Artificial-Intelligence-Enhanced Beamforming for Power- Efficient User Targeting in 5G Networks

Abstract

In the quest for optimizing 5G networks, this study was performed to introduce an innovative Artificial Intelligence (A.I.)- based beamforming technique focused on power efficiency and signal integrity. By leveraging a machine learning algorithm, the base station (BS) conducts an omnidirectional scan to identify and direct beams towards the user equipment (UE) exhibiting the lowest possible power signature for optimizing the overall network's performance. Extensive simulations conducted using a Uniform Linear Array (ULA) at 28 GHz with Quadrature Amplitude Modulation (QAM) to authenticate the process, A.I. algorithm dynamically adjusted the beamforming weights, which were then applied to synthetic user signals to simulate real-world conditions. The results that were validated through Bit Error Rate (BER), Throughput, Angle of Arrival (AOA), Direction of Arrival (DOA), and Array Response (AR) metrics has shown that the A.I.-driven approach does not only reduces power consumption but also maintains user’s signal fidelity with high precision. A.I.'s decision-making process was exactly analyzed showing its capability to fine-tune beam direction in the presence of noise and interference. The study concluded that A.I.-based steering in the direction of the least power-intensive user is not only capable of functioning adequately but also enhances and improves the overall network efficiency and reliability.