Abstract
Channel-charting (CC) is a machine learning technique for learning a multi-cell radio map, which can be used for cognitive radio-resource-management (RRM) problems. Each base-station (BS) extracts features from the channel-state-information samples (CSI) from transmissions of user-equipment (UE) at different unknown locations. The multi-path channel components are estimated and used to construct a dissimilarity matrix between CSI samples at each BS. A fusion center combines the dissimilarity matrices of all base-stations, performs dimensional reduction based on manifold learning, constructing a Multipoint-CC (MPCC). The MPCC is a two dimension map, where the spatial difference between any pair of UEs closely approximates the distance between the clustered features. MPCC provides a mapping for any given trained UE location. To use MPCC for cognitive RRM tasks, CSI measurements for new UEs would be acquired, and these UEs would be placed on the radio map. Repeating the MPCC procedure for out-of-sample CSI measurements is computationally expensive. For this, extensions of MPCC to out-of-sample UE CSIs are investigated in this paper, when Laplacian-Eigenmaps (LE) is used for dimensional reduction. Simulation results are used to show the merits of the proposed approach.
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References
Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural Comput. 15(6), 1373–1396 (2003)
Bengio, Y., Paiement, J.F., Vincent, P., Delalleau, O., Roux, N.L., Ouimet, M.: Out-of-sample extensions for LLE, Isomap, MDS, eigenmaps, and spectral clustering. In: Advances in Neural Information Processing Systems, pp. 177–184. MIT Press (2004)
Bjornson, E., Larsson, E.G., Marzetta, T.L.: Massive MIMO: ten myths and one critical question. IEEE Trans. Commun. 54(2), 114–123 (2016)
Busari, S.A., Huq, K.M.S., Mumtaz, S., Dai, L., Rodriguez, J.: Millimeter-wave massive MIMO communication for future wireless systems: a survey. IEEE Commun. Surv. Tutor. 20(2), 836–869 (2018)
Deng, J., Medjkouh, S., Malm, N., Tirkkonen, O., Studer, C.: Multipoint channel charting for wireless networks. In: Proceedings of 52nd Asilomar Conference on Signals, Systems, and Computers, pp. 286–290, October 2018
Ester, M., Kriegel, H.P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise, pp. 226–231. AAAI Press (1996)
Garcia, N., Wymeersch, H., Larsson, E.G., Haimovich, A.M., Coulon, M.: Direct localization for massive MIMO. IEEE Trans. Sig. Process. 65(10), 2475–2487 (2017)
Guidi, F., Guerra, A., Dardari, D., Clemente, A., D’Errico, R.: Environment mapping with millimeter-wave massive arrays: System design and performance. In: Proceedings of IEEE Globecom Workshops (GC Wkshps), pp. 1–6, December 2016
van der Maaten, L., Postma, E.O., van den Herik, H.J.: Dimensionality reduction: a comparative review (2008)
Schmidt, R.: Multiple emitter location and signal parameter estimation. IEEE Trans. Antennas Propag. 34(3), 276–280 (1986)
Shahmansoori, A., Garcia, G.E., Destino, G., Seco-Granados, G., Wymeersch, H.: Position and orientation estimation through millimeter-wave MIMO in 5G systems. IEEE Trans. Wireless Commun. 17(3), 1822–1835 (2018)
Studer, C., Medjkouh, S., Gönültaş, E., Goldstein, T., Tirkkonen, O.: Channel charting: locating users within the radio environment using channel state information. IEEE Access 6, 47682–47698 (2018)
Yang, S., Hanzo, L.: Fifty years of MIMO detection: the road to large-scale MIMOs. IEEE Commun. Surv. Tutor. 17(4), 1941–1988 (2015)
Acknowledgement
This work was funded in part by the Academy of Finland (grant 319484).
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© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Ponnada, T., Al-Tous, H., Tirkkonen, O., Studer, C. (2019). An Out-of-Sample Extension for Wireless Multipoint Channel Charting. In: Kliks, A., et al. Cognitive Radio-Oriented Wireless Networks. CrownCom 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 291. Springer, Cham. https://doi.org/10.1007/978-3-030-25748-4_16
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DOI: https://doi.org/10.1007/978-3-030-25748-4_16
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