The double-directional (DD) wireless channel model is important for realistic system design since it provides complete propagation information. While stochastic and deterministic channel models are widely adopted, and existing machine learning (ML) solutions mostly aim to align future channel realizations, these solutions are often limited to short time spans that may not be statistically significant. Moreover, because the number of multi-path components (MPCs) varies with spatial and temporal variation of the receiver (RX) and/or interacting objects (IOs), typical ML solutions that require fixed, predefined input and output shapes fall short. To curb these limitations, we propose a statistics-aided ML solution that relies on a fixed subset of MPCs selection. More specifically, we first select top-$M$ MPCs, where $M\in\mathbb{Z}^+$ is much smaller than the total number of MPCs, and construct learnable graphs to train our proposed hybrid TimesNet-TimeFilter (TNTF) model. We then use a channel statistics-aided training method to generate future top-M DD channel realizations such that the statistics calculated from these realizations matches closely with those of the actual statistics from the complete time-varying DD channel realizations. We validate the proposed solution using extensive simulations on both synthetic stochastic channel model (SCM)-based and deterministic ray-tracing-based datasets, and demonstrate its effectiveness relative to state-of-the-art baselines.

翻译：双向（DD）无线信道模型因提供完整的传播信息而对实际系统设计至关重要。尽管随机性和确定性信道模型被广泛采用，且现有机器学习（ML）方案主要致力于对齐未来信道实现，但这些方案往往局限于统计意义上可能不够显著的时间跨度。此外，由于多径分量（MPC）的数量随接收机（RX）和/或交互物体（IOs）的空间和时间变化而变化，要求固定预定输入输出形状的典型ML方案难以胜任。为克服这些限制，我们提出一种基于固定子集MPC选择的统计辅助ML方案。具体而言，我们首先选取前$M$个MPC（其中$M\in\mathbb{Z}^+$远小于MPC总数），构建可学习图结构以训练所提出的混合TimesNet-TimeFilter（TNTF）模型。随后，采用信道统计辅助训练方法生成前-M个DD信道实现，使得基于这些实现计算的统计量能够紧密匹配由完整时变DD信道实现得到的真实统计量。我们通过基于合成随机信道模型（SCM）和确定性射线追踪数据集的广泛仿真验证了所提方案，并证明了其相对于最先进基线的有效性。