Optimizing Millimeter-Wave Beamforming in 5G and Beyond with Neural Networks

One of the challenges in millimeter-wave beamforming for mobile communication is its adaptation to dynamic radio propagation. Real-time modifications based on instantaneous stations State Information (CSI) are necessary for efficient beam control. But things are made more difficult by the ascending system above with its growing number of antennae. This paper presents a new approach: a neural network-enhanced millimeter-wave beamforming technique. The “beam readjustment interval,” a crucial parameter, measures how effective beamforming is by indicating the longest period of time that the Signal-to-Interference-Plus-Noise Rate (SINR) of user equipment may remain above a preset threshold. By using a predictive ray adaptation approach, the system considers SINR at both the present location and probable future placements. The beam modulation interval may be greatly extended with this strategy, as shown by the simulation results. As a result of fewer beam modifications for mobile user devices, there is a significant reduction in total system overhead, with an average drop of 41.4% over 10 randomly generated test traces. To sum up, our novel millimeter-wave beamforming method, enhanced by neural networks, effectively tackles the growing difficulties linked to system overrun in beam management. The results of the simulations highlight the algorithm's higher performance, especially when the beam's adjustments period and predictive techniques were included.