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QBFO-BOMP Based Channel Estimation Algorithm for mmWave Massive MIMO Systems

Xiaoli Jing, Xianpeng Wang*, Xiang Lan, Ting Su

State Key Laboratory of Marine Resource Utilization in South China Sea and School of Information and Communication Engineering, Hainan University, Haikou, 570228, China

* Corresponding Author: Xianpeng Wang. Email: email

(This article belongs to the Special Issue: AI-Driven Intelligent Sensor Networks: Key Enabling Theories, Architectures, Modeling, and Techniques)

Computer Modeling in Engineering & Sciences 2023, 137(2), 1789-1804. https://doi.org/10.32604/cmes.2023.028477

Abstract

At present, the traditional channel estimation algorithms have the disadvantages of over-reliance on initial conditions and high complexity. The bacterial foraging optimization (BFO)-based algorithm has been applied in wireless communication and signal processing because of its simple operation and strong self-organization ability. But the BFO-based algorithm is easy to fall into local optimum. Therefore, this paper proposes the quantum bacterial foraging optimization (QBFO)-binary orthogonal matching pursuit (BOMP) channel estimation algorithm to the problem of local optimization. Firstly, the binary matrix is constructed according to whether atoms are selected or not. And the support set of the sparse signal is recovered according to the BOMP-based algorithm. Then, the QBFO-based algorithm is used to obtain the estimated channel matrix. The optimization function of the least squares method is taken as the fitness function. Based on the communication between the quantum bacteria and the fitness function value, chemotaxis, reproduction and dispersion operations are carried out to update the bacteria position. Simulation results show that compared with other algorithms, the estimation mechanism based on QBFO-BOMP algorithm can effectively improve the channel estimation performance of millimeter wave (mmWave) massive multiple input multiple output (MIMO) systems. Meanwhile, the analysis of the time ratio shows that the quantization of the bacteria does not significantly increase the complexity.

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APA Style
Jing, X., Wang, X., Lan, X., Su, T. (2023). QBFO-BOMP based channel estimation algorithm for mmwave massive MIMO systems. Computer Modeling in Engineering & Sciences, 137(2), 1789-1804. https://doi.org/10.32604/cmes.2023.028477
Vancouver Style
Jing X, Wang X, Lan X, Su T. QBFO-BOMP based channel estimation algorithm for mmwave massive MIMO systems. Comput Model Eng Sci. 2023;137(2):1789-1804 https://doi.org/10.32604/cmes.2023.028477
IEEE Style
X. Jing, X. Wang, X. Lan, and T. Su "QBFO-BOMP Based Channel Estimation Algorithm for mmWave Massive MIMO Systems," Comput. Model. Eng. Sci., vol. 137, no. 2, pp. 1789-1804. 2023. https://doi.org/10.32604/cmes.2023.028477



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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