User Clustering for Rate Splitting using Machine Learning
- URL: http://arxiv.org/abs/2205.11373v1
- Date: Mon, 23 May 2022 15:05:16 GMT
- Title: User Clustering for Rate Splitting using Machine Learning
- Authors: Roberto Pereira, Anay Ajit Deshpande, Cristian J. Vaca-Rubio, Xavier
Mestre, Andrea Zanella, David Gregoratti, Elisabeth de Carvalho, Petar
Popovski
- Abstract summary: A scalable and much lighter clustering mechanism based on Neural Network (NN) is proposed.
The accuracy and performance metrics show that the NN is able to learn and cluster the users based on the noisy channel response.
- Score: 37.734460275850076
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Hierarchical Rate Splitting (HRS) schemes proposed in recent years have shown
to provide significant improvements in exploiting spatial diversity in wireless
networks and provide high throughput for all users while minimising
interference among them. Hence, one of the major challenges for such HRS
schemes is the necessity to know the optimal clustering of these users based
only on their Channel State Information (CSI). This clustering problem is known
to be NP hard and, to deal with the unmanageable complexity of finding an
optimal solution, in this work a scalable and much lighter clustering mechanism
based on Neural Network (NN) is proposed. The accuracy and performance metrics
show that the NN is able to learn and cluster the users based on the noisy
channel response and is able to achieve a rate comparable to other more complex
clustering schemes from the literature.
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