Abstract
The addition of a large number of antennas to a conventional MIMO system results in significant improvement in the performance of multicellular communication systems. The performance of this so-called massive MIMO system however suffers from pilot contamination. This interference to a user communication by a nearby cell base station causes significant limitation in the performance of the system. In this work, we propose a pilot allocation scheme as a careful allocation of pilots sequences can mitigate the diverse effect of pilot contamination. We train convolutional neural networks to discover the best set of users that can share the same pilot sequences such that contamination does not occur. The simulation results show that our proposed solution is capable of pilot assignment to avoid pilot contamination.
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Code Availability
The code is confidential and cannot be shared as we plan to use it to extend the research work in near future. However, those interested can take advantage of the detailed steps of the proposed Pilot Allocation scheme as outlined in Algorithm 1 in the manuscript.
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Acknowledgements
I wish to acknowledge the support from the office of the Deanship of Research Oversight and Coordination (DROC) at KFUPM for funding the work through project No. SR171011.
Funding
This work was supported by the office of the Deanship of Research Oversight and Coordination (DROC) at KFUPM through project No. SR171011.
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Masood, M. Large-Scale MIMO Pilot Contamination: Deep Learning-Assisted Pilot Assignment Scheme. Wireless Pers Commun 129, 613–621 (2023). https://doi.org/10.1007/s11277-022-10113-5
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DOI: https://doi.org/10.1007/s11277-022-10113-5