Improved Data Detection Scheme for Massive MIMO Systems

Massive multiple-input multiple-output (MIMO) serves as a cornerstone technology for beyond fifth-generation $(5 \mathrm{G})$ wireless networks, delivering significant improvements in both spectral and energy efficiency. However, one of the key challenges lies in designing signal detection algorithms that achieve high performance while maintaining low computational complexity. Iterative detection techniques have gained significant attention, where they progressively improve the accuracy of detection through a series of successive updates or iterations. In this work, we propose two novel hybrid massive MIMO detectors: the improved Newton method (INI) combined with Gauss-Seidel (GS) and the INI combined with successive overrelaxation (SOR). The motivation behind this work is to combine low-complexity iterative methods with the adaptive and recursive estimation power of INI, resulting in a more efficient and accurate detection process. By first applying a lightweight INI-based initialization and then refining the estimate using efficient iterative updates, the proposed detectors can accelerate convergence with significantly reduced complexity. Simulation results demonstrate superior performance, outperforming existing up-to-date detectors, making them well-suited for practical deployments.