The paper proposes congestion control using fuzzy/neural techniques for int
egrated voice and data direct-sequence code division multiple access/frame
reservation multiple access (DS-CDMA/FRMA) cellular networks. The fuzzy/neu
ral congestion controller is constituted by a pipeline recurrent neural net
work (PRNN) interference predictor, a fuzzy performance indicator, and a fu
zzy/neural access probability controller It regulates traffic input to the
integrated voice and data DS-CDMA/FRMA cellular system by determining prope
r access probabilities for users so that congestion can be avoided and thro
ughput can be maximized. Simulation results show that the DS-CDMA/FRMA fuzz
y/neural congestion controllers perform better than conventional DS-CDMA/PR
MA with channel access function in voice packet dropping ratio, corruption
ratio, and utilization. In addition, the neural congestion controller outpe
rforms the fuzzy congestion controller.