# AI-Driven Pilot Overhead Reduction in 5G mmWaveMassive MIMO Systems
## 1 Introduction
The implementation of ISAC can lead to a reduction of up to 43.24% in overhead, improving communication throughput and beam tracking performance. In [9], the authors proposed a novel technique for pilot contamination analysis in 5G MIMO systems, utilizing multi-user pilot scheduling and convolutional adversarial training to enhance spectral efficiency and reduce pilot overhead, achieving significant improvements in SINR and detection accuracy. The results achieved an SINR of 72% and spectral efficiency of 85%, while detection accuracy reached 95% with a normalized MSE of 73%. The authors of [10] developed a deep learning-based hybrid beamforming technique that improved SE by 20% compared to traditional methods, with a corresponding BER reduction of 10%. Additionally, in [11], the authors proposed a data-driven approach that enhanced SE by 25% and reduced BER by 15% in high-mobility scenarios. The work discussed in [12] introduced a machine learning framework for hybrid beamforming that achieved a 35% increase in SE and a 20% reduction in BER, demonstrating the efficacy of AI-driven approaches.Furthermore, the authors of [13] presented a deep neural network-based hybrid beamforming method that outperformed existing techniques by 2 dB in terms of BER performance. Despite significant advancements, many studies have focused on isolated AI methods or relied on traditional optimization frameworks, limiting their scalability and generalizability. Our work bridges these gaps by integrating multiple AI techniques (k-clustering, linear regression, random forest regression, and NN-SVD) into a unified framework tailored for hybrid beamforming. Furthermore, we validated our approach using scalable configurations (up to 64 Tx/16 Rx antennas) and multiple modulation schemes, providing practical insights into its applicability. This paper investigates the application of various artificial intelligence (AI) techniques for pilot overhead reduction in millimeter-wave (mmWave) massive multipleinput and multipleoutput (m-MIMO) systems employing hybrid beamforming. The primary goal of this research was to address the challenge of pilot overhead in hybrid beamforming for 5G mmWave m-MIMO systems. By leveraging advanced artificial intelligence (AI) techniques, this paper proposes and evaluates methods that significantly reduce pilot overhead while maintaining or improving key system performance metrics, including spectral efficiency (SE) and bit error rate (BER). Specifically, the paper investigates the application of k-clustering, linear regression, and random forest regression and finally combines random forest regression with the integration of neural networks and singular value decomposition (SVD) to optimize pilot placement and hybrid beamforming strategies. This research aimed to bridge the gap between traditional pilot management techniques and emerging AI-driven solutions, contributing to the advancement of next-generation wireless communication systems. This paper contributes to the state of the art by introducing a comprehensive AI-driven framework that combines traditional and advanced machine learning (ML) methods specifically tailored for reducing pilot overhead in 5G mmWave m-MIMO systems. Unlike existing approaches, which often focus on single techniques or limited scenarios, our work contributes to the field in the following ways:  
* Develops an ensemble framework integrating multiple ML methods, leveraging their complementary strengths for optimal pilot placement and channel estimation;
* Proposes a hybrid beamforming system that combines NN-predicted SVD for adaptive optimization of beamforming matrices, reducing complexity and enhancing spectral efficiency;
* Extends the evaluation to include realistic channel conditions, advanced modulation schemes, and scalable antenna configurations, providing a more holistic and practical analysis;
* Demonstrates a significant reduction in pilot overhead (up to 82%) while maintaining or improving BER and SE compared to traditional and existing AI-based methods.