  We propose a new paradigm for telecommunications, and develop a framework
drawing on concepts from information (i.e., different metrics of complexity)
and computational (i.e., agent based modeling) theory, adapted from complex
system science. We proceed in a systematic fashion by dividing network
complexity understanding and analysis into different layers. Modelling layer
forms the foundation of the proposed framework, supporting analysis and tuning
layers. The modelling layer aims at capturing the significant attributes of
networks and the interactions that shape them, through the application of tools
such as agent-based modelling and graph theoretical abstractions, to derive new
metrics that holistically describe a network. The analysis phase completes the
core functionality of the framework by linking our new metrics to the overall
network performance. The tuning layer augments this core with algorithms that
aim at automatically guiding networks toward desired conditions. In order to
maximize the impact of our ideas, the proposed approach is rooted in relevant,
near-future architectures and use cases in 5G networks, i.e., Internet of
Things (IoT) and self-organizing cellular networks.
