Mitigating Pilot Contamination and Enabling IoT Scalability in Massive
  MIMO Systems
Muhammad Kamran Saeed Ahmed E. Kamal Ashfaq Khokhar
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.