This video show the results of the published paper:

D. D. Souza, M. M. M. Freitas, D. B. da Costa, G. S. Borges, A. M. Cavalcante, L. Valcarenghi, and J. C. W. A. Costa, “Effective channel DL pilot-based estimation in user-centric cell-free massive MIMO networks,” in Proc. IEEE Global Commun. Conf., pp. 705-710, Dec. 2022.
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Abstract:
This paper investigates the performance of downlink (DL) pilot-based training to estimate the effective channel in usercentric cell-free massive multiple-input multiple-output (MIMO) networks. An algorithm for DL pilot assignment is proposed based on the level of interference between each user equipment (UE). It is proposed a refinement method for access point (AP) selection that controls the maximum AP cluster size of
UEs. The strategy aims to control the maximum number of APs serving each UE to reduce the disparities among the AP cluster sizes. DL pilot-based training is compared with the blind, perfect and statistical channel state information (CSI) methods, assuming different precoding techniques, AP selection schemes, and the presence of pilot contamination. Our results demonstrate the following: (i) the proposed DL pilot assignment algorithm
outperforms the baseline solutions; (ii) the proposed AP selection refinement method can improve the energy efficiency up to 86.6% without compromising the spectral efficiency; and (iii) DL pilot-based estimation reduces the normalized mean-square error significantly compared with blind and statistical CSI methods.

Code available:
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Related paper:
D. D. Souza, M. M. M. Freitas, G. S. Borges, A. M. Cavalcante, D. B. da Costa, J. C. W. A. Costa, "Effective Channel Blind Estimation in Cell-Free Massive MIMO Networks," in IEEE Wireless Communications Letters, vol. 11, no. 3, pp. 468-472, March 2022.
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