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Secure wireless access in ultra-reliable low-latency communications (URLLC),which is a critical aspect of 5G security, has become increasingly importantdue to its potential support of grant-free configuration. In grant-free URLLC,precise allocation of different pilot resources to different users that sharethe same time-frequency resource is essential for the next generation NodeB(gNB) to exactly identify those users under access collision and to maintainprecise channel estimation required for reliable data transmission. However,this process easily suffers from attacks on pilots. We in this paper propose aquantum learning based nonrandom superimposed coding method to encode anddecode pilots on multidimensional resources, such that the uncertainty ofattacks can be learned quickly and eliminated precisely. Particularly,multiuser pilots for uplink access are encoded as distinguishable subcarrieractivation patterns (SAPs) and gNB decodes pilots of interest from observedSAPs, a superposition of SAPs from access users, by joint design of attack modedetection and user activity detection though a quantum learning network (QLN).We found that the uncertainty lies in the identification process of codeworddigits from the attacker, which can be always modelled as a black-box model,resolved by a quantum learning algorithm and quantum circuit. Novel analyticalclosed-form expressions of failure probability are derived to characterize thereliability of this URLLC system with short packet transmission. Simulationshow that our method can bring ultra-high reliability and low latency despiteattacks on pilots.

Quantum Learning Based Nonrandom Superimposed Coding for Secure Wireless Access in 5G URLLC