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A closed-loop training approach for massive MIMO beamforming systems

机译:大规模MIMO波束成形系统的闭环训练方法

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There has been a growing interest in wireless systems that employ a very large number of transmit antennas. Some theoretical results have shown that substantial improvements in network capacity are possible. Despite this work, a major challenge is how these large transmit arrays should perform training to allow receiver channel estimation. Without new techniques, the heavy burden of training could overwhelm the system and mitigate any possible improvements, especially in systems using frequency division duplexing (FDD) where channel reciprocity cannot be exploited. In this work, we propose the use of closedloop training. In this framework, the transmitted training signal is optimized to improve data communications performance by using prior information about the current channel obtained from past channel estimates. The work focuses on block-fading channels with temporal and spatial correlation. Simulation results show improved performance.
机译:对于使用大量发射天线的无线系统,人们越来越感兴趣。一些理论结果表明,网络容量的显着提高是可能的。尽管有这项工作,一个主要的挑战是这些大型发射阵列应如何执行训练以允许接收器信道估计。如果没有新技术,那么沉重的培训负担可能会使系统不堪重负,并减轻任何可能的改进,尤其是在使用频分双工(FDD)的系统中,其中无法利用信道互易性的情况下。在这项工作中,我们建议使用闭环训练。在此框架中,通过使用从过去的信道估计中获得的有关当前信道的先验信息,优化了传输的训练信号以提高数据通信性能。这项工作着重于具有时间和空间相关性的衰落信道。仿真结果表明性能有所提高。

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