This work introduces NetDiff, an expressive graph denoising diffusion probabilistic architecture that generates wireless ad hoc network link topologies. Such networks, with directional antennas, can achieve unmatched performance when the communication links are designed to provide good geometric properties, notably by reducing interference between these links while respecting diverse physical constraints. How to craft such a link assignment algorithm is yet a real problem. Deep graph generation offers multiple advantages compared to traditional approaches: it allows to relieve the network nodes of the communication burden caused by the search of viable links and to avoid resorting to heavy combinatorial methods to find a good link topology. Denoising diffusion also provides a built-in method to update the network over time. Given that graph neural networks sometimes tend to struggle with global, structural properties, we augment the popular graph transformer with cross-attentive modulation tokens in order to improve global control over the predicted topology. We also incorporate simple node and edge features, as well as additional loss terms, to facilitate the compliance with the network topology physical constraints. A network evolution algorithm based on partial diffusion is also proposed to maintain a stable network topology over time when the nodes move. Our results show that the generated links are realistic, present structural properties similar to the dataset graphs', and require only minor corrections and verification steps to be operational.

翻译：本文提出NetDiff，一种用于生成无线自组织网络链路拓扑的表达性图去噪扩散概率架构。采用定向天线的此类网络，当通信链路设计具备良好几何特性时——特别是通过减少链路间干扰并满足多样物理约束——可获得无与伦比的性能。如何设计此类链路分配算法仍是实际难题。与传统方法相比，深度图生成具有多重优势：既能减轻网络节点因搜索可行链路而产生的通信负担，又可避免采用繁重的组合方法来寻找优质链路拓扑。去噪扩散还提供了随时间更新网络的内置方法。鉴于图神经网络有时难以处理全局结构特性，我们通过交叉注意力调制令牌增强主流图Transformer，以提升对预测拓扑的全局控制能力。同时引入简洁的节点与边特征及附加损失项，以促进网络拓扑物理约束的满足。本文还提出基于部分扩散的网络演化算法，用于在节点移动时维持稳定的网络拓扑。实验结果表明，生成链路具有真实性，呈现与数据集图相似的结构特性，且仅需少量修正与验证步骤即可投入运行。