双边辅助计算卸载与资源分配方案用于空天海一体化网络
Double-Edge-Assisted Computation Offloading and Resource Allocation for Space-Air-Marine Integrated Networks
AI总结：
本文提出一种双边辅助计算卸载与资源分配方案，用于空天海一体化网络中的能量高效计算任务处理。
摘要：
本文提出了一种针对空天海一体化网络（SAMINs）的双边辅助计算卸载和资源分配方案。具体而言，我们考虑一种场景，其中无人驾驶航空器（UAVs）和低地球轨道（LEO）卫星均配备边缘服务器，为海上自主水面舰船（MASSs）提供计算服务。MASSs的部分计算任务可以卸载到UAVs和LEO卫星上，通过多接入方式同时处理。为了在延迟约束下最小化SAMINs的能量消耗，我们制定了一个优化问题，并提出能量高效的算法，共同优化LEO卫星和UAVs的卸载模式、卸载量和计算资源分配。我们进一步利用交替优化（AO）方法和分层方法将原问题分解，以获得最优解。最后，我们进行了仿真，验证了所提方案相对于基准算法的有效性和效率。
英文摘要：
In this paper, we propose a double-edge-assisted computation offloading and resource allocation scheme tailored for space-air-marine integrated networks (SAMINs). Specifically, we consider a scenario where both unmanned aerial vehicles (UAVs) and a low earth orbit (LEO) satellite are equipped with edge servers, providing computing services for maritime autonomous surface ships (MASSs). Partial computation workloads of MASSs can be offloaded to both UAVs and the LEO satellite, concurrently, for processing via a multi-access approach. To minimize the energy consumption of SAMINs under latency constraints, we formulate an optimization problem and propose energy efficient algorithms to jointly optimize offloading mode, offloading volume, and computing resource allocation of the LEO satellite and the UAVs, respectively. We further exploit an alternating optimization (AO) method and a layered approach to decompose the original problem to attain the optimal solutions. Finally, we conduct simulations to validate the effectiveness and efficiency of the proposed scheme in comparison with benchmark algorithms.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
