4.7 Article

Secure Transmission Rate of Short Packets With Queueing Delay Requirement

期刊

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TWC.2021.3094615

关键词

Wireless communication; Power control; Delays; Communication system security; Channel estimation; Cryptography; Quality of service; Short-packet transmission; queueing delay; unsupervised deep learning; physical layer security

资金

  1. National Research Foundation, Singapore
  2. Infocomm Media Development Authority under its Future Communications Research and Development Program
  3. MOE ARF Tier 2 [T2EP20120-0006]

向作者/读者索取更多资源

Physical layer security (PLS) is promising for secure short-packet transmissions in ultra-reliable and low-latency communications. The application of PLS faces challenges including the lack of accurate channel state information (CSI) and high computational complexity. This study compares different scenarios and proposes optimal power control policies to address these issues. The results show that learning-based power control policies outperform existing policies, and knowledge of eavesdropper's CSI only brings marginal gain.
Physical layer security (PLS) is promising for secure short-packet transmissions in ultra-reliable and low-latency communications. The bottlenecks of applying PLS in practice include 1) lack of accurate channel state information (CSI) of both the intended user and the eavesdropper; 2) high computational complexity for solving optimization problems. To address the first issue, we compare the secure transmission rates of short packets in different scenarios (i.e., with/without eavesdropper's instantaneous CSI and with/without channel estimation errors) and derive the closed-form optimal power control policy in a special case. To find numerical solutions in general cases, we apply an unsupervised deep learning method, which has low complexity after the training stage. Through numerical results, we obtain the following three key findings: 1) The learning-based power control policy approaches the closed-form optimal policy in the special case and outperforms two existing power control policies in general cases. 2) Knowing the instantaneous CSI of the eavesdropper only provides a marginal gain of the secure data rate in the high signal-to-noise ratio regime. 3) In the presence of channel estimation errors, the learning-based policy trained by the estimated channels can guarantee the average transmit power constraint, while the closed-form policy cannot.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据