Regular pilots in a massive multi-input multi-output (MIMO) system with large number of users suffer from the pilot contamination effect due to limited training time. In this paper, for a cell-free massive MIMO system, we have proposed a generalized superimposed pilot (GSP) scheme, where the available number of pilots are equal to the coherence time slots, and the transmitting data symbols are spread over the coherence time with the help of simple precoding. Further, in order to keep the system scalable, a low complexity and distributed time processing approach is employed, and the corresponding rate components are analyzed. It is shown that with careful design of precoding matrix and number of data symbols, the GSP symbols can provide much better channel estimation and data detection performance, as compared to the regular pilot scheme and the conventional superimposed scheme. These results have been verified via simulations. It is also inferred that centralized processing in cell free system improves the data detection performance than localized processing. Iterative data detection at the central node also improves the MSE of data estimates. The pilot contamination effect, is significantly reduced due to availability of larger number of pilots, as compared to regular pilots transmission.

中文翻译：

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无细胞大规模 MIMO 系统中的广义叠加训练方案

具有大量用户的大规模多输入多输出（MIMO）系统中的常规导频由于训练时间有限而遭受导频污染效应。在本文中，对于无小区的大规模 MIMO 系统，我们提出了一种广义叠加导频 (GSP) 方案，其中可用导频数等于相干时隙，传输数据符号分布在相干时间上。借助简单的预编码。此外，为了保持系统的可扩展性，采用了低复杂度和分布式时间处理的方法，并分析了相应的速率分量。结果表明，通过精心设计预编码矩阵和数据符号数量，GSP符号可以提供更好的信道估计和数据检测性能，与常规试点方案和传统叠加方案相比。这些结果已通过仿真得到验证。还可以推断，无细胞系统中的集中处理比局部处理提高了数据检测性能。中心节点的迭代数据检测也提高了数据估计的 MSE。与常规导频传输相比，由于有大量导频可用，导频污染效应显着降低。