Semantic communication is a promising communication paradigm that utilizes Deep Neural Networks (DNNs) to extract the information relevant to downstream tasks, hence significantly reducing the amount of transmitted data. In current practice, the semantic communication transmitter for a specific task is typically pre-trained and shared by all users. However, due to user heterogeneity, it is desirable to use different transmitters according to the available computational and communication resources of users. In this paper, we first show that it is possible to dynamically adjust the computational and communication overhead of DNN-based transmitters, thereby achieving adaptive semantic communication. After that, we investigate the user association and resource allocation problem in a multi-cell network where users are equipped with adaptive semantic communication transmitters. To solve this problem, we decompose it into three subproblems involving the scheduling of each user, the resource allocation of each base station (BS), and the user association between users and BSs. Then we solve each problem progressively based on the solution of the previous subproblem. The final algorithm can obtain near-optimal solutions in polynomial time. Numerical results show that our algorithm outperforms benchmarks under various situations.

翻译：语义通信是一种有前景的通信范式，它利用深度神经网络（DNN）提取与下游任务相关的信息，从而显著减少传输数据量。当前实践中，特定任务的语义通信发射机通常由所有用户预训练并共享。然而，由于用户异构性，应根据用户可用的计算与通信资源采用不同的发射机。本文首先证明，可动态调整基于DNN的发射机的计算与通信开销，从而实现自适应语义通信。随后，我们研究了配备自适应语义通信发射机的用户在多小区网络中的用户关联与资源分配问题。为求解该问题，将其分解为三个子问题：每个用户的调度、每个基站（BS）的资源分配，以及用户与BS间的关联。随后基于前一子问题的解逐步求解各子问题。最终算法可在多项式时间内获得近优解。数值结果表明，所提算法在不同场景下均优于基准方案。