User Selection for Secure Massive Mimo Based Mobile Edge Computing With Delay-Sensitive Applications

Abstract

Mobile edge computing (MEC) has been a promising technology that leverages cloud computing capabilities at the network edge to address compute-intensive and delay-sensitive applications of mobile users with limited resources. Employing massive multiple-input multiple-output (mMIMO) and nonorthogonal multiple access (NOMA) in the MEC system facilitates simultaneous task offloading for multiple users, resulting in increased spectral efficiency and decreased offloading delay. Despite the great potential of the mMIMO-NOMA-based MEC system, offloading computation tasks to MEC servers can introduce inherent security concerns and vulnerabilities. We address a notable gap in the existing literature by investigating the effect of user selection to minimize the delay in MEC while enhancing the security of this framework. Specifically, this paper presents a user selection strategy for an uplink mMIMO-NOMA-based secure MEC system in the presence of a malicious eavesdropper (Eve) to minimize offloading and computing delays, subject to the transmit power, computing resource, and secrecy rate constraints with remote computing. We propose a two-step secure user selection algorithm and solve the optimization problem with the active-set algorithm. The simulation results demonstrate the effectiveness of the proposed user selection strategy on secure MEC with a malicious Eve by minimizing the task execution delay compared to the benchmark schemes.