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Multivariate Extreme Value Theory Based Channel Modeling for Ultra-Reliable Communications (2401.05833v1)

Published 11 Jan 2024 in cs.IT, eess.SP, and math.IT

Abstract: Attaining ultra-reliable communication (URC) in fifth-generation (5G) and beyond networks requires deriving statistics of channel in ultra-reliable region by modeling the extreme events. Extreme value theory (EVT) has been previously adopted in channel modeling to characterize the lower tail of received powers in URC systems. In this paper, we propose a multivariate EVT (MEVT)-based channel modeling methodology for tail of the joint distribution of multi-channel by characterizing the multivariate extremes of multiple-input multiple-output (MIMO) system. The proposed approach derives lower tail statistics of received power of each channel by using the generalized Pareto distribution (GPD). Then, tail of the joint distribution is modeled as a function of estimated GPD parameters based on two approaches: logistic distribution, which utilizes logistic distribution to determine dependency factors among the Frechet transformed tail sequence and obtain a bi-variate extreme value model, and Poisson point process, which estimates probability measure function of the Pickands angular component to model bi-variate extreme values. Finally, validity of the proposed models is assessed by incorporating the mean constraint on probability measure function of Pichanks coordinates. Based on the data collected within the engine compartment of Fiat Linea, we demonstrate the superiority of proposed methodology compared to the conventional extrapolation-based methods in providing the best fit to the multivariate extremes.

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References (56)
  1. P. Popovski, J. J. Nielsen, C. Stefanovic, E. De Carvalho, E. Strom, K. F. Trillingsgaard, A.-S. Bana, D. M. Kim, R. Kotaba, J. Park, et al., “Wireless access for ultra-reliable low-latency communication: Principles and building blocks,” IEEE Network, vol. 32, no. 2, pp. 16–23, 2018.
  2. C. Gustafson, K. Haneda, S. Wyne, and F. Tufvesson, “On mm-wave multipath clustering and channel modeling,” IEEE Transactions on Antennas and Propagation, vol. 62, no. 3, pp. 1445–1455, 2014.
  3. P. Popovski, Č. Stefanović, J. J. Nielsen, E. De Carvalho, M. Angjelichinoski, K. F. Trillingsgaard, and A.-S. Bana, “Wireless access in ultra-reliable low-latency communication (URLLC),” IEEE Transactions on Communications, vol. 67, no. 8, pp. 5783–5801, 2019.
  4. P. C. F. Eggers, M. Angjelichinoski, and P. Popovski, “Wireless channel modeling perspectives for ultra-reliable communications,” IEEE Transactions on Wireless Communications, vol. 18, no. 4, pp. 2229–2243, 2019.
  5. M. Angjelichinoski, K. F. Trillingsgaard, and P. Popovski, “A statistical learning approach to ultra-reliable low latency communication,” IEEE Transactions on Communications, vol. 67, no. 7, pp. 5153–5166, 2019.
  6. S. Samarakoon, M. Bennis, W. Saad, and M. Debbah, “Predictive ultra-reliable communication: A survival analysis perspective,” IEEE Communications Letters, vol. 25, no. 4, pp. 1221–1225, 2020.
  7. D. Feng, L. Lai, J. Luo, Y. Zhong, C. Zheng, and K. Ying, “Ultra-reliable and low-latency communications: applications, opportunities and challenges,” Science China Information Sciences, vol. 64, pp. 1–12, 2021.
  8. M. A. Siddiqi, H. Yu, and J. Joung, “5g ultra-reliable low-latency communication implementation challenges and operational issues with iot devices,” Electronics, vol. 8, no. 9, p. 981, 2019.
  9. A. E. Kalor and P. Popovski, “Ultra-reliable communication for services with heterogeneous latency requirements,” in 2019 IEEE Globecom Workshops (GC Wkshps), pp. 1–6, 2019.
  10. B. Kharel, O. L. A. López, H. Alves, and M. Latva-Aho, “Ultra-reliable communication for critical machine type communication via cran-enabled multi-connectivity diversity schemes,” Sensors, vol. 21, no. 23, p. 8064, 2021.
  11. N. Mehrnia and S. Coleri, “Wireless channel modeling based on extreme value theory for ultra-reliable communications,” IEEE Transactions on Wireless Communications, pp. 1–1, 2021.
  12. N. Mehrnia and S. Coleri, “Non-stationary wireless channel modeling approach based on extreme value theory for ultra-reliable communications,” IEEE Transactions on Vehicular Technology, vol. 70, no. 8, pp. 8264–8268, 2021.
  13. S. R. Khosravirad, H. Viswanathan, and W. Yu, “Exploiting diversity for ultra-reliable and low-latency wireless control,” IEEE Transactions on Wireless Communications, vol. 20, no. 1, pp. 316–331, 2020.
  14. Springer, 2001.
  15. M. Serror, C. Dombrowski, K. Wehrle, and J. Gross, “Channel coding versus cooperative arq: Reducing outage probability in ultra-low latency wireless communications,” in 2015 IEEE Globecom Workshops (GC Wkshps), pp. 1–6, IEEE, 2015.
  16. C. Boyd, R. Kotaba, O. Tirkkonen, and P. Popovski, “Non-orthogonal contention-based access for urllc devices with frequency diversity,” in 2019 IEEE 20th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), pp. 1–5, IEEE, 2019.
  17. J. J. Nielsen, R. Liu, and P. Popovski, “Ultra-reliable low latency communication using interface diversity,” IEEE Transactions on Communications, vol. 66, pp. 1322–1334, March 2018.
  18. V. N. Swamy, P. Rigge, G. Ranade, B. Nikolić, and A. Sahai, “Wireless channel dynamics and robustness for ultra-reliable low-latency communications,” IEEE Journal on Selected Areas in Communications, vol. 37, no. 4, pp. 705–720, 2019.
  19. J. W. Wallace and M. A. Jensen, “Modeling the indoor mimo wireless channel,” IEEE Transactions on Antennas and Propagation, vol. 50, no. 5, pp. 591–599, 2002.
  20. K. Zheng, S. Ou, and X. Yin, “Massive mimo channel models: A survey,” International Journal of Antennas and Propagation, vol. 2014, 2014.
  21. P. Almers, E. Bonek, A. Burr, N. Czink, M. Debbah, V. Degli-Esposti, H. Hofstetter, P. Kyösti, D. Laurenson, G. Matz, et al., “Survey of channel and radio propagation models for wireless mimo systems,” EURASIP Journal on Wireless Communications and Networking, vol. 2007, pp. 1–19, 2007.
  22. V. N. Swamy, P. Rigge, G. Ranade, B. Nikolic, and A. Sahai, “Predicting wireless channels for ultra-reliable low-latency communications,” in 2018 IEEE International Symposium on Information Theory (ISIT), pp. 2609–2613, IEEE, 2018.
  23. W. Zhang, M. Derakhshani, and S. Lambotharan, “Non-parametric statistical learning for urllc transmission rate control,” in ICC 2021-IEEE International Conference on Communications, pp. 1–6, IEEE, 2021.
  24. S. Kotz and S. Nadarajah, Extreme value distributions: theory and applications. World Scientific, 2000.
  25. S. Samarakoon, M. Bennis, W. Saad, and M. Debbah, “Federated learning for ultra-reliable low-latency V2V communications,” in 2018 IEEE Global Communications Conference (GLOBECOM), pp. 1–7, IEEE, 2018.
  26. C.-F. Liu and M. Bennis, “Ultra-reliable and low-latency vehicular transmission: An extreme value theory approach,” IEEE Communications Letters, vol. 22, no. 6, pp. 1292–1295, 2018.
  27. M. K. Abdel-Aziz, S. Samarakoon, C.-F. Liu, M. Bennis, and W. Saad, “Optimized age of information tail for ultra-reliable low-latency communications in vehicular networks,” IEEE Transactions on Communications, vol. 68, no. 3, pp. 1911–1924, 2019.
  28. A. Mahmood and R. Jäntti, “Packet error rate analysis of uncoded schemes in block-fading channels using extreme value theory,” IEEE Communications Letters, vol. 21, no. 1, pp. 208–211, 2016.
  29. G. Song and Y. Li, “Asymptotic throughput analysis for channel-aware scheduling,” IEEE Transactions on Communications, vol. 54, no. 10, pp. 1827–1834, 2006.
  30. Y. H. Al-Badarneh, C. N. Georghiades, and M.-S. Alouini, “Asymptotic performance analysis of the k𝑘kitalic_k th best link selection over wireless fading channels: An extreme value theory approach,” IEEE Transactions on Vehicular Technology, vol. 67, no. 7, pp. 6652–6657, 2018.
  31. Y. Zhu, Y. Hu, T. Yang, T. Yang, J. Vogt, and A. Schmeink, “Reliability-optimal offloading in low-latency edge computing networks: Analytical and reinforcement learning based designs,” IEEE Transactions on Vehicular Technology, vol. 70, no. 6, pp. 6058–6072, 2021.
  32. C. Chaccour, M. N. Soorki, W. Saad, M. Bennis, and P. Popovski, “Can terahertz provide high-rate reliable low-latency communications for wireless VR?,” IEEE Internet of Things Journal, vol. 9, no. 12, pp. 9712–9729, 2022.
  33. A. Subhash and S. Kalyani, “Cooperative relaying in a swipt network: Asymptotic analysis using extreme value theory for non-identically distributed rvs,” IEEE Transactions on Communications, vol. 69, no. 7, pp. 4360–4372, 2021.
  34. S. Samarakoon, M. Bennis, W. Saad, and M. Debbah, “Distributed federated learning for ultra-reliable low-latency vehicular communications,” IEEE Transactions on Communications, vol. 68, no. 2, pp. 1146–1159, 2019.
  35. N. Mehrnia and S. Coleri, “Extreme value theory based rate selection for ultra-reliable communications,” IEEE Transactions on Vehicular Technology, pp. 1–1, 2022.
  36. N. Mehrnia and S. Coleri, “Incorporation of confidence interval into rate selection based on the extreme value theory for ultra-reliable communications,” in European Conference on Networks and Communications (EuCNC) and the 6G Summit, pp. 1–6, IEEE, 2022.
  37. CRC Press, 2003.
  38. J. Galambos, “Extreme value theory for applications,” in Proceedings of the Conference on Extreme Value Theory and Applications, pp. 1–14, Springer, 1994.
  39. Y. Bensalah, Steps in applying extreme value theory to finance: a review. Bank of Canada, 2000.
  40. M. Makarov, “Applications of exact extreme value theorem,” Journal of Operational Risk, vol. 2, no. 1, pp. 115–120, 2007.
  41. S. Caires, “Extreme value analysis: wave data,” Joint Tech Comm for Oceanography &\&& Marine Meteorology (JCOMM) Technical Report, no. 57, 2011.
  42. A. Mata, “Parameter uncertainty for extreme value distributions,” in GIRO Convention Papers, pp. 151–173, 2000.
  43. G. Durisi, T. Koch, and P. Popovski, “Toward massive, ultrareliable, and low-latency wireless communication with short packets,” Proceedings of the IEEE, vol. 104, no. 9, pp. 1711–1726, 2016.
  44. Y. Polyanskiy, H. V. Poor, and S. Verdú, “Channel coding rate in the finite blocklength regime,” IEEE Transactions on Information Theory, vol. 56, no. 5, pp. 2307–2359, 2010.
  45. M. Bennis, M. Debbah, and H. V. Poor, “Ultra reliable and low-latency wireless communication: Tail, risk, and scale,” Proceedings of the IEEE, vol. 106, no. 10, pp. 1834–1853, 2018.
  46. P. de Zea Bermudez and S. Kotz, “Parameter estimation of the generalized pareto distribution—part II,” Journal of Statistical Planning and Inference, vol. 140, no. 6, pp. 1374–1388, 2010.
  47. H. Joe, R. L. Smith, and I. Weissman, “Bivariate threshold methods for extremes,” Journal of the royal statistical society: series B (methodological), vol. 54, no. 1, pp. 171–183, 1992.
  48. L. Zheng, K. Ismail, T. Sayed, and T. Fatema, “Bivariate extreme value modeling for road safety estimation,” Accident Analysis & Prevention, vol. 120, pp. 83–91, 2018.
  49. R. Michel, Simulation and estimation in multivariate generalized Pareto models. PhD thesis, Universität Würzburg, 2006.
  50. Springer Science & Business Media, 2010.
  51. P. Capéraà and A.-L. Fougères, “Estimation of a bivariate extreme value distribution,” Extremes, vol. 3, no. 4, pp. 311–329, 2000.
  52. D. Cooley, R. A. Davis, and P. Naveau, “The pairwise beta distribution: A flexible parametric multivariate model for extremes,” Journal of Multivariate Analysis, vol. 101, no. 9, pp. 2103–2117, 2010.
  53. S. Nadarajah, “Approximations for bivariate extreme values,” Extremes, vol. 3, no. 1, pp. 87–98, 2000.
  54. J. Shi, D. Anzai, and J. Wang, “Channel modeling and performance analysis of diversity reception for implant uwb wireless link,” IEICE transactions on communications, vol. 95, no. 10, pp. 3197–3205, 2012.
  55. C. U. Bas and S. Coleri Ergen, “Ultra-wideband channel model for intra-vehicular wireless sensor networks beneath the chassis: From statistical model to simulations,” IEEE Transactions on Vehicular Technology, vol. 62, no. 1, pp. 14–25, 2012.
  56. U. Demir, C. U. Bas, and S. Coleri Ergen, “Engine compartment UWB channel model for intra-vehicular wireless sensor networks,” IEEE Transactions on Vehicular Technology, vol. 63, no. 6, pp. 2497–2505, 2013.
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