Beam Downtilt Optimization Of Active Antenna

With the rapid development of the information technology, people’s demand for network capacity is growing at an explosive speed. How to satisfy people’s demand for capacity and make more efficient use of the wireless resources is the key problem in the wireless industry. Given the current state of wireless technology development, there exists a very significant potential in utilizing the spatial domain to improve radio resource efficiency. AAS(Active Antenna System) can reinforce the transmission of radio signals in the spatial domain by controlling the horizontal and vertical domains of the array`s beam pattern. This allows the AAS to make gains offered by freedom in the vertical domain, something not available in legacy BSs. AAS BS is able to form multiple cells in the vertical domain by establishing multiple static beamforming vectors in a vertical array. This type of cell-splitting doubles the available radio resources by separating user equipments(UE) in the vertical domain and therefore improves system capacity. Thus AAS has been the hotspot in researching area. However, cell-spliting aggravates the interference between the users at the same frequency and how to adjust the downtilt angle of the beam to restrain the inter-cell interference is still a key problem to solve.First, this paper established the three-dimensional array antenna model in 3GPP standard on AAS and proposed a beamforming algorithm forming multiple beams used in the cell-spliting scenario based on signal superposition principle. After this, frequency could be reused in the same cell and cell-spliting was realized in LTE cells. An algorithm based on PSO(Particle Swarm Optimization) was proposed to adjust the beams` downtilt angles. Simulation results showed that an outstanding network performance gain is achieved with low complexity by the proposed algorithm.Then, a pricing-based algorithm was proposed to adjust the downtilts of the vertical beams jointly in multiple cells for inter-cell interference supression. In the algorithm, a pricing mechanism was defined to measure the decrease in utility due to an increase in interference. The proposed algorithm decomposes the problem of total capacity maximization into the problem of utility maximization in each cell. Afterwards, we solved the local utility maximization through PSO for each cell. After all, Simulation results showed that the proposed algorithm converges well and improves the capacity significantly. As well, the inter-cell interference was restrained efficiently.