Ultra-reliable low-latency communication (URLLC) is the cornerstone for a broad range of emerging services in next-generation wireless networks. URLLC fundamentally relies on the network's ability to proactively determine whether sufficient resources are available to support the URLLC traffic, and thus, prevent so-called cell overloads. Nonetheless, achieving accurate quality-of-service (QoS) predictions for URLLC user equipment (UEs) and preventing cell overloads are very challenging tasks. This is due to dependency of the QoS metrics (latency and reliability) on traffic and channel statistics, users' mobility, and interdependent performance across UEs. In this paper, a new QoS-aware UE admission control approach is developed to proactively estimate QoS for URLLC UEs, prior to associating them with a cell, and accordingly, admit only a subset of UEs that do not lead to a cell overload. To this end, an optimization problem is formulated to find an efficient UE admission control policy, cognizant of UEs' QoS requirements and cell-level load dynamics. To solve this problem, a new machine learning based method is proposed that builds on (deep) neural contextual bandits, a suitable framework for dealing with nonlinear bandit problems. In fact, the UE admission controller is treated as a bandit agent that observes a set of network measurements (context) and makes admission control decisions based on context-dependent QoS (reward) predictions. The simulation results show that the proposed scheme can achieve near-optimal performance and yield substantial gains in terms of cell-level service reliability and efficient resource utilization.

翻译：超高可靠低时延通信（URLLC）是下一代无线网络中众多新兴服务的基石。URLLC从根本上依赖于网络主动判断是否有足够资源支持URLLC流量，从而避免所谓的"小区过载"的能力。然而，实现URLLC用户设备（UE）的精确服务质量（QoS）预测并防止小区过载是极具挑战性的任务。这是因为QoS指标（时延和可靠性）依赖于流量与信道统计特征、用户移动性以及不同UE之间的性能相互依赖。本文提出了一种新的QoS感知UE准入控制方法，在UE与小区关联之前主动预测其QoS，从而仅接纳不会导致小区过载的UE子集。为此，我们构建了一个优化问题来寻找兼顾UE QoS需求与小区级负载动态的高效UE准入控制策略。为了解决该问题，我们提出了一种基于（深度）神经上下文赌博机的新型机器学习方法——该方法特别适用于处理非线性赌博机问题。实际上，UE准入控制器被视为一个赌博机智能体，它观测网络测量值集合（上下文），并根据上下文相关的QoS（奖励）预测做出准入控制决策。仿真结果表明，所提方案能够实现近优性能，并在小区级服务可靠性和资源利用效率方面获得显著提升。