Semantic Communication for Cooperative Multi-Task Processing over Wireless Networks

TL;DR

The concept of a “semantic source”, allowing multiple semantic interpretations from a single observation, is introduced, and an end-to-end optimization problem taking into account the communication channel is formulated to design the semantic encoding and decoding process exploiting the statistical relations between semantic variables.

Abstract

In this paper, we investigated semantic communication for multi-task processing using an information-theoretic approach. We introduced the concept of a "semantic source", allowing multiple semantic interpretations from a single observation. We formulated an end-to-end optimization problem taking into account the communication channel, maximizing mutual information (infomax) to design the semantic encoding and decoding process exploiting the statistical relations between semantic variables. To solve the problem we perform data-driven deep learning employing variational approximation techniques. Our semantic encoder is divided into a common unit and multiple specific units to facilitate cooperative multi-task processing. Simulation results demonstrate the effectiveness of our proposed semantic source and system design when statistical relationships exist, comparing cooperative task processing with independent task processing. However, our findings highlight that cooperative multi-tasking is not always beneficial, emphasizing the importance of statistical relationships between tasks and indicating the need for further investigation into the semantically processing of multiple tasks.

Figures (6)

• Figure 1: Probabilistic graphical modeling of the semantic source.
• Figure 2: Illustration of the cooperative multi-task semantic communication system model.
• Figure 3: Impact of the CU on task execution for Task1.
• Figure 4: Impact of the CU on task execution for Task2.
• Figure 5: Impact of the CU on simplification of the SUs.