Abstract: To meet the requirements of 5G networks for high capacity, low interference, and high spectral efficiency, massive MU-MIMO(Multi-User Multiple-Input Multiple-Output) has become a key base-station-side enabling technology. Among linear precoding schemes, ZF(Zero-Forcing) can effectively suppress multiuser interference. However, it suffers from noise amplification in the low-to-medium SNR(Signal-to-Noise Ratio) regime. RZF(Regularized Zero-Forcing) introduces a regularization term to balance interference suppression and noise mitigation, while system performance is highly sensitive to the choice of the regularization coefficient, and a fixed coefficient is difficult to adapt to varying SNR conditions and user loads. To address these issues, an A-RZF(Adaptive RZF) precoding method is proposed, where the regularization coefficient is set adaptively according to the number of users and the noise level, thereby improving spectral efficiency and cell-edge user rates without significantly increasing computational complexity. Based on a downlink MU-MIMO system model, reproducible simulation comparisons against baseline schemes including MRT(Maximum Ratio Transmission), ZF, and fixed-coefficient RZF are established, and performance evaluation in terms of sum spectral efficiency, antenna-scaling gains, cell-edge user rates, and computational complexity is conducted. Simulation results under typical parameter settings show that A-RZF achieves more robust performance advantages over ZF and fixed RZFin the low-to-medium SNR range, while maintaining capacity gains consistent with the growth in the number of antennas.

Key words: 5G net, massive multiple-input multiple-output(MIMO), linear precoding, regularized zero-forcing (RZF), spectral efficiency, artificial intelligence(AI)