提出轻量级任务导向通信框架，适配手机等设备的算力限制。
Computation-resource-efficient Task-oriented Communications
- 设计静态与动态双模型，动态模型通过多出口神经网络按输入复杂度分配算力。
- 静态模型在传输维度、计算量和精度上均优于基线，动态模型进一步提升效率。
- 适用于移动端、无人机等算力受限场景，理论证明收敛速度为O(1/√T)。
深度学习驱动的任务导向通信（TOC）正重塑无线通信范式。然而，高计算需求在移动设备和无人机等资源受限系统中构成挑战。为此，本文提出一种新型TOC方法，包含静态与动态两个模型：静态模型在无算力约束时采用神经网络作为任务导向编码器（TOE）；动态模型在算力受限时使用带多出口的动态神经网络作为TOE，根据输入数据复杂度设置阈值进行排序，实现算力高效分配。我们分析了所提方法的收敛性，证明其收敛速率为$O\left(\frac{1}{\sqrt{T}}\right)$，其中T为一个训练轮次的长度。实验表明，静态模型在传输维度、浮点运算量（FLOPs）和精度方面均优于基线模型；动态模型可进一步提升精度并降低计算需求，为资源受限系统提供更优解决方案。
原文摘要 · Abstract (English)
The rapid development of deep-learning enabled task-oriented communications (TOC) significantly shifts the paradigm of wireless communications. However, the high computation demands, particularly in resource-constrained systems e.g., mobile phones and UAVs, make TOC challenging for many tasks. To address the problem, we propose a novel TOC method with two models: a static and a dynamic model. In the static model, we apply a neural network (NN) as a task-oriented encoder (TOE) when there is no computation budget constraint. The dynamic model is used when device computation resources are limited, and it uses dynamic NNs with multiple exits as the TOE. The dynamic model sorts input data by complexity with thresholds, allowing the efficient allocation of computation resources. Furthermore, we analyze the convergence of the proposed TOC methods and show that the model converges at rate $O\left(\frac{1}{\sqrt{T}}\right)$ with an epoch of length $T$. Experimental results demonstrate that the static model outperforms baseline models in terms of transmitted dimensions, floating-point operations (FLOPs), and accuracy simultaneously. The dynamic model can further improve accuracy and computational demand, providing an improved solution for resource-constrained systems.
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