Merging CBR and Neural Networks for SLA-Based Radio Resource Management for QoS Sensitive Cellular Networks
This paper proposes a Radio Resource Management (RRM) approach to guarantee a predefined Service Level Agreement (SLA) with different classes of users, for on-going and in-coming connections in QoS sensitive cellular networks. This approach is based on intelligent agent architecture which gives autonomy to Radio Network Controller (RNC) or Base Station (BS) in accepting, rejecting or buffering a connection request to manage system capacity. Instead of simply blocking the connection request, different buffering times are allocated to different classes of users based on their SLA. This increases the chances of connection establishment and reduces the call blocking rate extensively.