Fast antenna selection method for united receiving and transmitting end of MIMO system

Abstract

The invention discloses an antenna selection method for a united receiving and transmitting end of an MIMO system and belongs to the field of wireless communication. The method of the invention can be used for exporting channel capacity, which is expressed by binary-encoded strings, via analyzing a channel capacity formula of the MIMO system by using a thought based on binary system particle swarm optimization, as fitness function of the particle swarm optimization, which simplifies the calculated amount, and the method of the invention simultaneously improves search efficiency by performing population initialization in a cyclic shift way so as to improve the channel capacity. The method of the invention can be used for obtaining higher channel capacity than other suboptimal algorithms while reducing the computation complexity.

Description

technical field [0001] The invention relates to the field of wireless communication, in particular to a fast method for joint antenna selection in a MIMO system. Background technique [0002] The MIMO (Multi-Input Multi-Output) system configures multiple antennas at the transmitting end and the receiving end to obtain diversity gain and multiplexing gain, so that the system can double the channel capacity and transmission reliability without increasing the bandwidth. However, the MIMO system needs to use radio frequency chains matching the number of antennas, which increases the implementation cost. Fast antenna selection can maintain their advantages while reducing the implementation complexity and hardware cost in practical applications of MIMO systems. [0003] The optimal algorithm for antenna selection is the exhaustive method. This algorithm needs to traverse all possible options. By calculating the channel capacity corresponding to each option, a subset is selected f...

AI Technical Summary

Problems solved by technology

[0004] Aiming at the decoupling algorithm that is close to the optimal channel capacity but slightly more computationally complex when the joint receiver and transmitter are selected, Gorokhov et al. Algorithms that simplify the analysis of the formula are proposed; however, these algorithms need to be run with the same number of antennas at the transceiver end

[0005] Due to the superior performance of intelligent algorithms in solving convex optimization problems, genetic algorithms are applied to antenna selection with the advantage of greatly reducing computational complexity. In order to improve the mismatch between the number of antennas and requirements in the selection process of genetic algorithms, Hoang-Yang Lu proposed a priority-based fast selection algorithm, but the multi-parameter and cross-mutation operations of the genetic algorithm make the algorithm need to be improved in terms of the balance between complexity and performance

Images