# Intelligent Transmission Control Scheme for 5G mmWave Networks Employing Hybrid Beamforming
## 1 Introduction  
The demand for high-capacity, low-latency communication has led to the development of 5G mmWave networks. Despite their potential, mmWave communications suffer from significant path loss, limited coverage, and increased susceptibility to interference. With growing needs for high-capacity digital services like video streaming, cloud storage,and real-time industrial automation, mmWave communication stands as a key driver for future wireless networks [1; 2]. However, the deployment of 5G mmWave networks is constrained by compelling challenges, such as high path loss, critical signal attenuation, and directional communication limitations, which affect transmission reliability and energy efficiency [3; 4]. To overcome these issues, hybrid beamforming (HB) emerges as a potential solution, leveraging the benefits of both analog and digital beamforming to enhance spectral efficiency while minimizing power consumption [5; 6]. With HB, base stations (BS) and user equipment (UE) can optimize transmission strategies to achieve maximum data throughput while reducing power consumption [7]. Further, intelligent reflecting surfaces (IRS) have also been investigated as a substitute for enormous MIMO systems, offering better signal propagation by dynamically changing reflection angles [8].  
Despite these developments, transmission control optimization in 5G mmWave networks is still a pressing research issue. Legacy TCP is challenged by varying mmWave channel conditions, leading to higher latency and lower link utilization [9]. Several approaches have been suggested to improve network reliability, such as deep reinforcement learning for adaptive beamforming [10], congestion-aware transmission schemes [11], and multi-user hybrid beamforming techniques [12]. Nevertheless, current methods do not effectively handle interference, congestion, and real-time adaptability, hence bounding their performance in dynamic network conditions [13; 14; 15].  
An intelligent transmission control scheme for 5G mmWave Networks is presented in this paper to address these problems. In contrast to stacked intelligent metasurface (SIM) techniques [16], which need cascaded reconfigurable layers, our system integrates machine learning, IRS-assisted NLOS connections, and hybrid beamforming (HB) to achieve similar performance with less complicated hardware. This suggested plan incorporates the following:  
* Addresses the limitations of classic TCP in mmWave dynamic scenarios by proposing a new mmS-TCP protocol and low latency congestion control schemes to enhance throughput and minimize latency in 5G mmWave networks.
* The row compression two-stage learning-based accurate multipath process network with received signal strength indicator-based association strategy (RCTS-AMP-RSSI-AS) is proposed to reduce training costs and improve the accuracy of both direct and cascaded channel estimates in IRS-assisted mmWave systems.
* Combines digital and analog hybrid beam training with long short-term memory (DAH-BT-LSTM with Mu-MATD3), which optimizes beamforming, enhances spectrum efficiency and interference management, and deals with multi-usr situations.  
The proposed framework's performance is evaluated using simulation experiments in a 5G mmWave network environment simulated with NS-3.35 and Python 3.9.6. The evaluation metrics include throughput, end-to-end delay, packet loss rate, beamforming accuracy, and SNR. The remainder of this paper is structured as follows: Section 2 presents a literature review of related works. Section 3 details the system model. Section 4 introduces the proposed methodology. Section 5 discusses the experimental setup and performance evaluation, followed by the conclusion and future research directions in Section 6.