Beamforming based algorithm for 5G applications

• Published in: Telecommunication systems 2023, Vol.82 (1), p.161-174
• Main Authors: Abohamra, Yousef Ali, Solymani, R., Shayan, Y., Srar, Jala A.
• Format: Article
• Language: English
• Subjects: 5G mobile communication; Adaptive algorithms; Algorithms; Artificial Intelligence; Beamforming; Business and Management; Cellular communication; Computer Communication Networks; Convergence; Direction finding; Direction of arrival; Frequency reuse; Internet of Things; IT in Business; Network latency; Power consumption; Probability Theory and Stochastic Processes; Radiation; Scheduling; Sidelobe reduction; Sidelobes; Synthesis; Telecommunications systems; Tracking; Video transmission
• ISSN: 1018-4864; 1572-9451
• DOI: 10.1007/s11235-022-00973-4

5G applications such as the Internet of Things, high-resolution video streaming, robotic cars, smart cities, and telehealth care have a universal presence nowadays. These applications mandate higher data rates, large bandwidth, increased capacity, low latency and high throughput. The key element to meet this demand is to apply efficient frequency reuse and scheduling strategies based on adaptive beamforming. Employing efficient frequency reuse and scheduling techniques based on adaptive beamforming will significantly increase the cellular system capacity by eighteen times and substantially reduce the consumption power levels and interference. In cellular networks, the performance of the adaptive beamforming algorithms is severely degraded by the presence of interfering signals. In this paper, we introduce a beamforming-based algorithm for 5G applications named Direction Finding for Beamforming and Synthesizing. This algorithm combines the Direction of Arrival (DOA), adaptive beamforming, and radiation pattern synthesizing. The proposed algorithm uses the DOA technique to feed the adaptive beamforming algorithms with estimations of the desired user direction, desired user signal, and the interfering signals with their directions as initial values. In addition, we use the adaptive beamforming process to supply the radiation pattern synthesizing algorithms with an initial radiation pattern, and the required positions of nulls. At the beamformer output, we evaluate our proposed mechanism in terms of error convergence, tracking capabilities, and the obtained radiation pattern characteristics. At the synthesizer output, we carry out analysis in terms of the convergence speed and the resultant radiation pattern attributes to investigate the efficiency of the proposed algorithm. The simulation results show that our proposed algorithm has significantly fast convergence, reliable tracking capabilities, and radiation patterns with very low Side Lobe Levels.