By transmitting task-related information only, semantic communications yield significant performance gains over conventional communications. However, the lack of mature semantic theory about semantic information quantification and performance evaluation makes it challenging to perform resource allocation for semantic communications, especially when multiple tasks coexist in the network. To cope with this challenge, we propose a quality-of-experience (QoE) based semantic-aware resource allocation method for multi-task networks in this paper. First, semantic entropy is defined to quantify the semantic information for different tasks, and the relationship between semantic entropy and Shannon entropy is analyzed. Then, we develop a novel QoE model to formulate the semantic-aware resource allocation in terms of semantic compression, channel assignment, and transmit power. The compatibility of the formulated problem with conventional communications is further demonstrated. To solve this problem, we decouple it into two subproblems and solved them by a developed deep Q-network (DQN) based method and a proposed low-complexity matching algorithm, respectively. Finally, simulation results validate the effectiveness and superiority of the proposed method, as well as its compatibility with conventional communications.

翻译：语义通信仅传输与任务相关的信息，相较于传统通信取得了显著的性能提升。然而，由于缺乏成熟的语义信息量化和性能评估理论，语义通信的资源分配面临挑战，尤其是在多任务共存的网络中。为应对这一挑战，本文提出了一种基于体验质量（QoE）的多任务网络语义感知资源分配方法。首先，定义了语义熵以量化不同任务的语义信息，并分析了语义熵与香农熵之间的关系。接着，构建了一种新型QoE模型，从语义压缩、信道分配和发射功率三个维度对语义感知资源分配进行建模，并进一步证明了所构建问题与传统通信的兼容性。为解决该问题，将其解耦为两个子问题，分别采用基于深度Q网络（DQN）的方法和一种低复杂度匹配算法进行求解。最终，仿真结果验证了所提方法的有效性、优越性及其与传统通信的兼容性。