Secure Offloading in NOMA-Enabled Multi-Access Edge Computing Networks

• Published in: IEEE transactions on communications Vol. 72; no. 4; pp. 2152 - 2165
• Main Authors: Zheng, Tong-Xing, Chen, Xin, Wen, Yating, Zhang, Ning, Ng, Derrick Wing Kwan, Al-Dhahir, Naofal
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.04.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Communication system security; Complexity; Computation offloading; Decoding; Eavesdropping; Edge computing; Energy consumption; Minimization; Mobile computing; Multi-access edge computing (MEC); NOMA; non-orthogonal multiple access (NOMA); Nonorthogonal multiple access; physical layer security (PLS); Resource management; Secrecy aspects; secrecy outage probability (SOP); secure offloading; Task analysis; total energy consumption; Wireless networks
• ISSN: 0090-6778; 1558-0857
• DOI: 10.1109/TCOMM.2023.3342242

Multi-access edge computing (MEC) has been recognized as a promising technology for enhancing the computation capability for next generation wireless networks. This paper studies physical layer security for an MEC network, where multiple users desire to securely offload part of their computation tasks to a base station (BS) simultaneously using non-orthogonal multiple access (NOMA) subject to the potential overhearing of a malicious eavesdropper. The secrecy outage probability (SOP) is adopted as a secrecy performance metric of the computation offloading against eavesdropping attacks. We aim to minimize the total energy consumption of the MEC system subject to an individual SOP constraint for each user. To this end, we jointly design each user's local computing bits, the transmit power, the secrecy code rates, as well as the successive interference cancellation decoding order at the BS side. As the formulated problem is highly non-convex and challenging to solve, we propose an efficient algorithm based on penalty dual decomposition (PDD) and sequential convex approximation methods to obtain an efficient suboptimal solution. To reduce the computational complexity, we further propose a reverse recursion (RR) algorithm and derive semi-closed-form solutions to the design problem. Numerical results are presented to validate the convergence and the effectiveness of our proposed algorithms. We show that the minimal total energy consumption obtained via either the PDD or RR method approaches the optimal performance of exhaustive search as the task duration increases. It is also demonstrated that the RR algorithm can achieve a comparable performance to that of the PDD algorithm while enjoying a much lower computational complexity.