Turbo-Like Joint Data-and-Channel Estimation in Quantized Massive MIMO Systems

机译：量化大规模MIMO系统中的Turbo-Like联合数据和信道估计

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We consider joint channel-and-data estimation for quantized massive MIMO systems. The estimation for both parts follows a turbo-like fashion, where the estimation error of one step is treated as additive Gaussian noise for the other. An approximate belief propagation algorithm is employed to obtain an approximate minimum mean square error estimate of both the data and channel. The performance of our scheme is compared to a Bayes optimal joint channel-and-data estimation approach by Wen et al. (2015). We observe that 10 turbo iterations are enough to achieve similar performance with lower complexity.

机译：我们考虑针对量化的大规模MIMO系统进行联合信道和数据估计。这两部分的估计都遵循类似turbo的方式，其中一步的估计误差被视为另一步的加性高斯噪声。采用近似置信度传播算法来获得数据和通道的近似最小均方误差估计。 Wen等人将我们方案的性能与贝叶斯最优联合信道和数据估计方法进行了比较。（2015）。我们观察到10个Turbo迭代足以以较低的复杂度实现类似的性能。

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《International ITG Workshop on Smart Antennas》|2016年|1-5|共5页
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Fabian Steiner; Amine Mezghani; Lee Swindlehurst; Josef A. Nossek; Wolfgang Utschick;
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Turbo-Like Joint Data-and-Channel Estimation in Quantized Massive MIMO Systems

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