Semantic communication (SemCom) has emerged as a promising paradigm that leverages Deep Neural Networks (DNNs) to extract task-relevant information, thereby substantially reducing the volume of transmitted data. In existing implementations, the semantic transceiver is typically pre-trained for a specific task and uniformly adopted by all users. However, due to user heterogeneity in computational and communication capabilities, employing a single, fixed semantic transceiver may degrade the coding efficiency and transmission robustness. To address this issue, we first demonstrate the feasibility of dynamically adjusting the computational and communication overhead of DNN-based semantic transceivers, enabling a more flexible paradigm referred to as Adaptive Semantic Communication (ASC). Building on this concept, we formulate a joint user association and resource allocation problem for ASC in 5G and beyond networks, aiming to maximize overall system utility under energy and latency constraints. However, the problem is very challenging due to the inherent interdependencies among decision variables. To tackle this complexity, we decompose the original problem into three subproblems: (i) ASC scheme selection for each user, (ii) spectrum allocation at each Small-cell Base Station (SBS), and (iii) user association across SBSs. Each subproblem is solved sequentially based on the solutions of the preceding stages. The proposed algorithm efficiently yields near-optimal solutions with polynomial-time complexity. Simulation results demonstrate our approach outperforms existing baselines under various situations.

翻译：语义通信作为一种新兴的范式，通过利用深度神经网络提取任务相关信息，显著降低了传输数据量。在现有实现中，语义收发器通常针对特定任务进行预训练，并被所有用户统一采用。然而，由于用户在计算和通信能力上的异构性，采用单一固定的语义收发器可能会降低编码效率和传输鲁棒性。为解决这一问题，我们首先论证了动态调整基于深度神经网络的语义收发器的计算与通信开销的可行性，从而支持一种更灵活的范式，称为自适应语义通信。基于此概念，我们针对5G及未来网络中的自适应语义通信，构建了一个联合用户关联与资源分配问题，旨在能量和时延约束下最大化整体系统效用。然而，由于决策变量之间固有的相互依赖关系，该问题极具挑战性。为应对这一复杂性，我们将原问题分解为三个子问题：(i) 为每个用户选择自适应语义通信方案，(ii) 在每个小蜂窝基站进行频谱分配，以及(iii) 跨小蜂窝基站的用户关联。每个子问题基于前一阶段的解依次求解。所提算法能以多项式时间复杂度高效地获得近似最优解。仿真结果表明，我们的方法在多种场景下均优于现有基线。