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

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