Multivariate extreme value theory based rate selection for ultra-reliable communications

Abstract

Diversity schemes play a vital role in improving the performance of ultra-reliable communication (URC) systems by transmitting over two or more communication channels to combat fading and co-channel interference. Determining an appropriate transmission strategy that satisfies the ultra-reliability constraint necessitates the derivation of the statistics of the channel in the ultra-reliable region and, subsequently, integration of these statistics into the rate selection while incorporating a confidence interval to account for potential uncertainties that may arise during estimation. In this paper, we propose a novel framework for ultra-reliable real-time transmission considering both spatial diversities and ultra-reliable channel statistics based on multivariate extreme value theory (MEVT). First, the tail distribution of the joint received power sequences obtained from different receivers is modeled while incorporating the inter-relations of extreme events occurring rarely based on the Poisson point process approach in MEVT. The optimum transmission strategies are then developed by determining the optimum transmission rate based on the estimated joint tail distribution and incorporating confidence intervals (CIs) into the estimations to cope with the availability of limited data. Finally, the system reliability is assessed by utilizing the outage probability metric. Through analysis of the data obtained from the engine compartment of the Fiat Linea, our study showcases the effectiveness of the proposed methodology in surpassing traditional extrapolation-based approaches. This innovative method not only achieves a higher transmission rate, but also effectively addresses the stringent requirements of ultra-reliability. The findings indicate that the proposed rate selection framework offers a viable solution for achieving a desired target error probability by employing a higher transmission rate and reducing the amount of training data compared to the conventional rate selection methods. Authors