Title: CNN-based hybrid precoding framework using dual-phase shifter in MIMO systems

Authors: Deepti Sharma; Ramesh Babu Battula

Addresses: Department of Computer Science and Engineering, Manipal University Jaipur, India ' Department of Computer Science and Engineering, Malaviya National Institute of Technology, Jaipur, India

Abstract: Emerging applications such as autonomous driving and intelligent healthcare demand ultra-low latency and precise positioning for efficient beamforming, essential for advanced 5G technologies. Hybrid beamforming (HB) has gained prominence as a technique to balance hardware complexity with transmission rates in 5G networks. However, traditional HB methods are computationally intensive and underutilise channel state information, leading to reduced spectral efficiency and transmission rates. Deep learning has revolutionised wireless communication by providing optimised solutions to complex challenges. This work proposes a convolutional neural network (CNN)-based HB framework to minimise complexity while achieving optimal beamforming with enhanced transmission rates. The framework incorporates a hybrid precoding network, HP-CNN, featuring a dual-phase analog shifter and a rate optimiser. Simulations demonstrate that the proposed framework surpasses conventional algorithms, offering superior performance and efficiency. By leveraging deep learning, this approach addresses key challenges in HB, paving the way for robust and high-performance 5G communication systems.

Keywords: hybrid beamforming; convolutional neural network; CNN; dual-phase shifter; DPS; channel-state information; CSI; multiple-input-multpile-output; MIMO.

DOI: 10.1504/IJAHUC.2025.148426

International Journal of Ad Hoc and Ubiquitous Computing, 2025 Vol.50 No.1, pp.1 - 8

Received: 22 Mar 2024
Accepted: 15 Dec 2024
Published online: 04 Sep 2025 *

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