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.

Incorporation of Confidence Interval into Rate Selection Based on the Extreme Value Theory for Ultra-Reliable Communications

“…where ξ and σ are the shape and scale parameters of the GPD, given that F u (.) is perfectly known and GPD is fitted to the sample sequence including the data in both training and test phases [10]. Then, the lower and upper bounds of the estimated Pareto parameters, as well as the estimated transmission rate are calculated for different values of probability of wrong decision α and at different sample numbers.…”

“…Nevertheless, such approximations characterize the tail statistic based on the extrapolation of the averagestatistics channel models, and therefore, might be deficient to fulfill the reliability targets of ultra-reliability [2]. Only recently, we have proposed a novel EVT-based framework for the estimation and validation of the optimal transmission rate for ultra-reliable communications [10]. However, the possible changes in the transmission rate due to the estimation error have been neglected in the computation.…”

Multivariate Extreme Value Theory Based Channel Modeling for Ultra-Reliable Communications

“…The design of communication systems for ultra-reliable communications has been addressed in the context of adaptive relay selection techniques [15], and rate selection frameworks [6], [16], [17]. A cooperative communication-based relaying scheme has been proposed in [15] to provide ultra-high reliability while meeting the latency requirements at a moderate signal-to-noise ratio (SNR) by modifying the definition of traditional coherence time and revising fading dynamics of wireless channels in the context of ultra-high reliability.…”

“…Moreover, [16] develops a data-driven rate selection framework where federated learning (FL) training has been used to estimate the transmission rate and assess the reliability of the system with minimum training time in the absence of knowledge about the channel state information (CSI). However, all of these designs use the extrapolation of average-statistics channel models, which have been demonstrated not to fit the empirical data in the ultrareliable region [14], [17]- [19]. We have recently proposed a novel extreme value theory (EVT)-based framework in [17] for estimating and validating the optimal transmission rate to address the constraints of ultra-reliable communications for a single transmitter-receiver pair.…”

Multi-Agent Reinforcement Learning Based Joint Resource Allocation for Ultra-Reliable Low-Latency Vehicular Communication

“…In particular, safety-related vehicle applications, such as real-time navigation and platooning, often demand a transmission latency within a few milliseconds and reliability on the order of 99.999% [2]. To achieve such quality-of-service (QoS) requirements for a URLLC network design, it is of utmost importance to develop new statistical methods for wireless channel modeling [3], [4], novel URLLC techniques [5], [6], and resource allocations strategies [7]- [13].…”