AI Technology in Optimizing 5G MIMO Antenna Networks
5G MIMO is a technology that uses multiple antenna arrays to achieve optimal network performance through beamforming and beam management.
Currently, the main deployment scenarios and requirements for Massive MIMO include: dense urban areas with local hotspots, challenges of high traffic; high-rise coverage, three-dimensional beamforming for vertical coverage; large venues and gatherings, challenges of high capacity and high-density crowds.
In the process of optimizing 5G MIMO antenna networks, the large number of parameters for the beamforming of Massive MIMO broadcast beams, such as the number of beams, beam layers, horizontal and vertical beam widths, azimuth, and electrical tilt, combined with the significant differences in configuration among the main system equipment vendors, have increased the difficulty and complexity of frontline operations and maintenance personnel.
Therefore, AI technology needs to be introduced to quickly identify and self-learn scenarios, adaptively adjust broadcast weights, achieve optimal coverage and performance gains, and realize fast intelligent network planning and optimization, reducing time and manpower. Additionally, for energy-saving control, based on the traffic tide phenomenon in hybrid coverage scenarios, power-saving requirements for base stations based on changes in service demand can be proposed. Furthermore, based on user location and distribution, adaptive beamforming can provide a better user experience.
Regarding the energy consumption problem of Massive MIMO, the power consumption of 5G AAUs has increased significantly. If the LTE uses 30W per antenna, theoretically, the annual power consumption of each base station for 4G is over 3000 kWh. However, with the AAU using 200W, the theoretical annual power consumption of 5G for each base station is over 20,000 kWh. Currently, energy-saving solutions mainly include innovative materials for power amplifiers (PAs), improving integration and process for chips, introducing new materials and structural designs for heat dissipation, and implementing AI energy-saving algorithms on the system side to intelligently shut down.
In addition, the increase in the number of 5G MIMO antenna provides more multiplexing gains and diversity gains for the propagation channel, resulting in better performance for downlink data rates, link reliability, and coverage. However, the terminal transmit power limits the uplink coverage of 5G, so carrier aggregation and uplink enhancement technologies are needed to achieve 5G high-low frequency coordinated networking.
It is also the evolving trend of 4G 5G collaborative development, 5G multi-frequency networking, and intelligent simplification of the antenna system architecture that 5G MIMO antenna should move towards miniaturization, multi-frequency, integration, and intelligence. Currently, two types of antennas have been mainly deployed in the existing network: one is the 3.5GHz AAU with one pair of antennas + other low-frequency antennas; the other is the A+P integrated antenna. This also poses challenges for 5G antenna manufacturers in terms of conflicting antenna specifications and indicators.
At the same time, 5G antenna design also faces the demand for scenario-based requirements. For large venues, antennas need to achieve accurate coverage and rapid expansion. For high-speed rail scenarios, the current coverage solution is mainly along the line wide coverage + on-board mode, and antennas need to meet multi-frequency, high-order 8TR, and intelligent management.TAG：AI Technology in Optimizing 5G MIMO Antenna Networks https://www.rfelement.com