# AI-Driven Pilot Overhead Reduction in 5G mmWaveMassive MIMO Systems
## 1 Introduction  
One of the major technologies of the next generation of wireless communication, leveraging millimeter-wave (mmWave) frequencies that enable high-speed, low-latency connectivity is the 5G/6G m-MIMO (massive multiple input multiple output) system. A large number of antennas at both the transmitter and receiver are utilized in m-MIMO systems to significantly enhance the spectral efficiency, energy efficiency, data rate, and reliability of wireless communication systems [1]. These systems are particularly effective in the mmWave frequency band, where the high free-space path loss necessitates large array gains to achieve sufficient signal-to-noise ratio (SNR) even at moderate distances, and this is where beamforming emerges as an essential block to achieve this technology. Beamforming is crucial in new-generation networks for targeting specific users/devices with focused beams to enhance signal quality. Digital beamforming offers advantages like multiple closely spaced beams, super resolution, and pattern correction. Convertingsignals into digital data streams in the DBF approach leads to composite signals, but it is costly and power-consuming in multi-user m-MIMO systems [2]. Analog beamforming, however, addresses cost and power issues by phase-shifting signals using analog methods, resulting in a single digitized output. Despite its benefits, analog beamforming does not fully leverage the advantages of DBF due to signal imperfections and noise introduced during amplification. On the other hand, hybrid beamforming is a promising solution to these challenges. It leverages both analog and digital processing capabilities, optimizing the performance of m-MIMO systems without compromising on the benefits they offer [3]. The beamforming task involves converting channel state information (CSI) into a beam pattern, utilizing pilot bits. Pilot signals are special reference signals transmitted by the base station to the user equipment (UE) in wireless communication systems. These signals are crucial for several reasons, including channel estimation, synchronization, and beamforming. In the context of hybrid beamforming, pilot signals play a pivotal role in facilitating the estimation of the CSI between the transmitting and receiving antennas. The CSI is essential for determining the optimal beamforming weights that maximize the signal-to-interference-plus-noise ratio (SINR) and, consequently, the data rate and reliability of the communication link. One of the major strategies used to determine where and how often pilot signals should be transmitted is the pilot placement technique [4]. Although the use of pilot signals is crucial in such systems, it imposes additional overhead in terms of bandwidth and energy consumption, leading to reduced spectral efficiency (SE) and increased energy consumption [5]. Therefore, minimizing the pilot overhead while maintaining sufficient accuracy in channel estimation is an important challenge in communication system design. Deep learning has revolutionized hybrid beamforming by enabling AI-driven optimization [6].Unlike traditional approaches, DNNs can learn from available data sources to minimize pilot overhead and maximize spectral efficiency, and this was our focus in this study. This innovative approach promises to significantly enhance the performance of wireless communication systems. Both unsupervised and supervised approaches can be used to achieve pilot overhead decrease while enhancing BER and SE. Few works have been reported on this topic. A user clustering-based pilot allocation scheme demonstrated a 17% reduction in required pilot overhead while enhancing spectral efficiency by 8-14% through optimization techniques like integer linear programming and heuristics [7]. The authors in [8] used integrated sensing and communications (ISAC) to reduce overhead in high-mobility V2X networks by optimizing channel state information (CSI) acquisition.