Semantic communication, recognized as a promising technology for future intelligent applications, has received widespread research attention. Despite the potential of semantic communication to enhance transmission reliability, especially in low signal-to-noise (SNR) environments, the critical issue of resource allocation and compatibility in the dynamic wireless environment remains largely unexplored. In this paper, we propose an adaptive semantic resource allocation paradigm with semantic-bit quantization (SBQ) compatibly for existing wireless communications, where the inaccurate environment perception introduced by the additional mapping relationship between semantic metrics and transmission metrics is solved. In order to investigate the performance of semantic communication networks, the quality of service for semantic communication (SC-QoS), including the semantic quantization efficiency (SQE) and transmission latency, is proposed for the first time. A problem of maximizing the overall effective SC-QoS is formulated by jointly optimizing the transmit beamforming of the base station, the bits for semantic representation, the subchannel assignment, and the bandwidth resource allocation. To address the non-convex formulated problem, an intelligent resource allocation scheme is proposed based on a hybrid deep reinforcement learning (DRL) algorithm, where the intelligent agent can perceive both semantic tasks and dynamic wireless environments. Simulation results demonstrate that our design can effectively combat semantic noise and achieve superior performance in wireless communications compared to several benchmark schemes. Furthermore, compared to mapping-guided paradigm based resource allocation schemes, our proposed adaptive scheme can achieve up to 13% performance improvement in terms of SC-QoS.

翻译：语义通信作为未来智能应用的一项有前景技术，已获得广泛研究关注。尽管语义通信在提升传输可靠性方面具有潜力，尤其在低信噪比环境下，但动态无线环境中资源分配与兼容性的关键问题仍鲜有探索。本文提出一种自适应语义资源分配范式，通过语义比特量化与现有无线通信兼容，解决了因语义度量与传输度量之间新增映射关系而引入的环境感知不准确问题。为研究语义通信网络性能，首次提出语义通信服务质量指标，包括语义量化效率与传输时延。通过联合优化基站的发射波束赋形、语义表示比特数、子信道分配及带宽资源分配，构建了最大化整体有效SC-QoS的问题。针对该非凸优化问题，提出一种基于混合深度强化学习算法的智能资源分配方案，其中智能体可同时感知语义任务与动态无线环境。仿真结果表明，与几种基准方案相比，所提设计可有效对抗语义噪声，并在无线通信中实现更优性能。此外，与基于映射引导范式的资源分配方案相比，所提自适应方案在SC-QoS方面可实现高达13%的性能提升。