Machine learning models are increasingly deployed in wireless networks with stringent performance requirements. However, dynamic propagation environments and fluctuating traffic densities introduce concept drift, which complicates the ability to maintain accurate predictive machine learning models. We propose a distributed optimization framework that jointly clusters cells and trains cluster-level predictive models, enabling nodes to cooperatively predict quality of service (QoS) distributions under communication constraints. The proposed method models QoS as a multivariate Gaussian/lognormal distribution and uses a novel clustering mechanism that groups cells with similar network conditions, allowing each cell to select the most appropriate predictor without retraining new models for each cell. By leveraging block coordinate descent, our solution efficiently clusters the cells and updates the predictive models to mitigate concept drift, while maintaining a compact model set to minimize computation overhead. Evaluation using data from realistic simulations with the Sionna ray-tracer and the ns-3 simulator shows that the method converges and yields cluster constellations that adapt to changes in the network that cause concept drift. The experimental evaluation focuses on providing a prediction of the distribution latency, jitter, and RSRP over a one-hour prediction horizon. The proposed method significantly outperforms the traditional single global predictive model approach and reduces the mean absolute error by 9-27% compared to local cell-level predictors. This demonstrates that the proposed method effectively captures local variability using far fewer models through scalable distributed clustering.

翻译：机器学习模型越来越多地部署在具有严格性能要求的无线网络中。然而，动态传播环境和波动的流量密度会引发概念漂移，这给保持准确的预测性机器学习模型带来了挑战。我们提出了一种分布式优化框架，该框架联合聚类蜂窝并训练聚类级预测模型，使节点能够在通信约束下协同预测服务质量（QoS）分布。所提出的方法将QoS建模为多元高斯/对数正态分布，并使用一种新颖的聚类机制，将具有相似网络条件的蜂窝分组，从而允许每个蜂窝选择最合适的预测器，无需为每个蜂窝重新训练新模型。通过利用块坐标下降法，我们的解决方案能够高效地对蜂窝进行聚类并更新预测模型以缓解概念漂移，同时保持紧凑的模型集以最小化计算开销。使用Sionna光线追踪器和ns-3模拟器在真实仿真数据上的评估表明，该方法能够收敛，并生成适应引起概念漂移的网络变化的聚类星座。实验评估侧重于在一小时的预测范围内提供延迟、抖动和RSRP的分布预测。与传统的单一全局预测模型方法相比，所提出的方法显著更优，并且与本地蜂窝级预测器相比，将平均绝对误差降低了9-27%。这表明该方法通过可扩展的分布式聚类，使用更少的模型有效捕获了局部变异性。