Quantum Learning on Structured Code with Computing Traps for Secure URLLC in Industrial IoT Scenarios
IEEE Internet of Things Journal
Resilient and secure ultra-reliable low-latency communications (URLLC) over radio interface is expected to play a crucial role in next generation Industrial IoT scenarios. However, attacking wireless pilot signals has been a potential easy way to interrupt URLLC services. In this work, we propose a random structured code to encode and decode pilot signals on multidimensional physical resources, and also design a quantum learning framework to make this code secure and reliable. Specifically, the code suggests using random encoding with little structures to disperse the effect of attacks. We find that the decoding process can be modeled as a computing trap if the group spatial channel features are employed. The security problem is therefore transformed as a random computing with redundancy. We employ a quantum algorithm to learn the computing trap model such that the computing redundancy can be removed quickly while the dispersed attack can be eliminated. In this respect, we can prove the existence of the quantum black-box model corresponding to the computing trap, and derive a precise expression of computing performance. Based on the result, we can formulate novel analytical closed-form expressions of system failure probability to characterize the reliability of the URLLC. Numerical results show that the proposed system can maintain ultra-high reliability and low latency against attacks on wireless pilots.
Codes, coding theory, Computational modeling, Decoding, Encoding, Industrial IoT, quantum learning, Reliability, security, Security, Ultra reliable low latency communication, wireless communications
D. Xu, K. Yu, L. Zhen, K. -K. R. Choo and M. Guizani, "Quantum Learning on Structured Code with Computing Traps for Secure URLLC in Industrial IoT Scenarios," in IEEE Internet of Things Journal, April 2023, doi: 10.1109/JIOT.2023.3268608.