Link scheduling in device-to-device (D2D) networks is usually formulated as a non-convex combinatorial problem, which is generally NP-hard and difficult to get the optimal solution. Traditional methods to solve this problem are mainly based on mathematical optimization techniques, where accurate channel state information (CSI), usually obtained through channel estimation and feedback, is needed. To overcome the high computational complexity of the traditional methods and eliminate the costly channel estimation stage, machine leaning (ML) has been introduced recently to address the wireless link scheduling problems. In this paper, we propose a novel graph embedding based method for link scheduling in D2D networks. We first construct a fully-connected directed graph for the D2D network, where each D2D pair is a node while interference links among D2D pairs are the edges. Then we compute a low-dimensional feature vector for each node in the graph. The graph embedding process is based on the distances of both communication and interference links, therefore without requiring the accurate CSI. By utilizing a multi-layer classifier, a scheduling strategy can be learned in a supervised manner based on the graph embedding results for each node. We also propose an unsupervised manner to train the graph embedding based method to further reinforce the scalability and generalizability and develop a K-nearest neighbor graph representation method to reduce the computational complexity. Extensive simulation demonstrates that the proposed method is near-optimal compared with the existing state-of-art methods but is with only hundreds of training samples. It is also competitive in terms of scalability and generalizability to more complicated scenarios.

中文翻译：

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训练样本少的基于图嵌入的无线链路调度

设备到设备 (D2D) 网络中的链路调度通常被表述为非凸组合问题，这通常是 NP-hard 并且难以获得最优解。解决这个问题的传统方法主要基于数学优化技术，需要准确的信道状态信息（CSI），通常通过信道估计和反馈获得。为了克服传统方法的高计算复杂性并消除昂贵的信道估计阶段，最近引入了机器学习（ML）来解决无线链路调度问题。在本文中，我们提出了一种新的基于图嵌入的方法，用于 D2D 网络中的链路调度。我们首先为 D2D 网络构建一个全连接的有向图，其中每个 D2D 对是一个节点，而 D2D 对之间的干扰链路是边。然后我们为图中的每个节点计算一个低维特征向量。图嵌入过程基于通信链路和干扰链路的距离，因此不需要准确的 CSI。通过利用多层分类器，可以基于每个节点的图嵌入结果以监督方式学习调度策略。我们还提出了一种无监督的方式来训练基于图嵌入的方法，以进一步增强可扩展性和泛化性，并开发 K-最近邻图表示方法以降低计算复杂度。广泛的模拟表明，与现有的最先进的方法相比，所提出的方法接近最优，但只有数百个训练样本。它在可扩展性和对更复杂场景的通用性方面也具有竞争力。