Implementation and Evaluation of a Conjugate Gradient (CG) Detector for 5G/6G-Like MIMO Scenarios Using Sionna

Massive multiple-input multiple-output (MIMO) systems are a cornerstone of 5G and emerging 6G wireless networks due to their ability to provide high spectral efficiency and improved reliability. However, signal detection in large-scale MIMO systems remains a major challenge because of the high computational complexity associated with conventional linear detectors. In this paper, we investigate the Conjugate Gradient (CG) algorithm as a low-complexity iterative detection technique for massive MIMO systems. The MIMO detection problem is formulated as a system of linear equations and solved using the CG method implemented within the Sionna simulation framework. The convergence behavior and bit error rate (BER) performance of the proposed detector are analyzed under different signal-to-noise ratio (SNR) levels and spatial correlation scenarios. Simulation results show that the CG-based detector achieves near-optimal BER performance while significantly reducing computational complexity compared to classical linear detectors such as the linear minimum mean square error (LMMSE) detector. These results demonstrate that CG-based detection is a promising and efficient solution for practical large-scale MIMO deployments.