TY  - UNPB
AV  - public
A1  - Sacchi, Claudio
A1  - D'Orazio, Leandro
A1  - Donelli, Massimo
A1  - De Natale, Francesco G.B.
ID  - elediasc12402
N2  - In this work, a novel Minimum-Mean Squared-Error (MMSE) multi-user detector is proposed for MC-CDMA transmission systems working over mobile radio channels characterized by time-varying multipath fading. The proposed MUD algorithm is based on a Genetic Algorithm (GA)-assisted per-carrier MMSE criterion. The GA block works in two successive steps: a training-aided step aimed at computing the optimal receiver weights using a very short training sequence, and a decision-directed step aimed at dynamically updating the weights vector during a channel coherence period. Numerical results evidenced BER performances almost coincident with ones yielded by ideal MMSE-MUD based on the perfect knowledge of channel impulse response. The proposed GA-assisted MMSE-MUD clearly outperforms state-of-the-art adaptive MMSE receivers based on deterministic gradient algorithms, especially for high number of transmitting users.
TI  - A genetic algorithm-assisted semi-adaptive MMSE multi-user detection for MC-CDMA mobile communication systems
Y1  - 2006/05//
UR  - http://www.eledia.org/students-reports/402/
ER  -