Predictive Ultra-Reliable Communication: A Survival Analysis Perspective

Ultra-reliable communication (URC) is a key enabler for supporting immersive and mission-critical 5G applications. Meeting the strict reliability requirements of these applications is challenging due to the absence of accurate statistical models tailored to URC systems. In this letter, the wireless connectivity over dynamic channels is characterized via statistical learning methods. In particular, model-based and data-driven learning approaches are proposed to estimate the non-blocking connectivity statistics over a set of training samples with no knowledge on the dynamic channel statistics. Using principles of survival analysis, the reliability of wireless connectivity is measured in terms of the probability of channel blocking events. Moreover, the maximum transmission duration for a given reliable non-blocking connectivity is predicted in conjunction with the confidence of the inferred transmission duration. Results show that the accuracy of detecting channel blocking events is higher using the model-based method for low to moderate reliability targets requiring low sample complexity. In contrast, the data-driven method shows higher detection accuracy for higher reliability targets at the cost of 100× sample complexity.

[1]  H. Vincent Poor,et al.  Ultrareliable and Low-Latency Wireless Communication: Tail, Risk, and Scale , 2018, Proceedings of the IEEE.

[2]  Petar Popovski,et al.  A Statistical Learning Approach to Ultra-Reliable Low Latency Communication , 2018, IEEE Transactions on Communications.

[3]  Steve P. Verrill,et al.  Confidence Bounds and Hypothesis Tests for Normal Distribution Coefficients of Variation , 2007 .

[4]  Branka Vucetic,et al.  Burstiness-Aware Bandwidth Reservation for Ultra-Reliable and Low-Latency Communications in Tactile Internet , 2018, IEEE Journal on Selected Areas in Communications.

[5]  Alexander Dekhtyar,et al.  Information Retrieval , 2018, Lecture Notes in Computer Science.

[6]  Walid Saad,et al.  Can Terahertz Provide High-Rate Reliable Low-Latency Communications for Wireless VR? , 2020, IEEE Internet of Things Journal.

[7]  Masahiro Morikura,et al.  Proactive Received Power Prediction Using Machine Learning and Depth Images for mmWave Networks , 2018, IEEE Journal on Selected Areas in Communications.

[8]  Shuangge Ma,et al.  Survival Analysis in Medicine and Genetics , 2013 .

[9]  Theodore S. Rappaport,et al.  Proposal on Millimeter-Wave Channel Modeling for 5G Cellular System , 2016, IEEE Journal of Selected Topics in Signal Processing.

[10]  D. Gleich.  TRUST REGION METHODS , 2017 .

[11]  Özgür Erçetin,et al.  Smart scheduling and feedback allocation over non-stationary wireless channels , 2012, 2012 IEEE International Conference on Communications (ICC).

[12]  Bryan Dodson,et al.  The Weibull Analysis Handbook , 1994 .

[13]  Takayuki Nishio,et al.  Extreme URLLC: Vision, Challenges, and Key Enablers , 2020, ArXiv.

[14]  Lingyang Song,et al.  Deep Learning for Ultra-Reliable and Low-Latency Communications in 6G Networks , 2020, IEEE Network.

[15]  Walid Saad,et al.  A Vision of 6G Wireless Systems: Applications, Trends, Technologies, and Open Research Problems , 2019, IEEE Network.

[16]  Theodore S. Rappaport,et al.  Wireless communications - principles and practice , 1996 .

[17]  Erkki K. Laitinen,et al.  Survival Analysis and Financial Distress Prediction: Finnish Evidence , 2005 .

[18]  H. Vincent Poor,et al.  Experienced Deep Reinforcement Learning With Generative Adversarial Networks (GANs) for Model-Free Ultra Reliable Low Latency Communication , 2019, IEEE Transactions on Communications.

[19]  Lawrence G. Votta,et al.  Analysis of failure and recovery rates in a wireless telecommunications system , 2002, Proceedings International Conference on Dependable Systems and Networks.

[20]  Petar Popovski,et al.  Ultra-reliable communication in 5G wireless systems , 2014, 1st International Conference on 5G for Ubiquitous Connectivity.