Multi-Receiver Task-Oriented Communications via Multi-Task Deep Learning

TL;DR

This paper introduces a multi-task deep learning framework for goal-oriented wireless communication with multiple receivers, optimizing resource use and task performance in 6G networks.

Contribution

It proposes a joint training approach with a shared encoder and individual decoders for multi-receiver, multi-task communication, enhancing efficiency and adaptability.

Findings

Improved classification accuracy on MNIST, Fashion MNIST, CIFAR-10 datasets.

Reduced transmission overhead compared to single-task systems.

Effective resource allocation in varying channel conditions.

Abstract

This paper studies task-oriented, otherwise known as goal-oriented, communications, in a setting where a transmitter communicates with multiple receivers, each with its own task to complete on a dataset, e.g., images, available at the transmitter. A multi-task deep learning approach that involves training a common encoder at the transmitter and individual decoders at the receivers is presented for joint optimization of completing multiple tasks and communicating with multiple receivers. By providing efficient resource allocation at the edge of 6G networks, the proposed approach allows the communications system to adapt to varying channel conditions and achieves task-specific objectives while minimizing transmission overhead. Joint training of the encoder and decoders using multi-task learning captures shared information across tasks and optimizes the communication process accordingly. By leveraging the broadcast nature of wireless communications, multi-receiver task-oriented communications (MTOC) reduces the number of transmissions required to complete tasks at different receivers. Performance evaluation conducted on the MNIST, Fashion MNIST, and CIFAR-10 datasets (with image classification considered for different tasks) demonstrates the effectiveness of MTOC in terms of classification accuracy and resource utilization compared to single-task-oriented communication systems.