arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 1302.4433cs.ITmath.IT
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大规模MIMO系统的自适应最小BER降秩线性检测

Adaptive Minimum BER Reduced-Rank Linear Detection for Massive MIMO Systems

Yunlong Cai, Rodrigo C. de Lamare

摘要:

针对超大型多用户多输入多输出(MIMO)系统,提出了一种新的自适应降秩策略。建议的降秩方案是基于联合迭代优化(JIO)的概念,根据最小化的比特出错率(BER)的成本函数的过滤器。所提出的优化技术调整的投影矩阵和降秩滤波器的权重联合。我们开发了随机梯度(SG)算法的自适应实现,并介绍了一种新的自动秩选择方法的BER标准的基础上。多用户MIMO系统的仿真结果表明,所提出的自适应算法明显优于现有的方案。

英文摘要:

In this paper, we propose a novel adaptive reduced-rank strategy for very large multiuser multi-input multi-output (MIMO) systems. The proposed reduced-rank scheme is based on the concept of joint iterative optimization (JIO) of filters according to the minimization of the bit error rate (BER) cost function. The proposed optimization technique adjusts the weights of a projection matrix and a reduced-rank filter jointly. We develop stochastic gradient (SG) algorithms for their adaptive implementation and introduce a novel automatic rank selection method based on the BER criterion. Simulation results for multiuser MIMO systems show that the proposed adaptive algorithms significantly outperform existing schemes.

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