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 article 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 distinguishable subcarrier activation patterns (SAPs) and gNB decodes pilots of interest from observed SAPs, a superposition of SAPs from access users, by joint design of attack mode detection and user activity detection though a quantum learning network (QLN). We found that the uncertainty lies in the identification process of codeword digits from the attacker, which can be always modelled as a black-box model, resolved by a quantum learning algorithm and quantum circuit. Novel analytical closed-form expressions of failure probability are derived to characterize the reliability of this URLLC system with short packet transmission. Simulations how that our method can bring ultra-high reliability and low latency despite attacks on pilots.

中文翻译：

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基于量子学习的非随机叠加编码，用于 5G URLLC 中的安全无线接入


超可靠低延迟通信 (URLLC) 中的安全无线接入是 5G 安全的一个关键方面，由于其潜在支持无授权配置而变得越来越重要。在无授权 URLLC 中，将不同导频资源精确分配给共享相同时频资源的不同用户对于下一代 NodeB (gNB) 准确识别接入冲突的用户并保持可靠所需的精确信道估计至关重要。数据传输。然而，这个过程很容易受到飞行员的攻击。我们在本文中提出了一种基于量子学习的非随机叠加编码方法，对多维资源上的导频进行编码和解码，从而可以快速学习并精确消除攻击的不确定性。特别是，用于上行链路接入的多用户导频被编码为可区分的子载波激活模式（SAP），并且 gNB 通过量子学习联合设计攻击模式检测和用户活动检测，从观察到的 SAP（来自接入用户的 SAP 的叠加）中解码感兴趣的导频。网络（QLN）。我们发现，不确定性在于攻击者对码字数字的识别过程，它总是可以建模为黑盒模型，通过量子学习算法和量子电路来解决。推导了新颖的故障概率分析封闭式表达式来表征短包传输的 URLLC 系统的可靠性。模拟我们的方法如何在飞行员遭受攻击的情况下带来超高可靠性和低延迟。