Antenna selection method based on full cross weight genetic algorithm

Abstract

The invention relates to an antenna selection method based on a full cross weight genetic algorithm, which is used for LTE-A (Long Term Evolution-Advanced) and belongs to the field of wireless mobile communication. MIMO system antennae in LTE-A are selected by adopting the genetic algorithm based on real number weight values, one chromosome represents the priorities of all antennae, each antenna corresponds to a gene with a weight value, and possible antenna selection manners are searched through the operations of crossing, variation, superior selection and inferior elimination, and the like in the genetic algorithm. Compared with the original simple genetic algorithm, the invention solves the problem that multiple antennae have the same priority values after crossing operation, has performance closer to an optimal antenna selection result, and can provide higher channel capacity under the condition of basically identical complexity; the step of generating a crossing word cover is omitted, and the hardware complexity of the system is reduced; and the antenna selection method based on the full cross weight genetic algorithm has good convergence, and a better solution can be found more quickly.

Description

technical field [0001] The invention relates to an antenna selection method based on a full cross weight genetic algorithm, in particular to an LTE-A antenna selection method based on a full cross weight genetic algorithm, belonging to the field of wireless mobile communications. Background technique [0002] TD-LTE is a milestone in the development of mobile communication technology and has been commercially promoted in Sweden and Japan. As a quasi-4G technology, LTE has made significant progress in indicators such as transmission rate, delay, terminal experience, and operating costs. With the advancement of technology, the new generation of mobile communication network has more complete network coverage, higher data service capability and comprehensive real-time multimedia services, and provides users with better network services in a more complex wireless environment. Multiple-input multiple-output MIMO technology is a key part of LTE. Using MIMO technology, the communi...

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Problems solved by technology

Subsequently, the researchers proposed a weighted genetic algorithm, which selects several antennas with the highest priority as the target antenna by assigning different weights to the chromosomal genes corresponding to the antennas; however, the weights in this algorithm are all integer values, which is easy After the crossover operation, there is a problem that multiple antennas have the same priority value, and additional sorting and refilling operations are required, which increases the complexity of the system

Since then, some researchers have proposed a real-valued weighted genetic algorithm, which solves this problem by setting the priority value of the gene as a real number. While reducing the complexity, it also improves the selection effect to a certain extent. However, its performance and optimal Method - Compared with exhaustive search, there is still a certain gap

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