Abstract
Wireless power transfer (WPT) and cognitive radio (CR) are two promising techniques in designing mobile-edge computing (MEC) systems. In this paper, we study a robust secure wireless powered multiple-input single-output (MISO) cognitive MEC system, which integrates several techniques: physical-layer security, WPT, CR, underlay spectrum sharing and MEC. Three optimization problems are formulated to minimize the total transmission power (TTP) of the primary transmitter (PT) and the secondary base station (SBS) under perfect channel state information (CSI) model, bounded CSI error model and the probabilistic CSI error model, respectively. The formulated problems are nonconvex and hard to solve. Three two-phase iterative optimization algorithms combined with Lagrangian dual, semidefinite relaxation (SDR), S-Procedure and Bernstein-type inequalities are proposed to jointly optimize the beamforming vectors of the PT and the SBS, the central processing unit (CPU) frequency and the transmit power of the MD. Simulation results are provided to verify the effectiveness of the proposed algorithms.
Highlights
In recent years, realizing the vision of smart city driven the explosive growth the mobile devices (MDs) and the new mobile applications
We propose a robust secure wireless powered multiple-input single-output (MISO) cognitive mobile edge computing (MEC) system, which consists of a multi-antenna primary transmitter (PT), a multi-antenna secondary base station (SBS), a single-antenna
The SBS and the PT must use larger power to charge the MD, which results in the increasing of the total transmission power (TTP) of the PT and the SBS
Summary
In recent years, realizing the vision of smart city driven the explosive growth the mobile devices (MDs) and the new mobile applications. Due to the finite computing and battery capacities, the MDs can not fully accomplish computation-intensive tasks, such as virtual reality (VR), augmented reality (AR) and face recognition. The concept of mobile edge computing (MEC) has emerged. IT and cloud computing, the MEC can provide cloud-like computing capability to the MDs [1]. In MEC systems, the MDs can offload computation-intensive tasks to the proximate servers such as access points (APs) and base stations (BSs) for remote execution, which can save energy and enhance the computing capability of the MDs. Obviously, task offloading is the core of the MEC systems. Even though the offloading may decrease the energy consumption of the MDs, it still costs energy of the MDs for data transmission, The associate editor coordinating the review of this manuscript and approving it for publication was Rui Wang
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