Machine learning-enabled joint antenna selection and precoding

Monica Nilesh Kalbande (Yeshwantrao Chavan College of Engineering)
Kanala Sai Madhuri (B. V. Raju Institute of Technology)
M. Venkateswara Rao (Vignana Bharathi Institute of Technology)
Saradha Rani Sabbavarapu (GITAM (Deemed to be University))
Rajyalakshmi Uppada (Aditya University)
Lakshmi Durga Rajamahendravarapu (Koneru Lakshmaiah Education Foundation)

IAES International Journal of Artificial Intelligence (IJ-AI)
Vol 15, No 3: June 2026

Joint antenna selection (AS) and precoding design is essential for improving spectral efficiency and energy efficiency in multi-antenna wireless communication systems. However, conventional optimization-based solutions rely on exhaustive search and iterative processing, leading to high computational complexity that limits real-time applicability. This work proposes a machine learning-enabled framework that shifts the computational burden from online operation to offline training. Optimal AS and precoding decisions are first generated offline using model-based optimization under diverse channel conditions. A supervised machine learning model is then trained to learn the relationship between channel state information (CSI) and optimal transmission configurations. During online operation, the trained model enables fast and efficient AS with significantly reduced processing time. Numerical results demonstrate that the proposed approach achieves near-optimal system performance while substantially lowering computational complexity, making it well suited for real-time and next-generation wireless communication systems.