Vol. 14, No. 10, October 31, 2020
10.3837/tiis.2020.10.016,
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Abstract
This paper introduces a pilot allocation scheme for massive MIMO systems based on deep
convolutional neural network (CNN) learning. This work is an extension of a prior work on the
basic deep learning framework of the pilot assignment problem, the application of which to a
high-user density nature is difficult owing to the factorial increase in both input features and
output layers. To solve this problem, by adopting the advantages of CNN in learning image
data, we design input features that represent users’ locations in all the cells as image data with
a two-dimensional fixed-size matrix. Furthermore, using a sorting mechanism for applying
proper rule, we construct output layers with a linear space complexity according to the number
of users. We also develop a theoretical framework for the network capacity model of the
massive MIMO systems and apply it to the training process. Finally, we implement the
proposed deep CNN-based pilot assignment scheme using a commercial vanilla CNN, which
takes into account shift invariant characteristics. Through extensive simulation, we
demonstrate that the proposed work realizes about a 98% theoretical upper-bound
performance and an elapsed time of 0.842 ms with low complexity in the case of a
high-user-density condition.
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Cite this article
[IEEE Style]
K. Kim and J. Lee, "Deep CNN based Pilot Allocation Scheme in Massive MIMO systems," KSII Transactions on Internet and Information Systems, vol. 14, no. 10, pp. 4214-4230, 2020. DOI: 10.3837/tiis.2020.10.016.
[ACM Style]
Kwihoon Kim and Joohyung Lee. 2020. Deep CNN based Pilot Allocation Scheme in Massive MIMO systems. KSII Transactions on Internet and Information Systems, 14, 10, (2020), 4214-4230. DOI: 10.3837/tiis.2020.10.016.
[BibTeX Style]
@article{tiis:23928, title="Deep CNN based Pilot Allocation Scheme in Massive MIMO systems", author="Kwihoon Kim and Joohyung Lee and ", journal="KSII Transactions on Internet and Information Systems", DOI={10.3837/tiis.2020.10.016}, volume={14}, number={10}, year="2020", month={October}, pages={4214-4230}}