New infrastructure in 5G networks, such as cloud-RANs, caches, relay-drones, and massive MIMO arrays, bring enormous improvements in data rates and delays even when applied naively using standard coding techniques. However, to satisfy future demands, new communication techniques perfectly tailored to exploit such infrastructure will be essential. In fact, the communication scenarios induced by these new elements differ from traditional scenarios significantly with respect to the terminals' contexts. For example, in networks with caches (i.e. additional storage spaces), terminals can download parts of their desired data directly from closeby caches. On the other hand, relay-drones can reinforce a transmit signal so as to provide a terminal with a stronger second observation of a desired signal. Finally, cloud-RANs allow terminals to obtain information about other transmit and receive signals. There is thus a basic need for highly improved context-oriented communication techniques. This need is even more immediate in view of the rapidly increasing number of distributed decision and control systems, where the various nodes measure highly correlated signals, and thus have a priori side-information about other nodes' signals. A second major difference between communication in distributed decision and control systems and traditional communication scenarios is their task. In these systems the final task is no more to convey and reconstruct sequences of data bits or observed signals, but to make distributed decisions or take distributed actions that attain a common goal. The traditional approach uses standard LDPC, Polar, or Turbo codes to exchange scalar- or vector-quantizations of the signals measured at the various nodes, and then runs decision, control, or prediction algorithms locally based on all the accumulated information. This approach can be highly suboptimal. In particular for situations where the decisions take value in a small range, the approach can lead to huge amounts of unnecessarily transmitted data. With new, task-oriented communication techniques we aim to drastically reduce this overhead.
The goal of this project is to devise improved context- and task-oriented coding techniques for timely and important applications such as:
- Cache-aided networks.
- Optical wireless systems.
- Networks with mixed-delay traffics such as 5G.
- Coordination of autonomous agents and joint communication and sensing tasks as required for autonomous driving.
- Distributed hypothesis testing systems as encountered in the Internet of Things (IoT).
- Distributed computing systems as used for running heavy machine learning applications.
- Age of Information systems as encountered in time-critical communication systems.
