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.

翻译：本文研究在发射机与多个接收机通信的场景下的任务导向（又称目标导向）通信，其中每个接收机需基于发射机拥有的数据集（如图像）完成各自的任务。提出了一种多任务深度学习方法，通过在发射机训练公共编码器、在各接收机训练独立解码器，实现多任务完成与多接收机通信的联合优化。该方法在6G网络边缘提供高效资源分配，使通信系统能够适应变化的信道条件，在最小化传输开销的同时实现任务特定目标。基于多任务学习的编码器-解码器联合训练能捕获任务间的共享信息，并据此优化通信过程。通过利用无线通信的广播特性，多接收器任务导向通信（MTOC）减少了完成不同接收机任务所需的传输次数。在MNIST、Fashion MNIST和CIFAR-10数据集上（针对不同任务进行图像分类）的性能评估表明，相比于单任务导向通信系统，MTOC在分类精度和资源利用率方面均具有有效性。