CDMA interference cancellation techniques using neural networks in Rayleigh channels

Abstract

Neural network based Interference cancellers occupy a prominent place in the class of multi user detectors for CDMA.The present work focuses on the effect of pre-processing of the data fed to the neural networks in those receivers. The effect on bit error rate due to pre-processing is presented, with respect to a variation of signal to noise ratios and the number of parallel interference canceller stages. The work considered, R a y l e i g h multipath fading channel as the medium and DPSK as baseband modulation.

Smart DS-CDMA Receiver Based on Feed Forward Neural Network

“…This algorithm is compared with other Neural Network (NN) based detectors. It is also demonstrated that the BER performance of the proposed SBRNN detector is better than that of other NN detectors that have been proposed earlier.In [4], the authors have used NN to cancel the MAI using specialized pre-processing of the data input to the NN. The NN is trained using Levenberg-Marquardt method.…”

Non-Linear Digital Self-Interference Cancellation for In-Band Full-Duplex Radios Using Neural Networks

“…The work of [15] considered using a neural network in order to decode polar codes. In [16], [17], [18], neural networks were employed in order to perform detection and intra-user (and mostly linear) successive interference cancellation in multiuser CDMA systems. Finally, the work of [19] considered using neural networks for wireless resource management.…”