AI Summary & Key Takeaways
AI InsightWith the use of an upper bound, it is not necessary to compute the matrix inversion that is needed in computing the MSE.
📌 3 Key Takeaways
Abstract
In this paper, we propose a differential evolution (DE) algorithm for optimizing the placement and power of the pilot tones that are utilized by a least square (LS) algorithm for channel estimation in multiple-input and multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) systems. Computer simulations demonstrated that the performance of the LS algorithm was increased by optimizing the pilot tones with the DE algorithm instead of locating them orthogonally. We used the upper bound of the mean square error (MSE) as a fitness function of the DE algorithm for optimization tasks. With the use of an upper bound, it is not necessary to compute the matrix inversion that is needed in computing the MSE.
Özet
In this paper, we propose a differential evolution (DE) algorithm for optimizing the placement and power of the pilot tones that are utilized by a least square (LS) algorithm for channel estimation in multiple-input and multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) systems. Computer simulations demonstrated that the performance of the LS algorithm was increased by optimizing the pilot tones with the DE algorithm instead of locating them orthogonally. We used the upper bound of the mean square error (MSE) as a fitness function of the DE algorithm for optimization tasks. With the use of an upper bound, it is not necessary to compute the matrix inversion that is needed in computing the MSE.