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基于低秩张量分解的大规模MIMO信息检测算法研究

The detection algorithm based on low rank tensor decomposition of large-scale MIMO
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摘要 提出了一种改进的Tucker分解法,将二维的张量分解到两个维度中.分别通过改进Tucker和Tucker算法的矩阵减秩和收敛运算,得到保存完整信息的原张量的近似估计值.仿真实验结果表明,改进Tucker算法提高了系统的检测性能. In this paper,we propose an improved Tucker decomposition algorithm.The tensor is decomposed into two dimensions.By using matrix rank reduction and convergence in both Tucker and improved Tucker algorithm,we get an approximate estimation which can preservewhole information of original tensor completely.The simulation result shows that the improved Tucker decomposition algorithm can enhance the performance of the error detection of the system.
出处 《上海师范大学学报(自然科学版)》 2017年第1期74-78,共5页 Journal of Shanghai Normal University(Natural Sciences)
基金 上海市自然基金项目(16ZR1424500)
关键词 大规模MIMO 张量分解 数据检测 Tucker算法 massive MIMO tensor decomposition data detection Tucker algorithm
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