We investigate mismatched data detection for massive multi-user (MU) multiple-input multiple-output (MIMO) wireless systems in which the prior distribution of the transmit signal used in the data detector differs from the true prior. In order to minimize the performance loss caused by the prior mismatch, we include a tuning stage into the recently proposed large-MIMO approximate message passing (LAMA) algorithm, which enables the development of data detectors with optimal as well as sub-optimal parameter tuning. We show that carefully-selected priors enable the design of simpler and computationally more efficient data detection algorithms compared to LAMA that uses the optimal prior, while achieving near-optimal error-rate performance. In particular, we demonstrate that a hardware-friendly approximation of the exact prior enables the design of low-complexity data detectors that achieve near individually-optimal performance. Furthermore, for Gaussian priors and uniform priors within a hypercube covering the quadrature amplitude modulation (QAM) constellation, our performance analysis recovers classical and recent results on linear and non-linear massive MU-MIMO data detection, respectively.

中文翻译：

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大规模 MU-MIMO 中的不匹配数据检测


我们研究大规模多用户（MU）多输入多输出（MIMO）无线系统的不匹配数据检测，其中数据检测器中使用的发射信号的先验分布与真实先验不同。为了最大限度地减少先前不匹配造成的性能损失，我们在最近提出的大型 MIMO 近似消息传递（LAMA）算法中加入了一个调整阶段，这使得能够开发具有最佳和次优参数调整的数据检测器。我们证明，与使用最佳先验的 LAMA 相比，精心选择的先验能够设计出更简单、计算更高效的数据检测算法，同时实现接近最佳的错误率性能。特别是，我们证明了精确先验的硬件友好近似可以实现低复杂度数据检测器的设计，从而实现接近个体最佳的性能。此外，对于覆盖正交幅度调制 (QAM) 星座的超立方体内的高斯先验和均匀先验，我们的性能分析分别恢复了线性和非线性大规模 MU-MIMO 数据检测的经典结果和最新结果。