面向 URLLC 流量的可靠性优化用户准入控制：一种神经上下文赌博机方法
Reliability-Optimized User Admission Control for URLLC Traffic: A Neural Contextual Bandit Approach
AI总结：
本文针对 URLLC 流量提出一种基于神经上下文赌博机的 QoS 感知用户准入控制方法，通过将控制器建模为赌博机智能体，基于网络上下文主动预测 QoS 并仅准许不导致基站过载的 UE，仿真证明其能实现接近最优的服务可靠性与资源利用率。
摘要：
超可靠低延迟通信（URLLC）是下一代无线网络中广泛新兴服务的基石。URLLC 从根本上依赖于网络主动确定是否有足够资源支持 URLLC 流量的能力，从而防止所谓的基站过载。然而，为 URLLC 用户设备（UE）实现准确的服务质量（QoS）预测并防止基站过载是非常具有挑战性的任务。这是由于 QoS 指标（延迟和可靠性）依赖于流量和信道统计、用户移动性以及 UE 之间相互依赖的性能。本文开发了一种新的 QoS 感知 UE 准入控制方法，在将 URLLC UE 关联到基站之前主动估计其 QoS，并据此仅准许不会导致基站过载的 UE 子集。为此，构建了一个优化问题，以找到一种高效的 UE 准入控制策略，该策略充分考量 UE 的 QoS 需求和基站级负载动态。为解决此问题，提出了一种基于机器学习的新方法，该方法基于（深度）神经上下文赌博机，这是处理非线性赌博机问题的合适框架。实际上，UE 准入控制器被视为一个赌博机智能体，它观察一组网络测量值（上下文），并基于依赖上下文的 QoS（奖励）预测做出准入控制决策。仿真结果表明，所提方案能够实现接近最优的性能，并在基站级服务可靠性和高效资源利用方面产生显著收益。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
