Incorporating both semantic level information and effectiveness level performance, the task-oriented semantic communication system has been designed for various tasks of different datatype. Although semantic communication improves the spectral utilization to some extent, indiscriminate transmission of semantic information for task-oriented semantic communication can still result in waste of wireless resources. In this paper, we propose an importance-aware joint source-channel coding (I-JSCC) framework for task-oriented semantic communications. A joint semantic-channel transmission (JSCT) mechanism is designed by selectively transmitting task-important features to reduce communication overhead. We define a new metric named task-oriented semantic spectral efficiency (TOSSE) to evaluate the effectiveness and efficiency of the proposed system, which measures the effective semantic information carried by each semantic symbol. An importance-aware semantic resource allocation problem is formulated to maximize the total TOSSE of all users by jointly optimizing the channel assignment and feature selection vector. To solve this problem, a knowledge-assisted proximal policy optimization (K-PPO) based reinforcement learning (RL) algorithm is proposed. The experimental results conducted on CIFAR100 dataset demonstrate the efficacy of the K-PPO algorithm, while also highlighting the superiority of the importance-aware semantic communication system in terms of the TOSSE.

中文翻译：

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特征重要性感知、面向任务的语义传输和优化


结合语义层信息和有效性层性能，面向任务的语义通信系统被设计用于不同数据类型的各种任务。虽然语义通信在一定程度上提高了频谱利用率，但是面向任务的语义通信不加区别地传输语义信息仍然会导致无线资源的浪费。在本文中，我们提出了一种用于面向任务的语义通信的重要性感知联合源通道编码（I-JSCC）框架。通过选择性地传输任务重要特征来设计联合语义通道传输（JSCT）机制以减少通信开销。我们定义了一个名为面向任务的语义谱效率（TOSSE）的新指标来评估所提出的系统的有效性和效率，该指标测量每个语义符号携带的有效语义信息。提出了一个重要性感知的语义资源分配问题，通过联合优化通道分配和特征选择向量来最大化所有用户的总 TOSSE。为了解决这个问题，提出了一种基于知识辅助的近端策略优化（K-PPO）的强化学习（RL）算法。在CIFAR100数据集上进行的实验结果证明了K-PPO算法的有效性，同时也凸显了重要性感知语义通信系统在TOSSE方面的优越性。