Research On Interference Suppression Technology Based On Quantum Intelligent Computing In 3D MIMO Systems

As one of the key technologies of 5G mobile broadband communication system,3D MIMO technology has attracted more and more attention from experts and scholars at home and abroad.Interference suppression technology can effectively reduce interference to improve communication quality and is one of the core technologies of 3D MIMO wireless communication systems.Quantum bacterial foraging optimization algorithm integrates the idea of quantum computation and swarm intelligence algorithms to solve the combinatorial non-convex optimization problem that can not be solved by the traditional Lagrangian-based optimization algorithms,and the convergence speed is faster.Based on the improved quantum bacterial foraging optimization algorithm,this paper studies the joint optimization of base station beam downtilts and power considering user fairness and the optimization of network sum rate in different transmission scenarios with massive MIMO pilot contamination.The main research work of this paper is as follows:Firstly,this paper considers multi-cell multi-user 3D MIMO downlink transmission system.Joint transmission is adopted between cells.Based on comprehensive consideration of system spectrum efficiency and user fairness,an effective transmission scheme is designed to suppress interference in the system.In this scheme,cell-center user and cell-edge user specific beam downtilts and power are jointly optimized and each user can reach an acceptable minimum sum rate.The target optimization problem is a combinatorial non-convex optimization problem,which is solved by the IQBFO algorithm.The simulation results show that the scheme has higher system throughput when considering user fairness(that is,all users can transmit at an acceptable rate),and compared with other swarm intelligence algorithms such as QBFO,QPSO,BFO,IQBFO converges faster in solving this objective optimization problem.Secondly,in multi-cell and multi-user 3D large-scale MIMO downlink networks,when the number of antennas of base stations is large,the SINR of users largely depends on the location of users and large-scale fading coefficients.The performance of the network is affected by inter-user interference within each cell and inter-cell interference between users using the same pilot sequence in neighboring cells.In this network,three different transmission scenarios are considered: squares,narrow streets and tall buildings.In each transmission scenario,users are simultaneously distributed in the cell-center sector and cell-edge sector.The optimal antenna pitch angle of each base station is solved by IQBFO algorithm to optimize the network and speed in these three transmission scenarios.The simulation results show that due to the denser user distribution in narrow street scenarios,the inter-user interference in each cell is more serious,which leads to the lower sum rate of the network,and it is confirmed once again that the IQBFO algorithm has better convergence characteristics when solving such combinatorial non-convex optimization problems.