Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
162 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
45 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Optimizing Multicarrier Multiantenna Systems for LoS Channel Charting (2310.03762v2)

Published 28 Sep 2023 in eess.SP and cs.AI

Abstract: Channel charting (CC) consists in learning a mapping between the space of raw channel observations, made available from pilot-based channel estimation in multicarrier multiantenna system, and a low-dimensional space where close points correspond to channels of user equipments (UEs) close spatially. Among the different methods of learning this mapping, some rely on a distance measure between channel vectors. Such a distance should reliably reflect the local spatial neighborhoods of the UEs. The recently proposed phase-insensitive (PI) distance exhibits good properties in this regards, but suffers from ambiguities due to both its periodic and oscillatory aspects, making users far away from each other appear closer in some cases. In this paper, a thorough theoretical analysis of the said distance and its limitations is provided, giving insights on how they can be mitigated. Guidelines for designing systems capable of learning quality charts are consequently derived. Experimental validation is then conducted on synthetic and realistic data in different scenarios.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (29)
  1. F. Tang, Y. Kawamoto, N. Kato, and J. Liu, “Future intelligent and secure vehicular network toward 6g: Machine-learning approaches,” Proceedings of the IEEE, vol. 108, no. 2, pp. 292–307, 2019.
  2. S. Ali, W. Saad, N. Rajatheva, K. Chang, D. Steinbach, B. Sliwa, C. Wietfeld, K. Mei, H. Shiri, H.-J. Zepernick et al., “6g white paper on machine learning in wireless communication networks,” arXiv preprint arXiv:2004.13875, 2020.
  3. F. Miltiadis, L. Vasiliki, M. Jafar, M. Mattia, E. Soykan, B. Tamas, R. Nandana, R. Nuwanthika, L. Le Magoarou, P. Pietro et al., “Pervasive artificial intelligence in next generation wireless: The hexa-x project perspective,” in Proc. 1st Int. Workshop Artif. Intell. Beyond 5G 6G Wireless Netw.(AIG) Co-Located With IEEE World Congr. Comput. Intell.(WCCI), 2022.
  4. Y. Shi, L. Lian, Y. Shi, Z. Wang, Y. Zhou, L. Fu, L. Bai, J. Zhang, and W. Zhang, “Machine learning for large-scale optimization in 6g wireless networks,” arXiv preprint arXiv:2301.03377, 2023.
  5. M. Merluzzi, T. Borsos, N. Rajatheva, A. A. Benczúr, H. Farhadi, T. Yassine, M. D. Mück, S. Barmpounakis, E. C. Strinati, D. Dampahalage, P. Demestichas, P. Ducange, M. C. Filippou, L. G. Baltar, J. Haraldson, L. Karaçay, D. Korpi, V. Lamprousi, F. Marcelloni, J. Mohammadi, N. Rajapaksha, A. Renda, and M. A. Uusitalo, “The hexa-x project vision on artificial intelligence and machine learning-driven communication and computation co-design for 6g,” IEEE Access, pp. 1–1, 2023.
  6. C. Studer, S. Medjkouh, E. Gönültaş, T. Goldstein, and O. Tirkkonen, “Channel charting: Locating users within the radio environment using channel state information,” IEEE Access, vol. 6, pp. 47 682–47 698, 2018.
  7. S. Dwivedi, R. Shreevastav, F. Munier, J. Nygren, I. Siomina, Y. Lyazidi, D. Shrestha, G. Lindmark, P. Ernström, E. Stare, S. M. Razavi, S. Muruganathan, G. Masini, Åke Busin, and F. Gunnarsson, “Positioning in 5g networks,” IEEE Communications Magazine, vol. 59, pp. 38–44, 2021.
  8. X. Wang, L. Gao, S. Mao, and S. Pandey, “Deepfi: Deep learning for indoor fingerprinting using channel state information,” in 2015 IEEE Wireless Communications and Networking Conference (WCNC), 2015, pp. 1666–1671.
  9. J. Vieira, E. Leitinger, M. Sarajlic, X. Li, and F. Tufvesson, “Deep convolutional neural networks for massive mimo fingerprint-based positioning,” in 2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC).   IEEE, 2017, pp. 1–6.
  10. A. Decurninge, L. G. Ordóñez, P. Ferrand, H. Gaoning, L. Bojie, Z. Wei, and M. Guillaud, “Csi-based outdoor localization for massive mimo: Experiments with a learning approach,” in 2018 15th International Symposium on Wireless Communication Systems (ISWCS), 2018, pp. 1–6.
  11. P. Ferrand, A. Decurninge, and M. Guillaud, “Dnn-based localization from channel estimates: Feature design and experimental results,” in GLOBECOM 2020 - 2020 IEEE Global Communications Conference, 2020, pp. 1–6.
  12. P. Ferrand, A. Decurninge, L. G. Ordoñez, and M. Guillaud, “Triplet-based wireless channel charting: Architecture and experiments,” IEEE J. Sel. Areas Commun., vol. 39, pp. 2361–2373, 2021.
  13. B. Rappaport, E. Gönültaş, J. Hoydis, M. Arnold, P. K. Srinath, and C. Studer, “Improving channel charting using a split triplet loss and an inertial regularizer,” in Proc. IEEE 17th Int. Symp. Wireless Commun. Syst. (ISWCS).   Berlin, Germany: IEEE, 2021, pp. 1–6.
  14. P. Kazemi, H. Al-Tous, C. Studer, and O. Tirkkonen, “Snr prediction in cellular systems based on channel charting,” in 2020 IEEE Eighth International Conference on Communications and Networking (ComNet), 2020, pp. 1–8.
  15. T. Ponnada, P. Kazemi, H. Al-Tous, Y.-C. Liang, and O. Tirkkonen, “Best beam prediction in non-standalone mm wave systems,” in 2021 Joint European Conference on Networks and Communications; 6G Summit (EuCNC/6G Summit).   IEEE, jun 2021.
  16. L. Ribeiro, M. Leinonen, H. Djelouat, and M. Juntti, “Channel charting for pilot reuse in mmtc with spatially correlated mimo channels,” in 2020 IEEE Globecom Workshops (GC Wkshps, 2020, pp. 1–6.
  17. L. Le Magoarou, T. Yassine, S. Paquelet, and M. Crussière, “Channel charting based beamforming.”   Pacific Grove, CA, USA: IEEE, 2022, pp. 1185–1189.
  18. P. Ferrand, M. Guillaud, C. Studer, and O. Tirkkonen, “Wireless channel charting: Theory, practice, and applications,” Apr. 2023.
  19. P. Agostini, Z. Utkovski, and S. Stańczak, “Channel charting: an euclidean distance matrix completion perspective.”   Barcelona, Spain: IEEE, 2020, pp. 5010–5014.
  20. L. Le Magoarou, “Efficient channel charting via phase-insensitive distance computation,” IEEE Wireless Commun. Lett., vol. 10, pp. 2634–2638, 2021.
  21. H. Al-Tous, P. Kazemi, C. Studer, and O. Tirkkonen, “Channel charting with angle-delay-power-profile features and earth-mover distance.”   Pacific Grove, CA, USA: IEEE, 2022, pp. 1195–1201.
  22. M. Stahlke, G. Yammine, T. Feigl, B. M. Eskofier, and C. Mutschler, “Indoor localization with robust global channel charting: A time-distance-based approach,” IEEE Transactions on Machine Learning in Communications and Networking, vol. 1, pp. 3–17, 2023.
  23. T. Yassine, L. Le Magoarou, S. Paquelet, and M. Crussière, “Leveraging triplet loss and nonlinear dimensionality reduction for on-the-fly channel charting,” in 2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC).   Oulu, Finland: IEEE, jul 2022, pp. 1–5.
  24. E. Lei, O. Castañeda, O. Tirkkonen, T. Goldstein, and C. Studer, “Siamese neural networks for wireless positioning and channel charting,” in Proc. 57th Ann. Allerton Conf. Commun., Control, and Comput.   Monticello, IL, USA: IEEE, 2019, pp. 200–207.
  25. P. Agostini, Z. Utkovski, S. Stańczak, A. A. Memon, B. Zafar, and M. Haardt, “Not-too-deep channel charting (n2d-cc).”   Austin, TX, USA: IEEE, 2022, pp. 2160–2165.
  26. H. Wymeersch, D. Shrestha, C. M. de Lima, V. Yajnanarayana, B. Richerzhagen, M. F. Keskin, K. Schindhelm, A. Ramirez, A. Wolfgang, M. F. de Guzman, K. Haneda, T. Svensson, R. Baldemair, and S. Parkvall, “Integration of communication and sensing in 6g: a joint industrial and academic perspective.”   Helsinki, Finland: IEEE, 2021, pp. 1–7.
  27. J. M. Mateos-Ramos, C. Häger, M. F. Keskin, L. L. Magoarou, and H. Wymeersch, “Model-driven end-to-end learning for integrated sensing and communication,” Dec. 2022.
  28. E. W. Weisstein, “Bessel function zeros,” MathWorld–A Wolfram Web Resource. [Online]. Available: https://mathworld.wolfram.com/BesselFunctionZeros.html, 2020.
  29. J. Hoydis, F. Ait Aoudia, S. Cammerer, M. Nimier-David, N. Binder, G. Marcus, and A. Keller, “Sionna RT: Differentiable Ray Tracing for Radio Propagation Modeling,” arXiv preprint, Mar. 2023.
Citations (3)

Summary

We haven't generated a summary for this paper yet.

X Twitter Logo Streamline Icon: https://streamlinehq.com