Antenna Weights Optimization Based on Filtered Variation Quantum Computing

Operators deploy large-scale array antennas on 5G base stations and set antenna weights to achieve beams with different angles and widths, enhancing 5G network coverage. Traditional antenna weight optimization is based on the user distribution characteristics of the network. Based on the user measurement reports, minimum drive tests, and engineering parameters, the antenna weight optimization determines the relative positions between hotspot areas with centralized user distribution and base station antennas. Then it calculates the antenna weights in the reverse direction. This method can achieve ideal results in optimizing single-cell antenna weights, but it is unsuitable for simultaneous optimization of multiple cells. Information technology is developing rapidly. 5G communication is evolving towards 6G communication, and the growth of network and business scale brings huge challenges to network optimization. Quantum computing is a new computing model that follows the laws of quantum mechanics. It has a computing speed advantage over classical computing and is expected to become a solution to communication network optimization. This paper presents an antenna weight optimization method based on the filter variational quantum matrix solver algorithm. The method utilizes the powerful solution space representation ability of qubits, and it is helpful to obtain better antenna weight optimization results or accelerate the optimization process.