Space-air-ground integrated networks (SAGINs) are emerging as a pivotal element in the evolution of future wireless networks. Despite their potential, the joint design of communication and computation within SAGINs remains a formidable challenge. In this paper, the problem of energy efficiency in SAGIN-enabled probabilistic semantic communication (PSC) system is investigated. In the considered model, a satellite needs to transmit data to multiple ground terminals (GTs) via an unmanned aerial vehicle (UAV) acting as a relay. During transmission, the satellite and the UAV can use PSC technique to compress the transmitting data, while the GTs can automatically recover the missing information. The PSC is underpinned by shared probability graphs that serve as a common knowledge base among the transceivers, allowing for resource-saving communication at the expense of increased computation resource. Through analysis, the computation overhead function in PSC is a piecewise function with respect to the semantic compression ratio. Therefore, it is important to make a balance between communication and computation to achieve optimal energy efficiency. The joint communication and computation problem is formulated as an optimization problem aiming to minimize the total communication and computation energy consumption of the network under latency, power, computation capacity, bandwidth, semantic compression ratio, and UAV location constraints. To solve this non-convex non-smooth problem, we propose an iterative algorithm where the closed-form solutions for computation capacity allocation and UAV altitude are obtained at each iteration. Numerical results show the effectiveness of the proposed algorithm.

翻译：空天地一体化网络（SAGINs）正逐渐成为未来无线网络演进的关键组成部分。尽管潜力巨大，但SAGINs中通信与计算的联合设计仍是一项艰巨的挑战。本文研究了基于SAGIN的概率语义通信（PSC）系统中的能效问题。在所考虑的模型中，一颗卫星需要通过作为中继的无人飞行器（UAV）向多个地面终端（GTs）传输数据。在传输过程中，卫星和UAV可以利用PSC技术对发送数据进行压缩，而GTs可以自动恢复缺失的信息。PSC以共享概率图为基础，该图作为收发器之间的公共知识库，允许以增加计算资源为代价实现节省资源的通信。通过分析发现，PSC中的计算开销函数是关于语义压缩比的分段函数。因此，在通信与计算之间取得平衡以实现最优能效至关重要。该联合通信与计算问题被建模为一个优化问题，目标是在时延、功率、计算能力、带宽、语义压缩比以及UAV位置等约束条件下，最小化网络的总通信与计算能耗。为了解决这个非凸非光滑问题，我们提出了一种迭代算法，在每次迭代中可获得计算能力分配和UAV高度的闭式解。数值结果验证了所提算法的有效性。