Data detection techniques for scalable cell-free massive MIMO systems

Cell-free (CF) massive multiple-input multiple-output (mMIMO) is emerging as a key technology for sixth-generation (6G) communication systems, offering nearly uniform service for users across various areas while effectively managing interference compared to traditional mMIMO systems. However, data detection in CF-mMIMO environments requires sophisticated signal processing techniques. While both linear and nonlinear detectors have demonstrated strong performance, the exploration of iterative detection methods in CF-mMIMO has been limited. This paper addresses this research gap by examining the performance of five efficient iterative scalable CF-mMIMO detectors based on approximate/avoid matrix inversion techniques: Newton iteration, Gauss-Seidel, Jacobi, accelerated over-relaxation, and successive over-relaxation. Additionally, we propose an efficient detector based on sphere decoding (CF-SD) for scalable CF-mMIMO systems. Simulation results indicate that the linear iterative methods can achieve performance that approximates that of the minimum mean square error detector, while also maintaining a lower computational burden. In addition, while the CF-SD detector demonstrates considerable performance enhancements, it requires higher computational complexity compared to its linear iterative counterparts.