Sixth-generation (6G) wireless networks are expected to support immersive and mission-critical applications requiring ultra-reliable communication, sub-second responsiveness, and multi-Gbps data rates. Dense small-cell deployments are a key enabler of these capabilities; however, the large number of candidate cells available to mobile users makes efficient user-cell association increasingly complex. Conventional signal-strength-based or heuristic approaches often lead to load imbalance, increased latency, packet loss, and inefficient utilization of radio resources. To address these challenges, this paper proposes a Knowledge-Defined Networking (KDN) framework for intelligent user association in dense 6G small-cell environments. The proposed architecture integrates the knowledge, control, and data planes to enable adaptive, data-driven decision-making. Small-cell conditions are modeled using queueing-theoretic indicators that capture traffic load and waiting-time dynamics. Based on these indicators, a joint optimization objective reflecting latency and packet loss is formulated and solved via Lagrangian relaxation to obtain globally guided association policies. These optimization outcomes are then used to supervise a lightweight Learning Vector Quantization (LVQ) model, enabling fast and scalable inference at the network edge. Extensive NS-3 simulations under varying mobility, traffic load, packet size, and network density demonstrate that the proposed approach consistently outperforms conventional baselines. The framework reduces average latency by 30-45% in high-mobility and heavy-traffic scenarios and decreases packet loss by more than 35% under congestion. The results confirm that combining optimization-driven knowledge with lightweight learning enables scalable, QoS-aware user association for future dense 6G networks.

翻译：第六代（6G）无线网络需支持沉浸式与关键任务型应用，这些应用要求具备超高可靠性通信、亚秒级响应能力及多Gbps数据速率。密集小基站部署是实现这些能力的关键使能因素；然而，移动用户可接入的候选基站数量庞大，使得高效的用户-基站关联日益复杂。传统的基于信号强度或启发式方法常导致负载不均衡、时延增加、丢包及无线资源利用效率低下。为应对这些挑战，本文提出一种面向密集6G小基站环境的智能用户关联的知识定义网络（KDN）框架。该架构融合知识平面、控制平面与数据平面，以实现自适应、数据驱动的决策。小基站状态通过排队论指标建模，以捕捉业务负载与等待时间动态特性。基于这些指标，构建了反映时延与丢包的联合优化目标，并通过拉格朗日松弛法求解，以获得全局引导的关联策略。这些优化结果随后用于监督轻量级学习向量量化（LVQ）模型，从而在网络边缘实现快速可扩展的推理。在不同移动性、业务负载、数据包大小及网络密度下进行的广泛NS-3仿真表明，所提方法持续优于传统基线方案。该框架在高移动性与高业务负载场景下将平均时延降低30-45%，在拥塞条件下将丢包率减少超过35%。结果证实，将优化驱动知识与轻量级学习相结合，可为未来密集6G网络实现可扩展、服务质量感知的用户关联。