An intelligent-agent approach for managing congestion in W-CDMA networks

Abstract

Resource Management is a crucial aspect in the next generation cellular networks since the use of W-CDMA technology gives an inherent flexibility in managing the system capacity. The concept of a “Service Level Agreement” (SLA) also plays a very important role as it is the means to guarantee the quality of service provided to the customers in response to the level of service to which they have subscribed. Hence there is a need to introduce effective SLA-based policies as part of the radio resource management. This work proposes the application of intelligent agents in SLA-based control in resource management, especially when congestion occurs. The work demonstrates the ability of intelligent agents in improving and maintaining the quality of service to meet the required SLA as the congestion occurs. A particularly novel aspect of this work is the use of learning (here Case Based Reasoning) to predict the control strategies to be imposed. As the system environment changes, the most suitable policy will be implemented. When congestion occurs, the system either proposes the solution by recalling from experience (if the event is similar to what has been previously solved) or recalculates the solution from its knowledge (if the event is new). With this approach, the system performance will be monitored at all times and a suitable policy can be immediately applied as the system environment changes, resulting in maintaining the system quality of service.