Satellite-aided communication networks play a vital role for users in remote areas. With the increasing enhancement of users' computing capacities for satellite-aided communication networks, a part of the data can be compressed first at the source node and then decompressed at the destination node to relieve the burden of the transmission. In this way, collaborative computing among multiple user pairs is achieved. Due to the limitations of the computing and communication resources, how to realize collaborative computing efficiently and allocate the communication resource optimally is the problem that needs to be addressed. In this paper, a collaborative computing and resources allocation (CCRA) method is proposed to minimize the weighted-sum latency of multiple user pairs formed by two nodes with computing capacities that exchange data via the satellite. With CCRA, the upper bound of the end-to-end latency is defined and used to decompose the problem of joint computing and resource allocation for multiple user pairs into independent problems for every user pair. And then, the optimal solutions for collaborative computing and resource allocation can be achieved with closed-form solutions and low complexity. Numerical results verify the effectiveness of CCRA in latency optimization.

中文翻译：

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多用户对卫星辅助协同计算资源优化分配

卫星辅助通信网络对偏远地区的用户起着至关重要的作用。随着用户对卫星辅助通信网络计算能力的日益增强，部分数据可以先在源节点压缩，再在目的节点解压缩，以减轻传输负担。这样就实现了多个用户对之间的协同计算。由于计算和通信资源的限制，如何高效地实现协同计算并优化分配通信资源是需要解决的问题。在本文中，提出了一种协同计算和资源分配 (CCRA) 方法，以最小化由具有计算能力的两个节点形成的多个用户对的加权和延迟，这些节点具有通过卫星交换数据的计算能力。CCRA 定义了端到端时延的上限，用于将多个用户对的联合计算和资源分配问题分解为每个用户对的独立问题。然后，可以通过封闭形式的解决方案和低复杂度来实现协同计算和资源分配的最佳解决方案。数值结果验证了 CCRA 在延迟优化中的有效性。定义了端到端延迟的上限，用于将多个用户对的联合计算和资源分配问题分解为每个用户对的独立问题。然后，可以通过封闭形式的解决方案和低复杂度来实现协同计算和资源分配的最佳解决方案。数值结果验证了 CCRA 在延迟优化中的有效性。定义了端到端延迟的上限，用于将多个用户对的联合计算和资源分配问题分解为每个用户对的独立问题。然后，可以通过封闭形式的解决方案和低复杂度来实现协同计算和资源分配的最佳解决方案。数值结果验证了 CCRA 在延迟优化中的有效性。