Semantic communication has gained attention as a key enabler for intelligent and context-aware communication. However, one of the key challenges of semantic communications is the need to tailor the resource allocation to meet the specific requirements of semantic transmission. In this paper, we focus on networks with limited resources where devices are constrained to transmit with limited bandwidth and power over large distance. Specifically, we devise an efficient strategy to select the most pertinent semantic features and participating users, taking into account the channel quality, the transmission time, and the recovery accuracy. To this end, we formulate an optimization problem with the goal of selecting the most relevant and accurate semantic features over devices while satisfying constraints on transmission time and quality of the channel. This involves optimizing communication resources, identifying participating users, and choosing specific semantic information for transmission. The underlying problem is inherently complex due to its non-convex nature and combinatorial constraints. To overcome this challenge, we efficiently approximate the optimal solution by solving a series of integer linear programming problems. Our numerical findings illustrate the effectiveness and efficiency of our approach in managing semantic communications in networks with limited resources.

翻译：语义通信作为一种实现智能和上下文感知通信的关键技术，已受到广泛关注。然而，语义通信的主要挑战之一是需要定制资源分配以满足语义传输的特定要求。本文聚焦于资源受限网络，其中设备需在有限带宽和功率条件下进行远距离传输。具体而言，我们设计了一种高效策略，综合考虑信道质量、传输时间和恢复精度，以选择最相关的语义特征和参与用户。为此，我们构建了一个优化问题，目标是在满足传输时间和信道质量约束的前提下，从设备中选择最相关且最准确的语义特征。该问题涉及通信资源优化、参与用户识别以及特定语义信息的选择。由于该问题具有非凸性和组合约束，其复杂度较高。为克服这一挑战，我们通过求解一系列整数线性规划问题来高效逼近最优解。数值结果验证了所提方法在管理资源受限网络中的语义通信方面的有效性和高效性。