| Issue |
E3S Web Conf.
Volume 351, 2022
10th International Conference on Innovation, Modern Applied Science & Environmental Studies (ICIES’2022)
|
|
|---|---|---|
| Article Number | 01078 | |
| Number of page(s) | 5 | |
| DOI | https://doi.org/10.1051/e3sconf/202235101078 | |
| 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: abdelfettah.belhabib@edu.uca.ac.ma
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
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