Robust Computation Offloading in Fog Radio Access Network with Fronthaul Compression

Jinghong Tan, Tsung Hui Chang, Kun Guo, Tony Q.S. Quek

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
JournalIEEE Transactions on Wireless Communications
DOIs
Publication statusAccepted/In press - 2021

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Applied Mathematics

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