Link activation using variational graph autoencoders

Jamshidiha, Saeed; Pourahmadi, Vahid; Mohammadi, Abbas; Bennis, Mehdi (2021-04-28)
 
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https://doi.org/10.1109/LCOMM.2021.3076190

Jamshidiha, Saeed
Pourahmadi, Vahid
Mohammadi, Abbas
Bennis, Mehdi
Institute of Electrical and Electronics Engineers
28.04.2021

S. Jamshidiha, V. Pourahmadi, A. Mohammadi and M. Bennis, "Link Activation Using Variational Graph Autoencoders," in IEEE Communications Letters, vol. 25, no. 7, pp. 2358-2361, July 2021, doi: 10.1109/LCOMM.2021.3076190

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doi:https://doi.org/10.1109/LCOMM.2021.3076190
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Abstract

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.

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