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

TL;DR
This paper proposes a novel pilot allocation scheme based on IoT data transfer patterns and graph coloring to mitigate pilot contamination and improve scalability in massive MIMO systems, enabling support for more devices with higher efficiency.
Contribution
It introduces a pilot assignment method using graph partitioning that assigns orthogonal pilots to device clusters, enhancing scalability and spectral efficiency in massive MIMO networks.
Findings
Significantly reduces pilot contamination.
Enables support for 200 devices with only 12.5% omission.
Improves spectral efficiency in multicell massive MIMO.
Abstract
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…
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Taxonomy
TopicsAdvanced MIMO Systems Optimization · Millimeter-Wave Propagation and Modeling · Advanced Wireless Communication Technologies
