Method for selecting multi-user MIMO system antenna based on priority genetic simulated annealing

Abstract

The invention discloses a method for selecting a multi-user MIMO system antenna based on priority genetic simulated annealing. TU and TB superior chromosomes are selected from chromosomes contained in user ends and a base station end, priority crossover and variation operation is conducted, so that a group and a group are obtained to be used as initial groups of simulated annealing operation, then two genes are randomly selected from each chromosome in the two groups to be exchanged so that a novel group and a novel group can be obtained, a fitness difference value delta f of each chromosome in the and the , and the and the is calculated, if the delta f is smaller than zero, or the delta f is larger than or equal to zero, and the condition of is met, moving to a new state can be achieved, and if not cooling is carried out until the number m of simulated annealing iterations is reached. By means of the method, optimal antenna subsets of the user ends and the base station end are efficiently selected in a combined mode, strong robustness and optimizing capacity are achieved, the operating speed of the system can be effectively increased, the complexity of software and hardware in the implementation process of the system is lowered, system performance and hardware cost are made to be well compromised, and the method is suitable for real-time communication systems.

Description

technical field [0001] The invention relates to an antenna selection method for a MIMO system, in particular to a method for selecting a combined antenna at a transceiver end of a multi-user MIMO system based on priority genetic simulated annealing, and belongs to the technical field of wireless communication. Background technique [0002] In recent years, with the rapid development of wireless communication technology, spectrum resources are becoming increasingly tight. The multiple-input multiple-output-put (MIMO) system can double the capacity and spectrum efficiency of the communication system without increasing the bandwidth. However, as the number of antennas increases, the hardware cost and algorithm complexity of the MIMO system will also continue to increase. Antenna selection technology can effectively solve the above problems. It selects a certain number of optimal antenna subsets at the sending end or receiving end for sending and receiving, which can effectivel...

AI Technical Summary

Problems solved by technology

Chinese patent document CN102208934A discloses an antenna selection method based on a full crossover weighted genetic algorithm, because the algorithm does not take into account that the number of genes corresponding to the selected antenna is 1 and the number of predetermined antennas is not equal in the crossover mutation operation problem, resulting in a decrease in system performance

In a multi-user MIMO system, Chinese patent document CN101394257A discloses a multi-user MIMO precoding antenna selection method, which uses the calculation of the energy sum of each user as the objective function of antenna selection, although it is not necessary to calculate the Singular value, but to determine each user and the number of antennas after rough screening, it is necessary to calculate the channel matrix norms of K users receiving antennas under various combinations, and then combine the number of antennas after rough screening to calculate and compare K respectively The sum of the energy of the users is used to obtain the antenna subset corresponding to the user with the maximum energy value as the final number of antennas, which not only increases the amount of calculation of the system but also does not optimize the number of antennas at the base station; "System The Journal of Simulation, Volume 22, Issue 6, Joint Antenna Selection for Space-Time Multiplexing Multi-User MIMO System, proposed a low-complexity precoding algorithm and corresponding optimized preprocessing algorithm, although it improved the capacity of the system to a certain extent and The solution complexity of the algorithm implementation process is reduced, but the antenna selection algorithm given in this literature still has a high computational complexity

Therefore, the above mentioned methods are not optimal antenna selection algorithms for multi-user MIMO systems.

Images