Real-time Dynamic Adaption-centric Multi-dimensional Antenna Design Optimization Using MPS-OOA for 6G-Enabled Wireless Networks

Abstract Objectives: A Massive Multiple-Input-Multiple-Output (MIMO) antenna has emerged as a cornerstone in 6G-wireless networks. However, prior studies have not focused on real-time adaptive optimization. Hence, this article proposes an innovative design for real-time adaptive optimization of MIMO antenna systems that dynamically adjust to varying network conditions. Methods: Firstly, the design parameters are initialized to set up and optimize a massive MIMO antenna. Thereafter, the non-linear relationships between each parameter are captured. Further, the trade-offs are balanced to diminish the conflicting objectives. Here, the proposed Multi-Phase Search Orangutan Optimization Algorithm (MPS-OOA) is employed to optimize the antenna design. In real-time communication, the antenna operates under dynamic conditions. From the collected data, the Gaussian Extended Approximation Kalman Filter (GEAKF) is used to monitor the network fluctuations. Findings: Based on the observed variations, data is grouped and then subjected to the MPO-OOA antenna design optimization. Novelty: The proposed work ensures continuous adaption of the antenna with 97.02% efficiency. Keywords: Antenna Design Optimization (ADO); Multiple-Input-Multiple-Output (MIMO); Antenna Systems (AS); Real-time Dynamic Adaption (RDA); 6G-Wireless Networks (6G-WN); Exponential Fuzzy Hyperbolic Tangent Interference System (EFHTIS); Pareto Linear Interpolation Front Analysis (PLIFA)