E3S Web Conf.
Volume 351, 202210th International Conference on Innovation, Modern Applied Science & Environmental Studies (ICIES’2022)
|Number of page(s)||5|
|Published online||24 May 2022|
Ant Colony-based Optimization algorithm to overcome the pilot contamination issue within multi-cell Massive MIMO systems
I2SP team Group, Faculty of Sciences Semlalia, Cadi Ayyad University, Marrakesh 40000, Morocco
* Corresponding author: firstname.lastname@example.org
the pilot contamination (PC) issue still faces and limits the promising massive MIMO (mMIMO) technology, to unleash the high benefits of mMIMO, we provide here a new decontaminating strategy, which exploits the large-scale fading coefficients' characteristics to construct a search space for the Ant colony-based optimization (ACO) algorithm. This algorithm is employed to find the best pattern in which each UE is linked to its most concurrent UEs of the adjacent cells; specifically, each UEs has an enemy UEs that if they are allocated with the same pilot, the severity of the PC upon the two UEs become subversive for the quality-of-service of the two UEs. Hence, the ACO algorithm finds for each UE its enemy UE, which leads to construct a Hamiltonian graph. This graph is exploited during the assignment of the pilot sequences to the overall UEs; specifically, the linked UEs are successively allocated with the available OPSs, which leads to address the PC problem within multi-cell mMIMO systems.
© The Authors, published by EDP Sciences, 2022
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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