Neural Network-Based Beamforming Techniques for Optimized Coverage in Cellular Networks

Beamforming is now widely incorporated as a key tool for coverage, capacity and energy facilities for cellular networks especially with the ongoing 5G and beyond. This research focuses on extending the use of neural networks into beam forming techniques for providing optimal coverage in various and dynamic cellular networks. Conventional beamforming techniques use predetermined algorithms and fixed models which are not efficient in meeting the dynamic user traffic and variability in the wireless network. Various machine learning techniques pose limitations in their characterization of manifold structures and the generalization of the learnt patterns exhibit high computational complexity. In this work, to further improve the system performance, the two neural network-based beamforming schemes are proposed and tested as follows. The proposed approach consists of using a supervised learning combined with a reinforcement learning technique to improve the quality of the signal and reduce interferences as well as increasing the spectral efficiency. The simulation results prove the enhanced coverage reliability and throughput for dense urban user distribution cases. In this case, the study also assesses the scalability and computational complexity propensity of the study models in large-scale networks. The conclusion highlights the neural network's applicability for diversifying beamforming approaches and the approaches for improving cellular networks that comprise the basis for modern Wi-Fi and mobile communication.