研究目的
Investigating the impact of the array’s geometrical shape on the cross-correlation diversity gain performance of MIMO antenna arrays.
研究成果
The paper demonstrates that the geometrical shape of MIMO antenna arrays significantly impacts their diversity gain performance. By optimizing the positions and orientations of the array elements, it is possible to achieve high diversity gains even with small array sizes. The study also identifies critical antenna densities beyond which no further improvement in diversity gain is possible, providing valuable insights for the design of MIMO systems.
研究不足
The study focuses on infinitesimal dipoles as the main radiating elements, which may not fully capture the behavior of more complex antenna types. Additionally, the optimization process is computationally expensive, especially for large arrays.
1:Experimental Design and Method Selection:
The design algorithm approximates arbitrary antenna geometrical configurations by arrays of infinitesimal dipoles and uses the cross-correlation Green’s function to compute far-field cross correlations without explicitly measuring or computing far-zone fields. A global optimization strategy (the genetic algorithm) is applied to find optimum positions and orientations of the MIMO antennas’ elements within a given geometrical shape.
2:Sample Selection and Data Sources:
The study uses infinitesimal dipoles as the main radiating elements, focusing on their positions and orientations within various array topologies.
3:List of Experimental Equipment and Materials:
The study involves simulations and numerical analyses without specifying physical equipment.
4:Experimental Procedures and Operational Workflow:
The method involves computing the far-field cross correlation in terms of the geometrical details of the antenna array and applying a genetic algorithm to optimize these details for maximum diversity gain.
5:Data Analysis Methods:
The diversity gain of the array is computed in terms of the positions and orientations of each dipole, and a nonlinear optimization problem is formulated to search for the optimum positions and orientations leading to the best performance.
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