Digital twins of radio access networks require packet-level traffic generators that reproduce the size and timing of packets while remaining compact and easy to recalibrate as traffic changes. We address this need with a hybrid generator that combines a small hidden Markov model, which captures buffering, streaming, and idle states, with a mixture density network that models the joint distribution of payload length and inter-arrival time (IAT) in each state using Student-t mixtures. The state space and emission family are designed to handle heavy-tailed IAT by anchoring an explicit idle state in the tail and allowing each component to adapt its tail thickness. We evaluate the model on public traces of web, smart home, and encrypted media traffic and compare it with recent neural network and transformer based generators as well as hidden Markov baselines. Across most datasets and metrics, including average per-flow cumulative distribution functions, autocorrelation based measures of temporal structure, and Wasserstein distances between flow descriptors, the proposed generator matches the real traffic most closely in the majority of cases while using orders of magnitude fewer parameters. The full model occupies around 0.2 MB in our experiments, which makes it suitable for deployment inside digital twins where memory footprint and low-overhead adaptation are critical.

翻译：针对无线接入网数字孪生对分组级流量生成器的需求——需在保持紧凑性和易重新校准特性的同时，精确复现数据包大小与时序特征，本文提出一种混合生成模型。该模型将捕获缓冲、流式传输及空闲状态的小规模隐马尔可夫模型，与基于混合密度网络的状态相关载荷长度-到达间隔联合分布建模方法（采用学生t分布混合）进行融合。通过设计包含显式空闲状态锚定机制的状态空间与发射族结构，模型可有效处理重尾到达间隔分布，并允许各分量自适应调整尾部厚度。基于网络、智能家居及加密媒体流量的公开轨迹数据，我们将该方法与近期基于神经网络、Transformer的生成模型及隐马尔可夫基线模型进行对比评估。在包含每流累积分布函数均值、基于自相关的时序结构度量及流描述符Wasserstein距离的多数数据集与指标中，所提生成模型以数量级更少的参数实现了最优的真实流量逼近效果。实验表明，完整模型仅占用约0.2 MB存储空间，特别适用于对内存占用与低开销自适应能力具有严苛要求的数字孪生部署场景。