Simultaneous wireless information and power transfer (SWIPT) becomes more and more popular in cognitive radio (CR) networks, as it can increase the resource reuse rate of the system and extend the user’s lifetime. Due to the deployment of energy harvesting nodes, traditional secure beamforming designs are not suitable for SWIPT-enabled CR networks as the power control and energy allocation should be considered. To address this problem, a dedicated green edge power grid is built to realize energy sharing between the primary base stations (PBSs) and cognitive base stations (CBSs) in SWIPT-enabled mobile edge computing (MEC) systems with CR. The energy and computing resource optimal allocation problem is formulated under the constraints of security, energy harvesting, power transfer, and tolerable interference. As the problem is nonconvex with probabilistic constraints, approximations based on generalized Bernstein-type inequalities are adopted to transform the problem into solvable forms. Then, a robust and secure artificial noise- (AN-) aided beamforming algorithm is presented to minimize the total transmit power of the CBS. Simulation results demonstrate that the algorithm achieves a close-to-optimal performance. In addition, the robust and secure AN-aided CR based on SWIPT with green energy sharing is shown to require a lower transmit power compared with traditional systems.

中文翻译：

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具有认知无线电的SWIPT感知移动边缘计算系统中的AN辅助安全波束成形

同时无线信息和功率传输（SWIPT）在认知无线电（CR）网络中变得越来越普遍，因为它可以提高系统的资源重用率并延长用户的寿命。由于能量收集节点的部署，传统的安全波束成形设计不适用于启用SWIPT的CR网络，因为应考虑功率控制和能量分配。为了解决此问题，在具有CR的启用SWIPT的移动边缘计算（MEC）系统中，建立了专用的绿色边缘电网，以实现主要基站（PBS）与认知基站（CBS）之间的能量共享。能源和计算资源的最佳分配问题是在安全性，能量收集，功率传输和可容忍的干扰的约束下制定的。由于问题是具有概率约束的非凸问题，因此采用基于广义伯恩斯坦型不等式的近似将问题转化为可解形式。然后，提出了一种鲁棒且安全的人工噪声（AN）辅助波束成形算法，以最小化CBS的总发射功率。仿真结果表明，该算法取得了接近最佳的性能。此外，与传统系统相比，基于SWIPT且具有绿色能量共享的强大而安全的AN辅助CR被证明需要更低的发射功率。仿真结果表明，该算法取得了接近最佳的性能。此外，与传统系统相比，基于SWIPT且具有绿色能量共享的强大而安全的AN辅助CR被证明需要更低的发射功率。仿真结果表明，该算法取得了接近最佳的性能。此外，与传统系统相比，基于SWIPT且具有绿色能量共享的强大而安全的AN辅助CR被证明需要更低的发射功率。