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

Mitigating Pilot Contamination and Enabling IoT Scalability in Massive MIMO Systems