MIMO System Based-Constrained Quantum optimization Solution

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 t...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:2022 13th International Symposium on Communication Systems, Networks and Digital Signal Processing (CSNDSP) s. 488 - 492
Hlavní autori: Sabaawi, Abdulbasit M A, Almasaoodi, Mohammed Rabeea, Gaily, Sara El, Imre, Sandor
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 20.07.2022
Predmet:
Bit rate
Computational complexity
constrained quantum optimization algorithm
MIMO system
Power demand
Signal processing algorithms
Simulation
Spectral efficiency
transmit power consumption
Transmitting antennas
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí: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.
DOI:10.1109/CSNDSP54353.2022.9907967