# AI-Driven Pilot Overhead Reduction in 5G mmWaveMassive MIMO Systems
## 3 Results
### Random Forest Regression NNSVD for Pilot Overhead Reduction
The comparison in Figure 18 among the four techniques [7; 8; 9] and RFR-NNSVD clearly shows the superior performance of the RFR-NNSVD method in terms of pilot overhead reduction, BER improvement, and SE. Regarding pilot overhead reduction, RFR-NNSVD achieves the highest reduction of 82.14%, slightly exceeding the 80% reduction of [9] while significantly outperforming ref. [7] at 17% and ref. [8] at 43.24%. In terms of SE improvement, RFR-NNSVD achieves an impressive 250% increase, far outpacing ref. [9] at 85%, ref. [8] at 43%, and ref. [7] at 14%. Moreover, RFR-NNSVD is the only method that demonstrates measurable BER improvement, reducing BER to \(5\times 10^{-4}\) and showing an 82% reduction in BER from the original system, while the other techniques [7; 8; 9] did not  
Figure 16: Random Forest Regression NNSVD SE vs. SNR. Figure 17: Comparison of Pilot Overhead Size, BER, and SE across System. take into consideration the BER study. This superiority is attributed to the integration of a neural network with SVD in RFR-NNSVD. The neural network effectively models the complex non-linear relationships of the hybrid beamforming matrix, while SVD ensures efficient matrix factorization, enhancing channel estimation and system performance. These attributes, combined with the robust regression capabilities of the RFR, leverage both supervised learning capabilities and optimized matrix factorization to enhance channel estimation, achieving significant reductions in pilot overhead and BER while maximizing spectral efficiency, making it the most effective technique in this comparison. The results in Table 2 align with the findings illustrated in Figure 18, emphasizing the exceptional performance of the proposed RFR-NNSVD system. While the graph highlights the comparative improvements for systems that studied pilot overhead reduction as a main parameter, the table provides a broader perspective by incorporating additional references, showcasing that RFR-NNSVD outperforms all other techniques across the three key performance metrics, which are the percentages of PO reduction, BER reduction, and SE improvement. Notably, the table reiterates that RFR-NNSVD achieves the best pilot overhead reduction, marginally surpassing [9] and significantly outperforming the reductions reported in [7; 8]. In terms of SE, the proposed system delivers improvements that are much greater than the referenced methods, reflecting its superior optimization capabilities. Furthermore, the inclusion of BER reduction, a metric neglected by the three prior studies that studied PO reduction, underscores the comprehensiveness of the RFR-NNSVD approach. Achieving an 82% BER reduction, RFR-NNSVD not only advances channel estimation accuracy but also addresses a critical aspect overlooked in the state-of-the-art techniques and overcomes the BER reduction in [10; 11; 12] by a wide margin. These results validate the significant enhancements introduced by integrating a neural network with SVD and leveraging RFR. This combination enables RFR-NNSVD to effectively model complex beamforming relationships, optimize matrix factorization, and achieve improvement in system performance, surpassing the limitations of previously proposed methods. Figure 18: RFR-NNVSD comparison with previous works [7; 8; 9].