Improving Performance and Scalability in Cell-Free Massive MIMO Networks with Gradient Descent Algorithms

Scalable cell-free massive MIMO (CF-mMIMO) systems are essential for addressing the increasing capacity and coverage demands of modern wireless networks. Despite their potential, existing CF-mMIMO architectures encounter scalability limitations, including increased latency and bandwidth requirements as network sizes expand. To address these challenges, this study proposes a novel and efficient partial minimum mean square error (PMMSE) precoding technique, optimized using a gradient descent (GD) algorithm. The proposed gradient descent-based PMMSE (GD-PMMSE) method significantly enhances the performance and scalability of CF-mMIMO systems. Our simulation results demonstrate that GD-PMMSE achieves notable improvements in spectral efficiency and signal-to-interference-plus-noise ratio (SINR) compared to conventional techniques such as PMMSE, local partial minimum mean square error (LPMMSE), and partial regularized zero-forcing (PRZF). These improvements are particularly pronounced in densely populated scenarios with high access point (AP) and user equipment (UE) densities, showcasing its superior adaptability to dynamic user demands while maintaining high spectral efficiency and reduced bit error rates (BER). Furthermore, the integration of dynamic cluster optimization strengthens CF-mMIMO system designs, enabling robust and efficient operation under varying conditions without performance degradation. This work provides a promising framework for the advancement of next-generation wireless communication systems.