摘要
Abstract
Linear precoding techniques,such as zero forcing,can achieve the near optimal capacity.In contrast to traditional multiple input multiple output (MIMO),large scale MIMO installed hundreds of antennas,with the increasing numbers of antennas,zero forcing precoding involve the matrix inversion of large size with high computational complexity,which may cause difficulty for the realization of the application.To reduce the computational complexity of linear precoding,we propose a low complexity iterative algorithm based on the degree of Jacobi.This method realized through the linear iteration meanwhile avoided the matrix inversion.To further reduce the computation time,we propose a heterogeneous multicore parallel algorithm based on CUDA (compute unified device architecture),which leverage the Graphics Processing Unit (GPU) characteristic of multicores and multithreads to realize heterogeneous multicore architecture parallel computation.Experimental result demonstrates this proposed algorithm not only can achieve the same performance of zero forcing precoding,but also has less computation time and shorter time than traditional linear precoding.关键词
预编码/雅克比/统一计算架构/迫零/异构多核Key words
Precoding/Jacobi/CUDA/Zero forcing/Heterogeneous multicore分类
信息技术与安全科学
