New Paper Accepted

Our paper User Clustering for MIMO NOMA via Classifier Chains and Gradient-Boosting Decision Trees, joint work with Carles Anton-Haro, Xavier Mestre, and Mohamed-Slim Alouini, has been accepted in IEEE Access.


In this paper, we propose a data-driven approach to group users in a Non-Orthogonal Multiple Access (NOMA) MIMO setting. Specifically, we formulate user clustering as a multi-label classification problem and solve it by coupling a Classifier Chain (CC) with a Gradient Boosting Decision Tree (GBDT), namely, the LightGBM algorithm. The performance of the proposed CC-LightGBM scheme is assessed via numerical simulations. For benchmarking, we consider two classical adaptation learning schemes, Multi-Label k-Nearest Neighbours (ML-KNN) and Multi-Label Twin Support Vector Machines (ML-TSVM); as well as other naive approaches. Besides, we also compare the computational complexity of the proposed scheme with those of the aforementioned benchmarks.

Chaouki ben Issaid
Chaouki ben Issaid
Postdoctoral Fellow Researcher