@Article{cmes.2023.028477,
AUTHOR = {Xiaoli Jing, Xianpeng Wang, Xiang Lan, Ting Su},
TITLE = {QBFO-BOMP Based Channel Estimation Algorithm for mmWave Massive MIMO Systems},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {137},
YEAR = {2023},
NUMBER = {2},
PAGES = {1789--1804},
URL = {http://www.techscience.com/CMES/v137n2/53350},
ISSN = {1526-1506},
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.},
DOI = {10.32604/cmes.2023.028477}
}