Low-Latency Task-Oriented Communications with Multi-Round, Multi-Task Deep Learning (2411.10385v1)

Abstract: 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.

• The paper presents a novel MRMTL approach that uses iterative transmissions to dynamically adjust channel use, reducing latency while ensuring accuracy.
• It integrates CNN-based encoder-decoder networks with feedback mechanisms, achieving competitive results on CIFAR-10 under AWGN and Rayleigh channels.
• The method offers practical benefits for latency-sensitive applications like IoT, AR/VR, and V2X by effectively managing the accuracy-delay tradeoff.

Overview of Low-Latency Task-Oriented Communications with Multi-Round, Multi-Task Deep Learning

This paper investigates the domain of task-oriented communications (TOC) within NextG communication systems, wherein the central aim is to transmit precisely the information necessary for completing a task, rather than delivering unprocessed data. It introduces the innovative concept of multi-round, multi-task learning (MRMTL), which incorporates deep learning techniques to significantly enhance task efficiency, specifically by dynamically adjusting the number of channel uses.

Technical Approach

The proposed MRMTL approach systematically integrates multi-task learning to train encoder-decoder neural networks jointly, optimizing both accuracy and latency. The central mechanism involves incrementally transmitting encoded data across multiple rounds. During each subsequent round, the receiver refines task performance by leveraging information from previous transmissions. This robust framework is distinguished from single-round, single-task learning (SRSTL) systems by its capacity to dynamically allocate additional channel resources based on the confidence level of the decoder in making the correct task decisions.

MRMTL employs convolutional neural networks (CNNs) as the underlying architecture and is evaluated using the CIFAR-10 dataset under both AWGN and Rayleigh channel models. This dynamic learning scheme introduces a feedback mechanism that triggers further rounds only if the task confidence is below a predefined threshold, effectively reducing latency without compromising task accuracy.

Numerical Results

The paper presents compelling numerical results, illustrating that MRMTL achieves comparable task accuracy to conventional methods but with significantly lower delay. For instance, MRMTL operating under the AWGN channel achieves a task accuracy of 0.8030 with an average delay of approximately 6.9343 channel uses, which is a marked improvement over traditional systems requiring higher channel use numbers. Notably, the architecture demonstrates robustness under the more challenging Rayleigh channel, attaining high accuracy levels albeit with a moderately increased delay.

Implications and Future Directions

The MRMTL approach addresses the critical accuracy-delay tradeoff in TOC, thus presenting a viable solution for latency-sensitive applications such as IoT, augmented reality/virtual reality (AR/VR), and vehicle-to-everything (V2X) communications. Its inherent adaptability makes it particularly advantageous for environments where channel conditions and task requirements may fluctuate.

The paper suggests potential expansions incorporating semantic communications and integrated sensing, which could further enhance TOC's applicability in complex and dynamic systems. Hence, future research may explore advanced machine learning techniques or explore cross-domain applications to extend this paper's contributions, potentially influencing the standard practices in developing efficient and responsive communication networks.

In summary, this paper provides a structured and methodologically robust approach to reducing latency in task-oriented communications, with significant practical applications in diverse aspects of NextG networks.