Emergent Systems

About this pillar

a. Edge Computing and its implementation with Future networks ecosystem:
Edge computing is a key enabler for a new breed of latency-critical applications (e.g., metaverse, real-time control, spatial perception). 3GPP noted that implementation of edge computing in next generation radio access networks (NG-RAN) may have the potential to provide up to 10x latency reduction. Within CHEDDAR, we are researching how edge computing can provide support for autonomy, as well as network self-configuration. We are investigating architectures which allow integration of edge computing across the radio network eco-system. Our work also includes understanding optimal cloud native implementations of access technologies to provide connectivity to edge.

b. Cloud Native Next Generation Networks:
Disaggregation, Decentralisation and Distributed computation are critical features for future networks. Our research is geared at not only developing framework for optimal cloud native integration of the next generation networks but also looks at the MLOps, RANOps and DevOps pipelines these networks create.

c. Generative AI for Future Networks:
Future networks provide capability to access AI at scale, as well as they are also one of the candidate application domains where AI can have ground breaking developments. Implementation of Network Digital Twins (NDTs) and agent based functional component which harness generative AI capabilities is one of the research areas we are currently exploring. We are interested in developing foundational model for telecommunication eco-system which can provide self-configuration and management capabilities.

d. Semantic Communication and Networking:
We are interested in not only exploiting semantic communication to optimally transmit information but how semantic information can be exploited to implement AI across various RAN avenues.

This list is by no-means exhaustive of what we as a team believe will unlock maximum value for future networks.