Report

Teaser, summary, work performed and final results

Periodic Reporting for period 1 - CTO Com (Context- and Task-Oriented Communication)

Teaser

New infrastructure in 5G networks, such as cloud-RANs, caches, relay-drones, and massive MIMO arrays, bring enormous improvementsin data rates and delays even when applied naively using standard coding techniques. However, to satisfyfuture demands, new communication techniques...

Summary

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 simple example of a context-oriented communication technique that significantly improves performance (like
data rates) compared to standard techniques is presented in the following.

A second major difference between communication in distributed decision and control systems and traditional
communication scenarios is the nal 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 (as in the example below), 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 present improved context- and task-oriented coding techniques for 5G applications.
The main goal is to improve energy-efficiency and reliability of current system as well as to enable new applications.

Work performed

During the first period, the project concentrated on finding communication techniques and fundamental limits of cache-aided systems, sensor networks, and distributed big data applications. For cache-aided systems we built and analyzed secure communication techniques that keep the transmitted data secure from an external eavesdropper. We also proposed new optimal coding techniques for relay combination networks. Our contributions for sensor networks are two-fold. On one hand side we designed optimal decision systems for multi-hop networks, noisy channels, and cooperative scenarios. On the other hand, we presented improved communication techniques for sensor networks, such as new multi-antenna visible-light communication schemes and mixed-delay constrained codes. These new communication techniques can find application in various scenarios, in particular also for the coordination of distributed smart agents in cyberphysical systems. In our last line of works on big data applications we derived the fundamental limits for systems built on the MAP-Reduce framework. In particular, we proposed optimal computation schemes when some of the nodes are unreliable.

Final results

We derived the fundamental performance limits of different distributed hypothesis testing systems. Our focus was on multi-hop and noisy netowrks, which are particularly relevant for Internet of Things applications were batteries of sensors are meant to last for decades and the transmissions of the sensors thus need to be of low energy. In particular, we showed when it is optimal for the sensors to only relay the decisions at previous sensors but to ignore all the other information transmitted by these sensors. Furthermore, we illustrated the importance unequal error protection codes for communication in sensor networks. In the remainder of this project we will further consider systems with more stringent energy and delay constraints as well as investigate systems where sensors can sample and communicate sequentially and adaptively.

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 show 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.

A third line of work 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. On a parallel 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. Both communication scenarios (free space and mixed-delay) are particularly relevant to sensor networks (distributed hypothesis testing) and cyberphysical systems which have to coordinate smart agents, some of them having stringent delay constraints. In the remainder of the project we will see how we can incorporate these coding schemes into our distrbuted hypothesis testing systems.

Our forth and last line of work established optimal coding and computing schemes for the popular MAP-Reduce framework. In particular, 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.