VHDL implementation of neuro-fuzzy based adaptive bandwidth controller for ATM networks

Abstract

The modern intelligent asynchronous transfer mode (ATM) networks need a scheme to maintain quality of service (QoS) towards different traffic constraints in real time environment for effective control and optimal system utilisation. This paper proposes the VHDL implementation of neuro-fuzzy based adaptive bandwidth controller. The intelligent control is used to manage the adaptive bandwidth prediction, congestion control and call admission control (CAC) of ATM networks simultaneously. This integrated hybrid mechanism reduces the decision errors of conventional CAC policies and operating errors occurred on fuzzy based CAC in ATM networks. The individual blocks needed to implement adaptive bandwidth controller for ATM networks are modelled using VHDL. The timing analysis for the validation, functionality and performance of the model is analysed using Altera Quartus II (5.1 version) HDL software and the logic synthesis is performed. The required codes are downloaded into FPGA Cyclone device.