Network Slicing for NOMA-Enabled Edge Computing

Mohammad Arif Hossain, Nirwan Ansari

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


The 5G network presents a new horizon with tremendous opportunities for future generation wireless networks. Mobile edge computing (MEC), non-orthogonal multiple access (NOMA), and network slicing (NS) are some of the key enablers for 5G. MEC reduces the latency to a great extent for a wireless network, while NOMA gives access to more users with resource constraints. NS provides users with a better quality of service and network operators with more flexibility. In this work, we propose an NS technique enabled with NOMA for a MEC network. The proposed NS technique improves service latency for MEC users and reduces the unnecessary allocation of radio resources in NOMA. The saved resources can be leveraged to accommodate more users, thus increasing the spectral efficiency of the network. We consider different types of services based on the task completion time of users in this work. The primary focus is to optimize the total energy consumption for wireless uplink transmission for the NOMA-enabled sliced MEC network. We also propose a heuristic algorithm as an alternative to reduce the time and computational complexities of the optimization algorithm and simulate the results extensively to show the effectiveness of our proposed algorithm.

Original languageEnglish (US)
JournalIEEE Transactions on Cloud Computing
StateAccepted/In press - 2021

All Science Journal Classification (ASJC) codes

  • Software
  • Information Systems
  • Hardware and Architecture
  • Computer Science Applications
  • Computer Networks and Communications


  • 5G mobile communication
  • Computational modeling
  • Energy consumption
  • Energy Efficiency
  • Mobile Edge Computing
  • Network Slicing
  • NOMA
  • NOMA
  • Resource management
  • Spectral Efficiency
  • Task analysis
  • Uplink
  • Uplink Energy Consumption Minimization


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