Mattias Lidström

Using the Policy Control system as an auxiliary tool to improve the Service Assurance Process in Telecommunication Networks

Telecommunication networks are shifting their role from networks optimized for circuit switched voice into IP-based multi service networks. Doing so enables the delivery of more feature-rich services, such as voice over IP (VoIP), streaming, push to talk (PoC) and mobile TV. Along with this change in network utilization, the infrastructure is also changing, to be able to deliver new and emerging services, as well as existing services, over different access technologies. These access technologies and services will require new tools and methods that are capable of monitoring operation in an efficient and automated manner. Service Assurance is especially important in this context and can be defined as a guarantee that services will be provided according to the expectations of the end-user. This paper shows how the policy control system can be used to enhance and automate the service assurance process without any major impacts on existing network architectures or functionalities. Information dynamically created in the policy control system is used to derive Key Performance Indicators for each media component in a service which are used to monitor and ensure the quality of the service. The benefits of using the policy control system compared to other methods (briefly described) are also explained. The process described in this paper follows the enhanced Telecom operations map (eTom) [1] guidelines for providing service assurance.

A Method for Providing Content and Service Recommendations Using Social Information from Telecommunications Networks

Social networks and community services are becoming an ever more popular phenomenon in the information society of today. The latest trend is also to utilize the information and collective wisdom that is created within these different types of social networks, in this paper we will show how information extracted from social networks can be used to derive recommendations and improve the user experience. We will show that different sources or type of social information enables different types of recommendations and the results of the evaluation will show an interesting conclusion of how social networks which utilize trust systems impact the recommendations that are given.