Wireless channel modeling based on extreme values theory for ultra-reliable low latency communications

Ultra-reliable low latency communication (URLLC) is one of the most important features of the fifth generation (5G) networks with the aim of supporting mission critical applications, such as remote control of robots, remote surgery, autonomous vehicles and vehicular teleoperation applications. At URLLC, the packet error rate (PER) is guaranteed to be as low as 10−9-10−5 to address the strict reliability constraint, while the acceptable latency is on the order of a few milliseconds. A key building block in the design of ultra-reliable communication systems is a wireless channel model that captures the statistics of rare events occurring due to significant fading. Extreme value theory (EVT) is a powerful framework that characterizes the probabilistic distribution of infrequent extreme events or equivalently the tail distribution. In this thesis, we propose a novel methodology based on EVT to statistically model the behavior of extreme events in a wireless channel for ultra-reliable communication. In the first part of the thesis, we initially include techniques based on EVT for fitting the lower tail distribution of the received power to the generalized Pareto distribution (GPD), determining the optimum threshold over which the tail statistics are derived, ascertaining the optimum stopping condition on the number of samples required to estimate the tail statistics by using GPD, and finally, assessing the validity of the derived Pareto model. Second, we model the tail distribution of non-stationary channel based on EVT by including techniques for splitting the channel data sequence into multiple groups concerning the environmental factors causing non-stationarity, and fitting the lower tail distribution of the received power in each group to the GPD. The proposed approach also consists of optimally determining the time-varying threshold over which the tail statistics are derived as a function of time, and assessing the validity of the derived Pareto model. Third, we propose EVT-based framework dealing with relatively low number of data samples to estimate the optimal transmission rate and validate it by assessing the outage probability so that reliability constraints are met with a given confidence for ultra-reliable communications. Fourth, we propose a novel channel modeling methodology based on multivariate EVT (MEVT) for a system using spatial diversity in multiple input multiple output (MIMO)-URLLC to derive the lower tail statistics of the received signal power in multiple dimensions while efficiently dealing with a massive amount of corresponding data. Accordingly, we adopt EVT to determine the optimum threshold over which the tail statistics are derived by using the UGPD model, validate the final model by using probability plots, and utilize MEVT to model the tail of the joint probability distribution by using the EVT-based logistic distribution and Poisson point process approaches. In the second part of the thesis, we address the requirement of ultra-reliability at the upper communication level by proposing a new algorithm based on the prior collection of data to eliminate power-consuming beam-tracking techniques while ensuring high received power with minimum diversity level.