Memory CO-NNPD for the Compensation of Memory Crosstalk and HPA Nonlinearity
Résumé
In [1] and [2], authors proposed two efficient crossover
predistortion schemes which are capable to compensate
simultaneously HPA nonlinearity and crosstalk effects in
MIMO systems. The crosstalk model considered in these
papers was memoryless one. However, memory effects of
crosstalk can no longer be ignored due to the broadband
transmitted signal.
Then, in this paper, we demonstrate the effect of memory
crosstalk on the Crossover Neural Network Predistorter
(CO-NNPD) proposed in [1]. Along, we propose a new
crossover predistortion structure based on this conventional
CO-NNPD which is capable to enhance good performance
in MIMO OFDM systems in presence of HPA nonlinearities
with taken into account the memory effects of crosstalk. The
Levenberg-Marquardt algorithm (LM) is used for neural
network training, which has proven [3] to exhibit a very
good performance with lower computation complexity and
faster convergence than other algorithms used in literature.
This paper is supported with simulation results for the
Alamouti STBC MIMO OFDM system in terms of Bit Error
Rate (BER) in Rayleigh fading channel.