Quantum Learning Based Nonrandom Superimposed Coding for Secure Wireless Access in 5G URLLC
Dongyang Xu, Pinyi Ren

TL;DR
This paper introduces a quantum learning-based coding scheme for secure, reliable, and low-latency wireless access in 5G URLLC, effectively mitigating pilot attacks and enhancing user identification accuracy.
Contribution
It proposes a novel quantum learning-based nonrandom superimposed coding method for secure pilot encoding and decoding in URLLC, improving attack resilience and system reliability.
Findings
Achieves ultra-high reliability in pilot identification under attack.
Provides closed-form expressions for failure probability.
Demonstrates low latency performance in simulations.
Abstract
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 of grant-free configuration. In grant-free URLLC, precise allocation of different pilot resources to different users that share the same time-frequency resource is essential for the next generation NodeB (gNB) to exactly identify those users under access collision and to maintain precise channel estimation required for reliable data transmission. However, this process easily suffers from attacks on pilots. We in this paper propose a quantum learning based nonrandom superimposed coding method to encode and decode pilots on multidimensional resources, such that the uncertainty of attacks can be learned quickly and eliminated precisely. Particularly, multiuser pilots for uplink access are encoded as…
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Taxonomy
TopicsWireless Communication Security Techniques · Wireless Signal Modulation Classification · Advanced MIMO Systems Optimization
