Massive MIMO OFDM waveforms help support a large number of users in the same time-frequency resource and also provide significant array gain for uplink reception in cellular systems. However, channel estimation in such large antenna systems can be tricky as pilot assignment for multiple users becomes more challenging with increasing number of users. Additionally, the pilot overhead especially for wideband rapidly changing channels can diminish the system throughput quite significantly. In this paper, we propose an iterative matrix decomposition algorithm for the blind demodulation of massive MIMO OFDM signals without using any pilots. This new decomposition technique provides estimates of both the user symbols and the user channel in the frequency domain simultaneously (to a scaling factor) without any pilots. We discuss methods for finding the appropriate initial points for the algorithm that ensure its convergence in different types of wireless channels. We also propose new methods for resolving the scaling factor in the estimated signal that do not increase pilot overhead. We show how the method can be adapted to both single-user and multi-user systems. Simulation results demonstrate that the lack of pilots does not affect the error performance of the proposed algorithm when compared to the conventional pilot-based channel estimation and equalization methods across a wide range of channels for both single and multi-user cases. We also demonstrate techniques to reduce the complexity of the estimation algorithm over multiple OFDM symbols in a 5G MIMO system by leveraging the temporal correlations in the channel.

翻译：大规模MIMO OFDM波形有助于在同一时频资源中支持大量用户，并为蜂窝系统中的上行链路接收提供显著阵列增益。然而，在此类大规模天线系统中，信道估计可能较为棘手，因为随着用户数量增加，多用户的导频分配变得更加困难。此外，对于宽带快速变化信道，导频开销尤其会显著降低系统吞吐量。本文提出一种迭代矩阵分解算法，用于无需任何导频的大规模MIMO OFDM信号盲解调。这种新的分解技术能够同时（在缩放因子范围内）在频域估计用户符号与用户信道，且完全无需导频。我们讨论了为算法寻找合适初始点的方法，以确保其在各类无线信道中的收敛性。同时提出了新的缩放因子解析方法，这些方法不会增加导频开销。我们阐述了如何将该方法适配于单用户与多用户系统。仿真结果表明，在单用户及多用户场景下，相较于传统基于导频的信道估计与均衡方法，所提算法在各类信道中的误码性能均不受导频缺失的影响。我们还展示了在5G MIMO系统中，通过利用信道时域相关性来降低算法在多个OFDM符号上估计复杂度的技术。