# AI-Driven Pilot Overhead Reduction in 5G mmWaveMassive MIMO Systems
## 3 Results
### Random Forest Regression NNSVD for Pilot Overhead Reduction  
This section presents the performance of the proposed random forest regression NNSVD (RFR-NNSVD) system, which combines SVD with a random forest regression neural network (NN) for optimal beamforming vector prediction. The proposed random forest regression-neural network-singular value decomposition (RFR-NNSVD) framework synergizes SVD-based hybrid beamforming with machine learning to enhance the performance of mmWave communication systems. SVD serves as a foundational step, enabling the decomposition of the channel matrix into analog and digital beamforming components that are mathematically aligned with the dominant eigenmodes of the channel. This ensures optimal hybrid beamforming, simplifying learning complexity, enhancing adaptation to channel conditions, and maximizing channel capacity and spectral efficiency while minimizing pilot overhead. A multi-layer perceptron (MLP regressor) is trained to predict the hybrid beamforming matrix by learning the mapping between phase information (input) and absolute values (target) derived from the SVD process, ensuring that the transmitted signals are optimally aligned with the channel conditions. Furthermore, random forest regressors are developed to model the relationship between system parameters, such as the number of antennas, data streams, pilot symbols, signal length, channel capacity, SNR, and key performance metrics, including pilot overhead, bit error rate, and spectral efficiency. By combining SVD's mathematical precision for beamforming optimization with the predictive capabilities of machine learning, the proposed framework provides a robust mechanism to evaluate and enhance system performance under diverse SNR conditions. The results highlight the framework's ability to achieve low pilot overhead, high spectral efficiency, and reliable signal reconstruction. Figure 14 depicts that with RFR-NNSVD the system achieves a pilot overhead size close to the system without NN implementation (approximately 62.5 at low SNR and 62.38 at high SNR). On the other hand, the BER and SR are enhanced. Figure 15 illustrates the significant BER reduction achieved by the RFR-NNSVD method compared to the original system and other ML-based approaches. At an SNR of -10 dB, the proposed method achieves a BER of \(1.05\times 10^{-4}\) bits/symbol, about a tenfold improvement. Furthermore, the BER continues to decrease to \(1.1\times 10^{-5}\) bits/symbol at 30 dB SNR. These results demonstrate the superior performance and robustness of the RFR-NNSVD system across a wide range of SNR conditions. Figure 16 demonstrates the improved spectral efficiency (SE) of RFR-NNSVD. The system achieves a maximum  
Figure 13: Random Forest Regression SE vs. SNR. SE of 40 bps/Hz, representing a 25% increase compared to prior ML methods. This improvement is attributed to the system's superior channel estimation capabilities and optimized beamforming vector adjustments facilitated by the RFR-NN architecture. The simulation results throughout all configurations of this study show that the use of machine learning AI-driven unsupervised and supervised methods can yield a significant improvement in the pilot overhead reduction while maintaining the BER and SE of the system in some cases and even improving them in other cases, such as the NNSVD random forest regression. Figure 17 presents a comparative analysis of the proposed systems in terms of pilot overhead, BER, and SE. The RFR-NNSVD system demonstrates superior performance across all metrics. It achieves the lowest pilot overhead of 62, a significant reduction from the original system's 350. Additionally, RFR-NNSVD significantly outperforms other methods in terms of BER reduction, achieving a 250-fold improvement. While all systems  
Figure 14: Random Forest Regression NNSVD pilot overhead vs. SNR. Figure 15: Random Forest Regression NNSVD BER vs. SNR. exhibit improved SE, RFR-NNSVD further enhances SE to 40 \(\rm{bps}/\rm{Hz}\), solidifying its position as the most effective system.