Comparative Analysis of Deep Learning and Traditional Optimization Algorithms for Adaptive Beamforming in MIMO Systems

Adaptive beamforming is a critical technology for multiple-input multiple-output communication systems, enabling spatial filtering that improves signal quality and suppresses interference. Traditional optimization-based beamforming algorithms, such as minimum variance distortionless response and linearly constrained minimum variance, rely on precise channel state information and face computational challenges in rapidly varying channel conditions. This paper presents a comprehensive comparative analysis of deep learning approaches and traditional optimization algorithms for adaptive beamforming in MIMO systems. We evaluate convolutional neural network-based, recurrent neural network-based, and transformer-based beamforming architectures against traditional optimization methods across multiple channel models and operating conditions. Experimental results demonstrate that deep learning approaches achieve 15-23% better beamforming performance in rapidly varying channels with imperfect channel state information, while traditional algorithms maintain advantages in stationary channel conditions with accurate channel estimates. We propose a hybrid architecture that dynamically selects between deep learning and traditional approaches based on channel conditions.