In this paper, the problem of joint transmission and computation resource allocation for a multi-user probabilistic semantic communication (PSC) network is investigated. In the considered model, users employ semantic information extraction techniques to compress their large-sized data before transmitting them to a multi-antenna base station (BS). Our model represents large-sized data through substantial knowledge graphs, utilizing shared probability graphs between the users and the BS for efficient semantic compression. The resource allocation problem is formulated as an optimization problem with the objective of maximizing the sum of equivalent rate of all users, considering total power budget and semantic resource limit constraints. The computation load considered in the PSC network is formulated as a non-smooth piecewise function with respect to the semantic compression ratio. To tackle this non-convex non-smooth optimization challenge, a three-stage algorithm is proposed where the solutions for the receive beamforming matrix of the BS, transmit power of each user, and semantic compression ratio of each user are obtained stage by stage. Numerical results validate the effectiveness of our proposed scheme.

翻译：本文研究多用户概率语义通信（PSC）网络中联合传输与计算资源分配问题。在所考虑模型中，用户采用语义信息提取技术对大尺寸数据进行压缩，随后将压缩数据传输至多天线基站（BS）。本模型通过构建大规模知识图谱表征数据，并利用用户与基站间共享的概率图实现高效语义压缩。将资源分配问题建模为在总功率预算和语义资源限制约束下最大化所有用户等效速率之和的优化问题。PSC网络中的计算负载被建模为关于语义压缩比的非光滑分段函数。针对这一非凸非光滑优化挑战，提出一种三阶段算法，依次求解基站接收波束赋形矩阵、用户发射功率和用户语义压缩比。数值结果验证了所提方案的有效性。