Innovative Channel Estimation Methods for Massive MIMO Using GAN Architectures

Channel estimation is a critical component of modern wireless communication systems, especially in massive multiple-input multiple-output (MIMO) architectures, where the accuracy of received signal decoding heavily depends on the quality of channel state information. As wireless networks evolve into fifth-generation (5G) and beyond, they face increasingly complex propagation environments with rapid mobility, dense connectivity, and hardware constraints. Accurate and timely channel estimation is therefore essential for maintaining system performance, enabling reliable data transmission, and supporting techniques such as beamforming and interference management. Traditional estimation methods like least squares and minimum mean square error offer baseline performance but are often limited by their computational complexity, sensitivity to noise, and inefficiency in quantised systems—particularly those employing one-bit analogue-to-digital converters. These limitations hinder their applicability in real-time, low-power, and bandwidth-constrained scenarios. To address these challenges, this paper proposes a novel channel estimation framework based on conditional generative adversarial networks. The approach incorporates a U-Net-based generator and a sequential convolutional neural network discriminator to learn complex channel mappings from highly quantised received signals. Unlike existing methods, the proposed architecture dynamically adapts to various noise levels and system configurations, offering improved robustness and generalisation. Comprehensive experiments conducted on realistic indoor massive MIMO datasets demonstrate that the proposed method achieves substantial performance gains. The model improves estimation accuracy from 93% to 95.5% and significantly enhances normalised mean square error, consistently outperforming conventional and deep learning-based techniques across diverse training conditions. These results confirm the effectiveness of the proposed scheme in delivering high-accuracy channel estimation under extreme quantisation conditions, making it suitable for next-generation wireless systems.