An intelligent-agent approach for congestion management in 3G networks

The exploitation of wideband code division multiple access (W-CDMA) technology in third generation (3G) networks gives an inherent flexibility in managing the system capacity, although radio resource management (RRM), including congestion management, is more complicated. To guarantee the quality of service (QoS) provided to customers, the concept of a “service level agreement” (SLA) is introduced and these must be managed by the RRM. This work proposes the application of intelligent agents in SLA-based control in the RRM, essentially for congestion management and demonstrates the ability of intelligent agents to improve and maintain the QoS to meet the required SLA. A particularly novel aspect of this work is the use of learning (case-based reasoning—CBR) to predict the control strategies to be imposed. If there is no congestion, the network operates as provisioned, but, if congestion occurs, it is detected by the agent monitoring process and CBR will be used to provide a suitable policy either by recalling from experience or recalculating the solution from its knowledge. With this approach, the system performance will be monitored at all times and a suitable policy can be applied immediately as the system environment changes, resulting in the QoS being maintained.