Massive MIMO uses a large number of antennas to increase the spectral efficiency (SE) through spatial multiplexing of users, which requires accurate channel state information. It is often assumed that regular pilots (RP), where a fraction of the time-frequency resources is reserved for pilots, suffices to provide high SE. However, the SE is limited by the pilot overhead and pilot contamination. An alternative is superimposed pilots (SP) where all resources are used for pilots and data. This removes the pilot overhead and reduces pilot contamination by using longer pilots. However, SP suffers from data interference that reduces the SE gains. This paper proposes the Massive-MIMO Iterative Channel Estimation and Decoding (MICED) algorithm where partially decoded data is used as side-information to improve the channel estimation and increase SE. We show that users with precise data estimates can help users with poor data estimates to decode. Numerical results with QPSK modulation and LDPC codes show that the MICED algorithm increases the SE and reduces the block-error-rate with RP and SP compared to conventional methods. The MICED algorithm with SP delivers the highest SE and it is especially effective in scenarios with short coherence blocks like high mobility or high frequencies.

中文翻译：

---

上行链路中的大规模 MIMO 迭代信道估计和解码 (MICED)

Massive MIMO使用大量天线通过用户空间复用来提高频谱效率（SE），这需要准确的信道状态信息。通常假设常规导频 (RP)，其中一小部分时频资源是为导频保留的，足以提供高 SE。然而，SE 受到导频开销和导频污染的限制。另一种选择是叠加导频 (SP)，其中所有资源都用于导频和数据。这通过使用更长的导频消除了导频开销并减少了导频污染。然而，SP 会受到数据干扰的影响，这会降低 SE 的增益。本文提出了大规模 MIMO 迭代信道估计和解码 (MICED) 算法，其中部分解码的数据用作边信息以改进信道估计并增加 SE。我们表明，具有精确数据估计的用户可以帮助数据估计较差的用户进行解码。QPSK 调制和 LDPC 码的数值结果表明，与传统方法相比，MICED 算法增加了 SE，并降低了 RP 和 SP 的误块率。带有 SP 的 MICED 算法提供最高的 SE，它在高移动性或高频等短相干块的场景中特别有效。