混沌无人机通信环境中通过基于标签的编码实现联合保密速率与认证增强
Joint Secrecy Rate Achieving and Authentication Enhancement via Tag-based Encoding in Chaotic UAV Communication Environment
AI总结：
针对混沌大规模MIMO无人机通信中的窃听与干扰威胁，提出TBE标签编码与人工噪声方案，联合优化保密速率和认证失败概率。
摘要：
在由无人机（UAV）通信网络赋能的众多新兴系统中，安全通信至关重要。为了在混沌无人机环境中保护合法通信——在该环境中，由于视距信号传播，多个对手可以轻易实施窃听和干扰——本文针对大规模多输入多输出（MIMO）无人机系统提出了一种新型可靠的一体化物理层安全机制。具体而言，首先将物理层指纹（也称为标签）嵌入每条消息中，以用于认证目的。随后，我们提出额外复用该标签作为编码每条消息的参考，从而以低成本确保保密性并增强机密性。具体来说，我们通过将明文的编码不敏感特征与密钥一起输入哈希函数，创建了一种新的双参考对称标签生成机制。在合法接收端，可基于接收到的密文对称地重新生成对解码可靠的预期标签，并通过将重新生成的参考标签与接收到的标签进行比较来执行认证。然而，非法接收端只能接收到模糊标签，该标签无法用于解码接收到的消息。此外，我们引入人工噪声（AN）来削弱窃听，以进一步减少消息泄漏。为验证所提出的基于标签的编码（TBE）方案的效率，我们构建了两个优化问题，包括遍历和保密速率最大化以及认证失败概率最小化。功率分配解分别通过凸差分（DC）规划和拉格朗日方法推导得出。仿真结果表明，与现有基于AN辅助的标签嵌入方案相比，所提出的TBE方法具有优越性能。
英文摘要：
Secure communication is crucial in many emerging systems enabled by unmanned aerial vehicle (UAV) communication networks. To protect legitimate communication in a chaotic UAV environment, where both eavesdropping and jamming become straightforward from multiple adversaries with line-of-sight signal propagation, a new reliable and integrated physical layer security mechanism is proposed in this paper for a massive multiple-input-multiple-output (MIMO) UAV system. Particularly, a physical layer fingerprint, also called a tag, is first embedded into each message for authentication purpose. We then propose to reuse the tag additionally as a reference to encode each message to ensure secrecy for confidentiality enhancement at a low cost. Specifically, we create a new dual-reference symmetric tag generation mechanism by inputting an encoding-insensitive feature of plaintext along with the key into a hash function. At a legitimate receiver, an expected tag, reliable for decoding, can be symmetrically regenerated based on the received ciphertext, and authentication can be performed by comparing the regenerated reference tag to the received tag. However, an illegitimate receiver can only receive the fuzzy tag which can not be used to decode the received message. Additionally, we introduce artificial noise (AN) to degrade eavesdropping to further decrease message leakage. To verify the efficiency of our proposed tag-based encoding (TBE) scheme, we formulate two optimization problems including ergodic sum secrecy rate maximization and authentication fail probability minimization. The power allocation solutions are derived by difference-of-convex (DC) programming and the Lagrange method, respectively. The simulation results demonstrate the superior performance of the proposed TBE approach compared to the prior AN-aided tag embedding scheme.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
