A Binary Relevance Approach for Smart Antenna Selection in Massive MIMO Systems

Future transceivers are projected to incorporate massive antenna arrays, which could significantly increase power consumption. To mitigate this challenge, the antenna selection technique (AS) emerges as a viable solution. By strategically selecting a subset of antennas, the system power consumption can be significantly reduced without compromising the overall system performance. This paper proposes a novel AS approach for massive MIMO systems under real-world channel measurements. By employing the binary relevance technique (BR), a straightforward approach to multi-label (ML) learning that tackles the problem by treating each class label as an independent binary classification task, we formulate the AS problem as a ML classification task. We conducted simulations using SVM as the base learning algorithm to assess the performance of our proposed approach and compare results to the Multi Label convolutional neural network (ML-CNN) and convex relaxation based approaches (CVX). The binary relevance based SVM (SVM-BR) performance, while slightly below the suboptimal convex relaxation approach in terms of system capacity, remains competitive with the MLCNN under different antenna array configurations.