2024/01/10 by Niloofar Mehrnia, Sinem Çöleri, Mehrnia, Niloofar +1
Decision Sciences · Mathematics · #FOS: Computer and information sciences #FOS: Electrical engineering #Information Theory (cs.IT) #Probabilistic and Robust Engineering Design #Signal Processing (eess.SP) #Statistical Distribution Estimation and Applications #Statistical Methods and Inference #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2401.05171
openalex publication_date 2024/01/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Diversity schemes play a vital role in improving the performance of ultra-reliable communication systems by transmitting over two or more communication channels to combat fading and co-channel interference. Determining an appropriate transmission strategy that satisfies ultra-reliability constraint necessitates derivation of statistics of channel in ultra-reliable region and, subsequently, integration of these statistics into 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. First, tail distribution of joint received power sequences obtained from different receivers is modeled while incorporating inter-relations of extreme events occurring rarely based on Poisson point process approach in MEVT. The optimum transmission strategies are then developed by determining optimum transmission rate based on estimated joint tail distribution and incorporating confidence intervals into estimations to cope with the availability of limited data. Finally, system reliability is assessed by utilizing outage probability metric. Through analysis of data obtained from the engine compartment of Fiat Linea, our study showcases the effectiveness of proposed methodology in surpassing traditional extrapolation-based approaches. This innovative method not only achieves a higher transmission rate, but also effectively addresses stringent requirements of ultra-reliability. The findings indicate that 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 conventional rate selection methods.