Energy-Efficient Scheduling in RIS-Aided MEC Networks for Collaborative Inference

Yang Yang, M. Cenk Gursoy

Research output: Chapter in Book/Entry/PoemConference contribution


In this paper, we consider a reconfigurable intelligent surface (RIS) aided mobile edge computing (MEC) network and investigate the minimization of the energy consumption for collaborative inference. Within the collaborative inference framework, the user equipments (UEs) are allowed to offload parts of computation-intensive perception services to the MEC server and thereby reduce the energy consumption subject to their latency constraints. In this setting, considering the collaborative inference task and the transmission model, we aim to minimize the global energy consumption under the UEs' latency constraints. A three-step optimization algorithm is devised to address the considered energy minimization problem in which the RIS phase shift matrix, inference partition decisions and CPU frequency allocations at the MEC server are optimally determined. Simulation results on the collaborative inference task demonstrate the effectiveness of our proposed approach in reducing the energy consumption levels while satisfying the UEs' latency constraints.

Original languageEnglish (US)
Title of host publicationICC 2023 - IEEE International Conference on Communications
Subtitle of host publicationSustainable Communications for Renaissance
EditorsMichele Zorzi, Meixia Tao, Walid Saad
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781538674628
StatePublished - 2023
Externally publishedYes
Event2023 IEEE International Conference on Communications, ICC 2023 - Rome, Italy
Duration: May 28 2023Jun 1 2023

Publication series

NameIEEE International Conference on Communications
ISSN (Print)1550-3607


Conference2023 IEEE International Conference on Communications, ICC 2023


  • Reconfigurable intelligent surface (RIS)
  • collaborative inference
  • edge computing
  • energy efficiency

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Electrical and Electronic Engineering


Dive into the research topics of 'Energy-Efficient Scheduling in RIS-Aided MEC Networks for Collaborative Inference'. Together they form a unique fingerprint.

Cite this