Massive MIMO Channel Estimation With Low-Resolution Spatial Sigma-Delta ADCs
Massive MIMO Channel Estimation With Low-Resolution Spatial Sigma-Delta ADCs
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DOI：
10.1109/access.2021.3101159
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发表时间：
2020-05
期刊：
影响因子：
3.9
通讯作者：
Shilpa Rao;G. Seco-Granados;Hessam Pirzadeh;A. L. Swindlehurst
中科院分区：
文献类型：
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作者：
Shilpa Rao;G. Seco-Granados;Hessam Pirzadeh;A. L. Swindlehurst
我们考虑上行链路大规模多输入多输出 (MIMO) 系统的信道估计，其中基站 (BS) 使用具有低分辨率（1-2 位）模数转换器的阵列和空间 Sigma-Delta ( $\Sigma \Delta $ ) 架构来塑造量化噪声，使其远离某些角度扇区中的用户。我们开发了一种基于 Bussgang 分解的线性最小均方误差 (LMMSE) 信道估计器，该估计器使用等效线性模型加上量化噪声重新表述非线性量化器模型。我们还分析了最大比合并 (MRC)、迫零 (ZF) 和 LMMSE 接收器的上行链路可实现速率，并提供了 MRC 接收器可实现速率的下限。数值结果表明，与传统的 1 或 2 位量化大规模 MIMO 系统相比，使用 $\Sigma \Delta $ 架构具有卓越的信道估计和总频谱效率性能。
We consider channel estimation for an uplink massive multiple-input multiple-output (MIMO) system where the base station (BS) uses an array with low-resolution (1-2 bit) analog-to-digital converters and a spatial Sigma-Delta ( $\Sigma \Delta $ ) architecture to shape the quantization noise away from users in some angular sector. We develop a linear minimum mean squared error (LMMSE) channel estimator based on the Bussgang decomposition that reformulates the nonlinear quantizer model using an equivalent linear model plus quantization noise. We also analyze the uplink achievable rate with maximal ratio combining (MRC), zero-forcing (ZF) and LMMSE receivers and provide a lower bound for the achievable rate with the MRC receiver. Numerical results show superior channel estimation and sum spectral efficiency performance using the $\Sigma \Delta $ architecture compared to conventional 1- or 2-bit quantized massive MIMO systems.