2007 | OriginalPaper | Buchkapitel
Binary Ant Colony Algorithm for Symbol Detection in a Spatial Multiplexing System
verfasst von : Adnan Khan, Sajid Bashir, Muhammad Naeem, Syed Ismail Shah, Asrar Sheikh
Erschienen in: Unconventional Computation
Verlag: Springer Berlin Heidelberg
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
While an optimal Maximum Likelihood (ML) detection using an exhaustive search method is prohibitively complex, we show that binary Ant Colony Optimization (ACO) based Multi-Input Multi-Output (MIMO) detection algorithm gives near-optimal Bit Error Rate (BER) performance with reduced computational complexity. The simulation results suggest that the reported unconventional detector gives an acceptable performance complexity trade-off in comparison with conventional ML and non-linear Vertical Bell labs Layered Space Time (VBLAST) detectors. The proposed technique results in 7-dB enhanced BER performance with acceptable increase in computational complexity in comparison with VBLAST. The reported algorithm reduces the computer time requirement by as much as 94% over exhaustive search method with a reasonable BER performance.