The multi-user detection in code division multiple access with adaptive neuro-fuzzy inference system

In this paper, multi user detection in Code Division Multiple Access (CDMA) was realized with an adaptive neuro-fuzzy inference system (ANFIS) and the bit error rate (BER) performance was compared with the performances of the matched filter and a neural network receiver. Increment of the number of the active users and the receiving various user signals at the receiver input stage in different power levels in CDMA degrade BER performance of the receiver. The receiver that used ANFIS has a better bit error rate (BER) performance than the neural network receiver's and the training process of the ANFIS is faster than the neural network's.