Periodic Reporting for period 2 - CTO Com (Context- and Task-Oriented Communication)
Reporting period: 2018-11-01 to 2020-04-30
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.
In another line of work, we presented and analyzed new secure and non-secure cache-aided communication techniques based on our piggyback coding idea. We further showed that in heterogeneous networks, it is beneficial to assign cache memories in an asymmetric way, according to the strengths of the channels. In the remainder of this project we will implement our coding techniques on a platform and hopefully show their relevance in improving practical cache-aided communication systems.
In yet another line of work we designed new coding techniques that can accommodate different information flows, some of them subject to very limited delay constraints. We further prove that the coding schemes attain the fundamental performance limits in certain scenarios. Such mixed-delay networks are particularly important also for control applications or coordination of smart agents because these applications typically have very stringent delay constraints but coexist with applications that are less sensitive to delays. In futures work we will concentrate both on communication for control and on communication for coordination purposes.
Our forth line of work established optimal coding and computing schemes for the popular MAP-Reduce framework. In particular, we treated scenarios with unreliable computing nodes, where any node can fail with a certain probability. In the remainder of this project we will consider other big data applications such as clustering or analysis of deep neural networks.
Our last line of work of ours considers new coding techniques for multi-antenna free-space (visible light or infra-red) communication where we proposed a new antenna cooperation strategy and showed that it is optimal at high and low signal-to-noise ratio (SNR), and it achieves improved performances also in the moderate SNR regime.
To summarize, in this project we made significant progress with new context- and task-oriented communication techniques for various applications. In the rest of the project we will further investigate these scenarios but also branch out to other practically relevant setups.