In this paper, we address task-oriented (or goal-oriented) communications where an encoder at the transmitter learns compressed latent representations of data, which are then transmitted over a wireless channel. At the receiver, a decoder performs a machine learning task, specifically for classifying the received signals. The deep neural networks corresponding to the encoder-decoder pair are jointly trained, taking both channel and data characteristics into account. Our objective is to achieve high accuracy in completing the underlying task while minimizing the number of channel uses determined by the encoder's output size. To this end, we propose a multi-round, multi-task learning (MRMTL) approach for the dynamic update of channel uses in multi-round transmissions. The transmitter incrementally sends an increasing number of encoded samples over the channel based on the feedback from the receiver, and the receiver utilizes the signals from a previous round to enhance the task performance, rather than only considering the latest transmission. This approach employs multi-task learning to jointly optimize accuracy across varying number of channel uses, treating each configuration as a distinct task. By evaluating the confidence of the receiver in task decisions, MRMTL decides on whether to allocate additional channel uses in multiple rounds. We characterize both the accuracy and the delay (total number of channel uses) of MRMTL, demonstrating that it achieves the accuracy close to that of conventional methods requiring large numbers of channel uses, but with reduced delay by incorporating signals from a prior round. We consider the CIFAR-10 dataset, convolutional neural network architectures, and AWGN and Rayleigh channel models for performance evaluation. We show that MRMTL significantly improves the efficiency of task-oriented communications, balancing accuracy and latency effectively.

翻译：本文研究任务导向（或目标导向）通信，其中发射端的编码器学习数据的压缩潜在表示，随后通过无线信道传输。在接收端，解码器执行机器学习任务，特别是对接收信号进行分类。编码器-解码器对对应的深度神经网络通过联合训练，同时考虑信道与数据特性。我们的目标是在完成基础任务时实现高精度，同时最小化由编码器输出尺寸决定的信道使用次数。为此，我们提出一种多轮多任务学习（MRMTL）方法，用于动态调整多轮传输中的信道使用次数。发射器根据接收器的反馈逐步增加通过信道发送的编码样本数量，而接收器利用前一轮的信号来提升任务性能，而非仅考虑最新传输。该方法采用多任务学习联合优化不同信道使用次数配置下的精度，将每种配置视为独立任务。通过评估接收器在任务决策中的置信度，MRMTL决定是否在多轮中分配额外信道使用次数。我们刻画了MRMTL的精度与延迟（总信道使用次数），证明其能达到接近传统需要大量信道使用次数方法的精度，但通过融合前一轮信号降低了延迟。我们采用CIFAR-10数据集、卷积神经网络架构以及AWGN和瑞利信道模型进行性能评估，结果表明MRMTL显著提升了任务导向通信的效率，有效平衡了精度与延迟。