  Distributed or Cell-free (CF) massive Multiple-Input, Multiple-Output
(mMIMO), has been recently proposed as an answer to the limitations of the
current network-centric systems in providing high-rate ubiquitous transmission.
The capability of providing uniform service level makes CF mMIMO a potential
technology for beyond-5G and 6G networks. The acquisition of accurate Channel
State Information (CSI) is critical for different CF mMIMO operations. Hence,
an uplink pilot training phase is used to efficiently estimate transmission
channels. The number of available orthogonal pilot signals is limited, and
reusing these pilots will increase co-pilot interference. This causes an
undesirable effect known as pilot contamination that could reduce the system
performance. Hence, a proper pilot reuse strategy is needed to mitigate the
effects of pilot contamination. In this paper, we formulate pilot assignment in
CF mMIMO as a diverse clustering problem and propose an iterative maxima search
scheme to solve it. In this approach, we first form the clusters of User
Equipments (UEs) so that the intra-cluster diversity maximizes and then assign
the same pilots for all UEs in the same cluster. The numerical results show the
proposed techniques' superiority over other methods concerning the achieved
uplink and downlink average and per-user data rate.
