Massive MIMO is expected to play an important role in the development of 5G
networks. This paper addresses the issue of pilot contamination and scalability
in massive MIMO systems. The current practice of reusing orthogonal pilot
sequences in adjacent cells leads to difficulty in differentiating incoming
inter- and intra-cell pilot sequences. One possible solution is to increase the
number of orthogonal pilot sequences, which results in dedicating more space of
coherence block to pilot transmission than data transmission. This, in turn,
also hinders the scalability of massive MIMO systems, particularly in
accommodating a large number of IoT devices within a cell. To overcome these
challenges, this paper devises an innovative pilot allocation scheme based on
the data transfer patterns of IoT devices. The scheme assigns orthogonal pilot
sequences to clusters of devices instead of individual devices, allowing
multiple devices to utilize the same pilot for periodically transmitting data.
Moreover, we formulate the pilot assignment problem as a graph coloring problem
and use the max k-cut graph partitioning approach to overcome the pilot
contamination in a multicell massive MIMO system. The proposed scheme
significantly improves the spectral efficiency and enables the scalability of
massive MIMO systems; for instance, by using ten orthogonal pilot sequences, we
are able to accommodate 200 devices with only a 12.5% omission rate.

本文针对巨型 MIMO 系统中的导频污染和可扩展性问题，提出了一种创新的基于物联网设备的导频分配方案，通过将导频序列分配给设备簇而不是个体设备，实现了多设备利用同一导频进行周期性数据传输，通过将导频分配问题建模为图的着色问题，并使用最大 k - 割图划分方法克服了多小区巨型 MIMO 系统中的导频污染，该方案显著提高了光谱效率并实现了巨型 MIMO 系统的可扩展性，在使用十个正交导频序列的情况下，仅有 12.5% 的遗漏率能容纳 200 个设备。