Research On Key Technologies Of 5G Edge Computing Task Offloading Assisted By Physical Layer Security

The rapid development of 5G technology has led to explosive growth in various types of data in the network,which significantly increases the pressure on user devices to process and store data.However,user devices are not equipped to handle large-scale centralized computing and storage tasks.By utilizing the open capabilities of 5G networks and employing Mobile Edge Computing(MEC)technology,which is based on user-side offloading and local migration techniques,it is possible to reduce latency in communication between users and cloud networks,while simultaneously alleviating the pressure on user devices.This results in an improved quality of experience for users.Despite the potential benefits of these approaches,wireless communication networks are more susceptible to threats due to the broadcast nature of wireless channels and the rapid development of eavesdropping and simulation attack techniques.This presents new challenges for the security performance of 5G edge computing wireless communication systems.Therefore,this thesis introduces the Intelligent Reflecting Surface(IRS)technology as another means to address the Physical Layer Security(PLS)issue.By designing a reasonable communication and computation offloading model,this technology ensures the security transmission of users while improving the performance of Mobile Edge Computing(MEC)wireless communication systems.This thesis primarily investigates the key technologies of physical layer security-assisted 5G edge computing task offloading and can be divided into the following two parts:(1)In response to the security issues in MEC wireless communication networks,this thesis introduces the IRS as a means of PLS technology and proposes an efficient IRS-assisted computation offloading scheme for MEC security.In the MEC scenario with multiple users and a single eavesdropper,the IRS is intelligently adjusted to suppress the eavesdropper’s interception of user information,thereby achieving secure transmission of the system.The system model consists of a communication model and a computation offloading model.To maximize the system’s security transmission rate,a joint optimization of secure computation offloading strategy is proposed.Firstly,the user’s transmit power and IRS phase shift are solved by designing an alternating optimization algorithm and a conjugate gradient-based manifold optimization algorithm.Based on this,a heuristic algorithm is used to optimize the computation offloading ratio for each user and the computation resources of the MEC server,with the aim of minimizing the computation offloading delay of the MEC system while ensuring secure transmission for users.Simulation results show that compared to other benchmark schemes,the proposed scheme effectively reduces the total computation offloading delay while ensuring secure transmission for the MEC system.(2)To further enhance the reliability of wireless communication systems,reduce user energy consumption,and achieve secure and energy-efficient computation offloading for MEC,this thesis proposes an IRS-assisted MEC security and energy-saving computation offloading scheme.In the MEC scenario with multiple users and eavesdroppers,the system model of the proposed plan is divided into communication and computation offloading models.Based on the proposed system model,a user energy minimization problem is designed by jointly optimizing the IRS phase shift,user offloading time,transmit power,and local computation tasks.As this is a non-convex problem and cannot be solved directly,an optimal iterative algorithm is proposed to decompose the problem into two manageable subproblems.The user offloading time and IRS phase shift are optimized using the semidefinite relaxation(SDR)algorithm,while the transmit power and local computation tasks are optimized using the Dinkelbach method.Continuously iteratively update these two sets of variables to obtain the optimal solution.Simulation results demonstrate that the proposed plan can reduce user computation offloading energy consumption while ensuring secure transmission in the MEC system compared to other benchmark schemes.