In semantic communications, only task-relevant information is transmitted, yielding significant performance gains over conventional communications. To satisfy user requirements for different tasks, we investigate the semantic-aware resource allocation in a multi-cell network for serving multiple tasks in this paper. First, semantic entropy is defined and quantified to measure the semantic information for different tasks. Then, we develop a novel quality-of-experience (QoE) model to formulate the semantic-aware resource allocation problem in terms of semantic compression, channel assignment, and transmit power allocation. To solve the formulated problem, we first decouple it into two subproblems. The first one is to optimize semantic compression with given channel assignment and power allocation results, which is solved by a developed deep Q-network (DQN) based method. The second one is to optimize the channel assignment and transmit power, which is modeled as a many-to-one matching game and solved by a proposed low-complexity matching algorithm. Simulation results validate the effectiveness and superiority of the proposed semantic-aware resource allocation method, as well as its compatibility with conventional and semantic communications.

翻译：在语义通信中，仅传输与任务相关的信息，相较于传统通信能实现显著的性能提升。为满足用户对不同任务的需求，本文研究了多小区网络中面向多任务服务的语义感知资源分配问题。首先，定义并量化了语义熵以衡量不同任务的语义信息量。随后，构建了一种新型体验质量（QoE）模型，从语义压缩、信道分配和发射功率分配三个角度对语义感知资源分配问题进行建模。为求解该问题，将其解耦为两个子问题：第一个子问题是在给定信道分配和功率分配结果下优化语义压缩，采用基于深度Q网络（DQN）的方法进行求解；第二个子问题则优化信道分配与发射功率，将其建模为多对一匹配博弈，并提出一种低复杂度匹配算法进行求解。仿真结果验证了所提语义感知资源分配方法的有效性与优越性，及其与传统通信和语义通信的兼容性。