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Paper by Prof. Guanding Yu's Team on Application of Machine Learning in Wireless Networks Published on Top Journal IEEE TWC


In April 2021, IEEE Transactions on Wireless Communications (IF: 6.779) published a paper by Prof. Guanding Yu's team on the application of machine learning in wireless networks.


Link scheduling in 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 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 the article, they proposed a novel graph embedding based method for link scheduling in D2D networks. They first constructed 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 they computed 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. They also proposed an unsupervised manner to train the graph embedding based method to further reinforce the scalability 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 network layouts. It is also competitive in terms of scalability and generalizability to more complicated scenarios.


The first author of the paper is Mengyuan Li, a third-year doctoral student, and Mr. G Yu is the corresponding author of the paper. The collaborator includes Professor Li Ye from Imperial College London. The paper was supported by a cooperative project of Huawei Technologies Co., Ltd.

 M. Lee, G. Yu, and G. Y. Li, “Graph Embedding-Based Wireless Link Scheduling With Few Training Samples”, IEEE Trans. Wireless Commun., vol. 20, no. 4, pp. 2282 - 2294, Apr. 2021.

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