论文标题
无线网络中启用无人机的机密数据收集
UAV-Enabled Confidential Data Collection in Wireless Networks
论文作者
论文摘要
这项工作首次考虑在无人机(UAV)无线网络的背景下考虑机密数据收集,该网络计划的地面传感器节点(SN)打算将机密信息传输到无人机,而不会被其他未经许可的接地SNS拦截。具体而言,全双工(FD)无人机从地面上的每个计划的SN收集数据,并生成人造噪声(AN),以防止计划的SN的机密信息被其他未安排的SNS窃听。我们首先得出了所考虑的固定利率传输的可靠性中断概率(ROP)和保密中断概率(SOP),基于我们制定优化问题,以最大程度地提高受某些特定约束的最低平均保密率(ASR)。然后,我们在一阶限制性近似技术和惩罚方法的帮助下将公式的优化问题转换为凸问题。最终的问题是广义的非线性凸编程(GNCP)并直接解决它仍然会导致高复杂性,这激发了我们将此问题进一步近似为二阶锥体程序(SOCP),以降低计算复杂性。最后,我们基于惩罚连续的凸近似(P-SCA)算法制定了迭代程序,以追求解决公式优化问题的解决方案。我们的检查表明,与基准方案相比,开发的联合设计可实现显着的性能增长。
This work, for the first time, considers confidential data collection in the context of unmanned aerial vehicle (UAV) wireless networks, where the scheduled ground sensor node (SN) intends to transmit confidential information to the UAV without being intercepted by other unscheduled ground SNs. Specifically, a full-duplex (FD) UAV collects data from each scheduled SN on the ground and generates artificial noise (AN) to prevent the scheduled SN's confidential information from being wiretapped by other unscheduled SNs. We first derive the reliability outage probability (ROP) and secrecy outage probability (SOP) of a considered fixed-rate transmission, based on which we formulate an optimization problem that maximizes the minimum average secrecy rate (ASR) subject to some specific constraints. We then transform the formulated optimization problem into a convex problem with the aid of first-order restrictive approximation technique and penalty method. The resultant problem is a generalized nonlinear convex programming (GNCP) and solving it directly still leads to a high complexity, which motivates us to further approximate this problem as a second-order cone program (SOCP) in order to reduce the computational complexity. Finally, we develop an iteration procedure based on penalty successive convex approximation (P-SCA) algorithm to pursue the solution to the formulated optimization problem. Our examination shows that the developed joint design achieves a significant performance gain compared to a benchmark scheme.