Reinforcement Learning Based Latency Minimization in Secure NOMA-MEC Systems with Hybrid SIC

Kaidi Wang, Haodong Li, Zhiguo Ding, Pei Xiao

Research output: Contribution to journalArticlepeer-review

14 Scopus citations


In this paper, physical layer security (PLS) in a non-orthogonal multiple access (NOMA)-based mobile edge computing (MEC) system is investigated, where hybrid successive interference cancellation (SIC) decoding is considered. Specifically, users intend to complete confidential tasks with the help of the MEC server, while an eavesdropper attempts to intercept the offloaded tasks. By jointly designing computational resource allocation, task assignment, and power allocation, a latency minimization problem is formulated. Based on the interactions between local computing time and MEC processing time, the closed-from solutions of computational resource allocation and task assignment are derived. After that, a strategy selection mechanism is established to select offloading strategies based on the corresponding conditions. Moreover, according to the analysis of hybrid SIC decoding, the conditions of different decoding orders in secure NOMA networks are derived. Furthermore, a reinforcement learning based algorithm is proposed to solve the power allocation problems for NOMA and OMA offloading strategies. This work is extended to a multi-user scenario, in which a matching-based algorithm is proposed to solve the formulated sub-channel assignment problem. Simulation results indicate that: i) the proposed solution can significantly reduce the latency and provide dynamic strategy selection for various scenarios; ii) the NOMA offloading strategy with hybrid SIC decoding can outperform other strategies in the considered system.

Original languageBritish English
Pages (from-to)408-422
Number of pages15
JournalIEEE Transactions on Wireless Communications
Issue number1
StatePublished - 1 Jan 2023


  • Mobile edge computing (MEC)
  • non-orthogonal multiple access (NOMA)
  • physical layer security (PLS)
  • reinforcement learning
  • sub-channel assignment


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