This paper presents a comprehensive performance evaluation of signal detection algorithms for reconfigurable intelligent surface (RIS)-assisted cell-free massive MIMO systems (CF-mMIMO) in 5G/6G networks. We investigate six detection schemes: zero-forcing (ZF), minimum mean square error (MMSE), conjugate gradient (CG), Neumann-series MMSE, orthogonal approximate message passing (OAMP), and a novel hybrid successive over-relaxation MMSE (SOR-MMSE) detector. The proposed algorithm is specifically designed for diverse system loading conditions, employing a hybrid approach that adaptively switches between SOR iterations for overdetermined systems and residual-based updates for underdetermined systems. It further incorporates adaptive constellation projection and reliability-based soft decisions, optimized for RIS-enhanced environments. We provide mathematical derivations, convergence analysis with eigenvalue-based bounds, and relaxation parameter optimization. Through extensive Monte Carlo simulations encompassing balanced, overloaded, and underloaded antenna-to-user configurations with a 64-element RIS, we demonstrate significant performance improvements. The hybrid SOR-MMSE detector achieves remarkable gains, particularly in underloaded RIS-assisted systems where it provides approximately 2-3 dB signal-to-noise ratio (SNR) improvement compared to ZF detection for an equivalent bit error rate (BER). For balanced configurations, it maintains a 1–2 dB advantage in high-SNR regimes compared to the standard MMSE detector with RIS assistance. The benefits of RIS assistance are evident across all scenarios. For example, an underloaded system reaches a capacity of nearly 19 bps/Hz at 20 dB SNR, which is a significant increase from the 15 bps/Hz achieved without RIS.
Khurshid et al. (Thu,) studied this question.