{"id":46,"date":"2016-06-20T11:58:25","date_gmt":"2016-06-20T11:58:25","guid":{"rendered":"http:\/\/flexnets.ceng.metu.edu.tr\/?page_id=46"},"modified":"2021-05-22T12:25:46","modified_gmt":"2021-05-22T12:25:46","slug":"cfp","status":"publish","type":"page","link":"https:\/\/flexnets.ceng.metu.edu.tr\/?page_id=46","title":{"rendered":"Call for Papers"},"content":{"rendered":"

Download in PDF<\/a>.<\/p>\n

Scope<\/strong><\/p>\n

As the next generation communication systems beyond 5G, such as 6G, are expected to address a wider range of services and applications with a wider range of dynamic environmental and service-specific requirements, these systems evolve towards an architecture supported by enhanced capabilities to adapt to such flexible environments and connectivity needs. Envisioned 6G systems are expected to serve applications with varying requirements, be adaptive to connectivity requirements through dynamic topologies, and take autonomous decisions to reconfigure the networks for efficiency and resiliency purposes. Flexible and agile networking solutions are required to deal with uncertainties and dynamic parameters. <\/span>As the number of QoS-demanding applications increases, the capability of end-to-end holistic network transformation is required triggered by the dynamicity and mobility of users and their service demands as well as the radio and resource topology. The complexity introduced by these flexible characteristics needs to be addressed with more adaptive network resource sharing mechanisms among users and applications. This includes the adaptation to individual QoE\/QoS needs and the dynamics of the applications and services, starting from access networks to core networks.<\/span><\/p>\n

Ability to process the big data generated by the network, derive necessary feedbacks from the network and AI-assisted cognitive network management are key enablers for elasticity and adaptiveness to network dynamics such as topology changes due to mobility of the end users, base stations and rearrangement of network resources (either physical or virtual network functions). A distributed, self-*, AI-assisted and deeply programmable\/r<\/span>econfigurable end-to-end<\/span> network architecture is required to address such network dynamics. <\/span><\/p>\n

In this FlexNets workshop, the topic of interests include, but are not limited to:<\/p>\n