# Collaborative Beamforming with DQN for Interference Mitigation in 5G and Beyond Networks
## 2 Related Work  
Beamforming is a signal processing technique that can be optimized using machine learning approaches [8]. By leveraging neural networks, reinforcement learning, and other algorithms, machine learning enables beamforming systems to intelligently adapt beams based on dynamic environments. Key applications of machine learning include beam pattern prediction, adaptive beam steering, direction of arrival estimation, and beam selection. With machine learning, beamforming systems can achieve more flexible, intelligent, and data-driven optimization of antenna arrays beyond traditional approaches. Several recent studies have explored hybrid beamforming/combining for millimeter wave (mmWave) multiple-input multiple-output systems to achieve a balance between performance and cost [9]. Table 1 summarizes the related work. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Topic** & **Work** & **Contribution** & **Pros** & **Cons** \\ \hline \multirow{6}{*}{Machine Learning for Beamforming Optimization} & \multirow{6}{*}{[8; 9; 10; 11]} & Machine learning techniques enhance beamforming in mmWave & Flexible and adaptive optimization in dynamic environments [8]; reduced complexity in real-time processing [9]. & High computational cost and complexity in training large datasets [10]; limited generalizability across varied configurations [11]. \\ \hline \multirow{6}{*}{Hybrid Beamforming Systems} & \multirow{6}{*}{[10; 12]} & Development of neural hybrid beamforming approaches using autoencoders for flexible architecture compatibility and fast convergence. & Improved flexibility in beamforming/combining architectures [10]; fast training convergence [12]. & Dependence on specific architecture configurations limits general application [10]. \\ \hline \multirow{6}{*}{Reinforcement Learning-Based Beamforming} & \multirow{6}{*}{[9; 13; 14; 15]} & Deep reinforcement learning (DRL) applications in beamforming for dynamic adaptation in MIMO and V2X communications. & DRL enables high adaptability in changing environments [9]; enhanced performance in vehicular communications [15]. & Training is resource-intensive, requiring high computational power [13]. \\ \hline \multirow{6}{*}{Beam Scheduling and Localization} & \multirow{6}{*}{[16; 17]} & Adaptive beam scheduling and localization techniques in distributed MIMO systems using machine learning for optimized performance. & Effective in large-scale MIMO and subleased RF slices [16]; improved accuracy in localization under diverse channel conditions [17]. \\ \hline \multirow{6}{*}{Physical Layer Security} & \multirow{6}{*}{[18; 19]} & Combination of beamforming and IRS for beamforming and IRS for enhanced security in IoT networks and cooperative jamming networks. & Enhanced security against eavesdropping and jamming [18]; efficient energy utilization in IRS-assisted networks [19]. & Complexity in coordinating IRS phase shifts and beamforming in real time [19]. \\ \hline \multirow{6}{*}{Collaborative and Distributed Beamforming} & \multirow{6}{*}{[20; 21; 22]} & Collaborative strategies for MIMO beamforming and dynamic, distributed algorithms for coordinated beamforming. & Distributed approach reduces the need for centralized CSI [21]; effective in multiuser and large MIMO scenarios [20]. \\ \hline \multirow{6}{*}{Energy Efficiency and UAV Beamforming} & \multirow{6}{*}{[6; 14]} & Optimization of energy consumption in UAV collaborative beamforming and sidelobe reduction in sensor networks. & Reduced energy consumption in UAV beamforming [14]; effective sidelobe management [6]. & Limited scalability in multi-UAV scenarios; high power requirements for large sensor networks [6]. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Related work summary. Authors in [10] addressed this gap by proposing a neural hybrid beamforming/combining (NHB) system utilizing autoencoders.