This paper designs a helper-assisted offloading strategy in non-orthogonal multiple access enabled mobile edge computing systems, in order to guarantee the quality of service of the energy/delay-sensitive user equipments (UEs). To achieve a tradeoff between the energy consumption and the delay, we introduce a performance metric called energy-delay tradeoff. Aiming at the maximal energy-delay tradeoff minimization, the joint optimization of user association, resource block (RB) assignment, power allocation, task assignment, and computation resource allocation is formulated as a non-convex problem with coupled continuous and 0-1 variables. To tackle this challenging problem, we decompose it as a two-level problem. For the inner-level problem, an iterative parametric convex approximation (IPCA) algorithm is proposed. Then, based on the solution obtained from the inner-level problem, we model the outer-level problem as a four-sided matching problem, and then propose a low-complexity four-sided UE-RB-helper-server matching (FS-URHSM) algorithm. Theoretical analysis demonstrates that the IPCA algorithm can converge to a stationary Karush-Kuhn-Tucker (KKT) point and the FS-URHSM algorithm is guaranteed to converge to a stable matching with polynomial complexity. Simulation results demonstrate the superior performance of proposed algorithms in terms of the energy consumption and the delay.

Abstract This paper investigates low‐latency offloading strategy in a non‐orthogonal multiple access aided mobile edge computing (NOMA‐MEC) system consisting of K edge servers, one mobile user and one cloud server. An intelligent edge server selection strategy (IESSS) based on Markov decision process (MDP) is proposed to select an edge server, in order to reduce the task completion latency of this system. When an edge server is selected by the proposed IESSS, a joint optimization problem of power allocation and task scheduling factors is formulated to minimize the task completion latency of the hybrid NOMA‐MEC system. To solve the formulated non‐convex optimization problem with coupled variables, a low‐complexity adaptive power‐task resource allocation iterative (APTRAI) algorithm is proposed. Simulation results demonstrate the advantages of the proposed IESSS and verify the convergence and time complexity of the proposed APTRAI algorithm.

This paper develops a multi-helper non-orthogonal multiple access (NOMA)-enabled mobile edge computing (MEC) system, in order to support massive connectivity. To achieve a tradeoff between the energy consumption and delay, we introduce a novel performance metric, called energy-delay tradeoff, which is defined as the weighted sum of energy consumption and delay. The joint optimization of helper clustering, power allocation and task assignment is formulated as a mixed integer nonlinear programming problem with the aim of minimizing the energy-delay tradeoff. The formulated problem with coupled and 0-1 variables belongs to the NP-hard problem, which cannot be directly solved within polynomial time. To efficiently solve such a challenging problem, we first decouple it into a power allocation and task assignment (PATA) subproblem. Then, with the solution obtained from the PATA subproblem, we equivalently reformulate the original problem as a discrete helper clustering (DHC) problem. For the PATA subproblem, a successive convex approximation (SCA)-based algorithm is proposed. Then, based on the solution obtained from the PATA subproblem, we design a low-complexity matching-based clustering (MBC) algorithm to solve the DHC problem. Simulation results are provided to demonstrate the effectiveness of our proposed algorithm in the compromise of energy consumption and delay.

Abstract This study investigates the physical‐layer security of a terrestrial communication system in the presence of potential unmanned aerial vehicle (UAV) eavesdroppers (UEDs). Due to the line‐of‐sight channels of ground‐to‐air links and the broadcast characteristics of wireless channels, the UEDs have a better chance to eavesdrop terrestrial communication systems compared with the conventional ground eavesdroppers. It indicates that the emerging of UEDs brings new challenges to secure transmissions in terrestrial communication systems. In this study, a UAV is recruited to wisely jam the UEDs by adopting a dynamic three‐dimensional (3D) trajectory, in order to protect the legitimate transmission. To maximally improve the average secrecy rate, the joint optimization of the 3D trajectory of the UAV jammer and legitimate user scheduling as a non‐convex mixed‐integer optimization problem is formulated. To efficiently solve such a challenging problem, a novel enhanced genetic algorithm (EGA), where a combined encoding method and a fitness‐based crossover method are developed to improve the convergence performance, is proposed. Simulation results demonstrate the superior performance of the proposed EGA in terms of the average secrecy rate.