & Serv Muegyet Rkp 3 H-1111 Budapest Hungary Ninevah Univ Coll Elect Engn Mosul Iraq Kerbala Univ Kerbala Iraq

ISBN: (数字)9781665410441

ISBN: (纸本)9781665410441

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.

关键词： MIMO system constrained quantum optimization algorithm transmit power consumption

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constrained quantum optimization for Resource Distribution Management

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2021年第8期12卷 42-51页

作者： El Gaily, Sara Imre, Sandor Budapest Univ Technol & Econ Dept Networked Syst & Serv Budapest Hungary

The cloud computing field suffers from the heavy processing caused by the exponentially increasing data traffic. Therefore, optimizing the network performance and achieving a better quality of service (QoS) became a central goal. In cloud computing, the problem of energy consumption of resource distribution management system (RDMS) is presented as an optimization problem. Most of the existing classical optimization approaches, such as heuristic and metaheuristic have high computational complexity. In this work, we proposed a quantum optimization strategy that executes the tasks exponentially faster and with high accuracy named constrained quantum optimization algorithm (CQOA). We exploit the CQOA in RDMS as a toy example for pointing out the efficiency of the proposed quantum strategy in reducing energy consumption and computational complexity. Following that, we investigate the CQOA's implementation, setup, and computational complexity. Finally, we create a simulation environment to evaluate the efficiency of the suggested implemented constrained quantum strategy.

关键词： quantum computing constrained quantum optimization algorithm quantum extreme values searching algorithm resource distribution management cloud computing

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