反向散射(电子邮件)
计算机科学
隐蔽的
工程类
电信
电气工程
计算机硬件
无线
语言学
哲学
作者
Huan Li,Yinghui Ye,Lu Lv,Guangyue Lu,Naofal Al‐Dhahir
出处
期刊:IEEE Transactions on Vehicular Technology
[Institute of Electrical and Electronics Engineers]
日期:2024-02-20
卷期号:73 (7): 10150-10163
标识
DOI:10.1109/tvt.2024.3367449
摘要
Most of the existing works in the area of covert backscatter communication (BackCom) neglect residual hardware impairments (RHIs) that are unavoidable in practical radio frequency (RF) transceivers, and may have an impact on the communication covertness and reliability. Motivated by this fact, in this paper, we investigate a covert cooperative BackCom network under RHIs. To characterize the communication covertness, we derive an analytical expression for the eavesdropper's detection error probability by assuming the availability of statistical channel state information (CSI). To assess the communication reliability, a closed-form expression for the system outage probability is derived via a high transmit signal-to-noise (SNR) approximation. Using the above results, we prove that RHIs can enhance the communication covertness but significantly degrade outage performance. To further improve the covert performance, we propose to maximize the minimum detection error probability by jointly optimizing the power reflection coefficient and the detection threshold while satisfying the communication reliability requirement. As the formulated problem is non-convex, we use a block coordinated descent (BCD) method to decompose it into two sub-problems, one of which is shown to be convex and the other one is a decreasing function with respect to the power reflection coefficient in the high transmit SNR regime. Then, we develop a BCD-based iterative algorithm to obtain its locally optimal solutions. Simulation results are provided to verify the correctness of our analytical results and the superior performance of the proposed iterative algorithm compared with the baseline schemes in terms of covertness under the same communication-reliability constraint.
科研通智能强力驱动
Strongly Powered by AbleSci AI