.png){kind=icon}
Return
MIMO Array Capacity Optimization Using a Genetic Algorithm
.png){kind=icon}
PRE
AI
DOI：10.1109/TVT.2011.2158460.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This paper discusses the design of multiple-input-multiple-output (MIMO) antenna systems and proposes a genetic algorithm to obtain the position and orientation of each MIMO array antenna that maximizes the ergodic capacity for a given propagation scenario. One challenging task in the MIMO system design is to accommodate the multiple antennas in the mobile device without compromising the system capacity, due to spatial and electrical constraints. Based on an interface between the antenna model and the propagation channel model, the ergodic capacity is considered as the objective function of the MIMO array optimization. Simulation results corroborate the importance of polarization and antenna pattern diversities for MIMO in small terminals. Our results also show that the electromagnetic coupling effect can be exploited by the optimizer to decrease signal correlation and increase MIMO capacity. A comparison among a uniform linear array (ULA), a uniform circular array (UCA), and a genetic algorithm (GA)-optimized array is also carried out, showing that the topology given by the optimizer is superior to that of the standard ULA and UCA for the considered propagation channel.
Keywords:
Array optimization
capacity
genetic algorithm (GA)
multiple-input-multiple-output (MIMO)
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
7.1
Papers:
1.8W
Citations:
6.6W
Organization
Cited Papers
.png){kind=icon}
ShareSaveShareSaveShareSaveShareSave
View more