In this article,the secure computation efficiency(SCE)problem is studied in a massive multipleinput multiple-output(mMIMO)-assisted mobile edge computing(MEC)*** first derive the secure transmission rate based on the ...
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In this article,the secure computation efficiency(SCE)problem is studied in a massive multipleinput multiple-output(mMIMO)-assisted mobile edge computing(MEC)*** first derive the secure transmission rate based on the mMIMO under imperfect channel state *** on this,the SCE maximization problem is formulated by jointly optimizing the local computation frequency,the offloading time,the downloading time,the users and the base station transmit *** to its difficulty to directly solve the formulated problem,we first transform the fractional objective function into the subtractive form one via the dinkelbach ***,the original problem is transformed into a convex one by applying the successive convex approximation technique,and an iteration algorithm is proposed to obtain the ***,the stimulations are conducted to show that the performance of the proposed schemes is superior to that of the other schemes.
The mobile and flexible unmanned aerial vehicle (UAV) with mobile edge computing (MEC) can effectively relieve the computing pressure of the massive data traffic in 5G Internet of Things. In this paper, we propose a n...
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The mobile and flexible unmanned aerial vehicle (UAV) with mobile edge computing (MEC) can effectively relieve the computing pressure of the massive data traffic in 5G Internet of Things. In this paper, we propose a novel online edge learning offloading (OELO) scheme for UAV-assisted MEC secure communications, which can improve the securecomputation performance. Moreover, the problem of information security is further considered since the offloading information of terminal users (TUs) may be eavesdropped due to the light-of-sight characteristic of UAV transmission. In the OELO scheme, we maximize the secure computation efficiency by optimizing TUs' binary offloading decision and resource management while guaranteeing dynamic task data queue stability and minimum secure computing requirement. Since the optimization problem is fractionally structured, binary constrained and multi-variable coupled, we first utilize the Dinkelbach method to transform the fractionally structured problem into a tractable form. Then, OELO generates the offloading decision based on deep reinforcement learning (DRL) and optimizes the resource management in an iterative manner through successive convex approximation (SCA). Simulation results show that the proposed scheme achieves better computing performance and enhances the stability and security compared with benchmarks.
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