7 research outputs found
GANs for EVT Based Model Parameter Estimation in Real-time Ultra-Reliable Communication
The Ultra-Reliable Low-Latency Communications (URLLC) paradigm in
sixth-generation (6G) systems heavily relies on precise channel modeling,
especially when dealing with rare and extreme events within wireless
communication channels. This paper explores a novel methodology integrating
Extreme Value Theory (EVT) and Generative Adversarial Networks (GANs) to
achieve the precise channel modeling in real-time. The proposed approach
harnesses EVT by employing the Generalized Pareto Distribution (GPD) to model
the distribution of extreme events. Subsequently, Generative Adversarial
Networks (GANs) are employed to estimate the parameters of the GPD. In contrast
to conventional GAN configurations that focus on estimating the overall
distribution, the proposed approach involves the incorporation of an additional
block within the GAN structure. This specific augmentation is designed with the
explicit purpose of directly estimating the parameters of the Generalized
Pareto Distribution (GPD). Through extensive simulations across different
sample sizes, the proposed GAN based approach consistently demonstrates
superior adaptability, surpassing Maximum Likelihood Estimation (MLE),
particularly in scenarios with limited sample sizes
A Statistical Learning Approach to Ultra-Reliable Low Latency Communication
Mission-critical applications require Ultra-Reliable Low Latency (URLLC)
wireless connections, where the packet error rate (PER) goes down to .
Fulfillment of the bold reliability figures becomes meaningful only if it can
be related to a statistical model in which the URLLC system operates. However,
this model is generally not known and needs to be learned by sampling the
wireless environment. In this paper we treat this fundamental problem in the
simplest possible communication-theoretic setting: selecting a transmission
rate over a dynamic wireless channel in order to guarantee high transmission
reliability. We introduce a novel statistical framework for design and
assessment of URLLC systems, consisting of three key components: (i) channel
model selection; (ii) learning the model using training; (3) selecting the
transmission rate to satisfy the required reliability. As it is insufficient to
specify the URLLC requirements only through PER, two types of statistical
constraints are introduced, Averaged Reliability (AR) and Probably Correct
Reliability (PCR). The analysis and the evaluations show that adequate model
selection and learning are indispensable for designing consistent physical
layer that asymptotically behaves as if the channel was known perfectly, while
maintaining the reliability requirements in URLLC systems.Comment: Submitted for publicatio