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Link Activation Using Variational Graph Autoencoders
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DOI：10.1109/LCOMM.2021.3076190.png)
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Abstract
En 中文
An unsupervised method is proposed for link activation in wireless networks by identifying clusters of interfering users. A k-nearest neighbors interference graph is first defined for the wireless network which is then mapped to a stochastic latent space. The users are then clustered in the latent space using a Gaussian mixture model, and one user from each interfering cluster is activated while the rest of the users in that cluster remain idle. The proposed framework is scalable, works across several network topologies such as device to device (D2D), and is close to the optimal solution in performance.
Keywords:
Interference
Wireless networks
Transmitters
Device-to-device communication
Stochastic processes
Receivers
Deep learning
Graph embedding
variational graph autoencoder
wireless networks
Bayesian deep learning
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