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：本文研究了多用户概率语义通信（PSC）网络中联合传输与计算资源分配的问题。在所考虑的模型中，用户采用语义信息提取技术对大尺寸数据进行压缩，然后将其传输至多天线基站（BS）。我们的模型通过大规模知识图谱表示大尺寸数据，并利用用户与BS之间的共享概率图实现高效的语义压缩。该资源分配问题被形式化为一个优化问题，其目标是在总功率预算与语义资源限制约束下，最大化所有用户的等效速率之和。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。为应对这一非凸非光滑优化挑战，提出了一种三阶段算法，依次求解基站的接收波束赋形矩阵、各用户的发射功率以及各用户的语义压缩比。数值结果验证了所提方案的有效性。