Deployed with computation resources, fog radio access network (F-RAN) provides a promising solution for computation offloading. To take full advantage of two-tier computing in the F-RAN, on one hand, it is inevitable to design, between edge and cloud, an efficient and flexible fronthaul transmission strategy, and fronthaul resource allocation should be jointly optimized with allocation of tasks and other resources. On the other hand, a robust computation provisioning strategy that can avoid failures caused by estimation errors of available computation resources is necessary. In this work, considering the fronthaul compression and the uncertain computation capacity, we design an energy-efficient computation offloading mechanism in the F-RAN. The formulated problem is challenging to solve due to coupled communication and computation resource constraints and binary variables for task placement. We show that the problem can be recast as a convex problem if binary variables are relaxed. On top of this result, we propose an efficient algorithm to find a stationary solution. Through simulation, we demonstrate that the proposed algorithm outperforms the baseline algorithm significantly and converges to the near-optimal point solution. Besides, we compare the F-RAN with single-tier computing systems and show the excellence of the F-RAN in energy conservation for mobile devices.

中文翻译：

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具有前传压缩的雾无线接入网络中的鲁棒计算卸载


雾无线接入网络（F-RAN）部署了计算资源，为计算卸载提供了一种有前景的解决方案。为了充分利用F-RAN中的两层计算，一方面必须在边缘和云端之间设计高效灵活的前传传输策略，前传资源分配与任务分配共同优化和其他资源。另一方面，需要一种鲁棒的计算配置策略来避免由于可用计算资源的估计错误而导致的失败。在这项工作中，考虑到前传压缩和不确定的计算能力，我们在F-RAN中设计了一种节能的计算卸载机制。由于耦合的通信和计算资源限制以及任务放置的二进制变量，所提出的问题很难解决。我们证明，如果放松二元变量，则该问题可以重新转换为凸问题。在此结果之上，我们提出了一种有效的算法来寻找平稳解。通过仿真，我们证明所提出的算法显着优于基线算法并收敛到接近最优的点解。此外，我们将F-RAN与单层计算系统进行比较，展示F-RAN在移动设备节能方面的卓越性能。