Deep learning-based user clustering for MIMO-NOMA networks - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2021

Deep learning-based user clustering for MIMO-NOMA networks

Abstract

The user clustering problem in an uplink MIMO Non-Orthogonal Multiple Access (NOMA) scheme is considered here. The receiver is assumed to operate in two sequential stages that employ Linear Minimum Mean Squared Error (LMMSE) receivers. At the first stage, the receiver is designed to recover the transmission from a cluster of selected users/nodes. The contribution of these users is then subtracted from the received signal and the remaining user transmissions are then linearly recovered. The determination of which users should be detected during the first stage is formulated as a deep learning based multiple classification problem. In order to guarantee that the selection is robust to fast fading, the input to the neural network is based on second order channel statistics. Furthermore, the training process is simplified by using a large system approximation of the resulting sum-rates. Simulation results indicate that the proposed deep learning-based solution is able to achieve a significant rate advantage with respect to other lazy approaches, such as fixed or random cluster assignments.
Fichier principal
Vignette du fichier
WCNC2021.pdf (804.62 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-03482521 , version 1 (16-12-2021)

Identifiers

Cite

Antoine Dejonghe, Carles Antón-Haro, Xavier Mestre, Leonardo Cardoso, Claire Goursaud. Deep learning-based user clustering for MIMO-NOMA networks. WCNC 2021 - IEEE Wireless Communications and Networking Conference, Mar 2021, Nanjing, China. pp.1-6, ⟨10.1109/WCNC49053.2021.9417426⟩. ⟨hal-03482521⟩
55 View
198 Download

Altmetric

Share

Gmail Facebook X LinkedIn More