In this work, we propose an evolutionary-like approach to the problem of blind adaptive spatial filtering that is based on the decision-directed criterion and on the dopt-aiNet, an artificial immune network conceived to perform multimodal search in dynamic environments. The proposal was tested under static and time-varying undermodeled channel models, and, in all cases, its ability to find and track a solution close to the Wiener global optimum was attested. The obtained results reveal that the dopt-aiNet may decisively enhance the performance of adaptive arrays in scenarios built from elements that are representative of some aspects of real-world communication systems.
['C. Junqueira', 'F.O. de Franca', 'R.R.F. Attux', 'Cristiano Panazio', 'L.N. de Castro', 'F.J. Von Zuben', 'João Marcos Travassos Romano']
Immune-inspired Dynamic Optimization for Blind Spatial Equalization in Undermodeled Channels