2,565 research outputs found
Ultra-Reliable Communication in 5G Wireless Systems
Wireless 5G systems will not only be "4G, but faster". One of the novel
features discussed in relation to 5G is Ultra-Reliable Communication (URC), an
operation mode not present in today's wireless systems. URC refers to provision
of certain level of communication service almost 100 % of the time. Example URC
applications include reliable cloud connectivity, critical connections for
industrial automation and reliable wireless coordination among vehicles. This
paper puts forward a systematic view on URC in 5G wireless systems. It starts
by analyzing the fundamental mechanisms that constitute a wireless connection
and concludes that one of the key steps towards enabling URC is revision of the
methods for encoding control information (metadata) and data. It introduces the
key concept of Reliable Service Composition, where a service is designed to
adapt its requirements to the level of reliability that can be attained. The
problem of URC is analyzed across two different dimensions. The first dimension
is the type of URC problem that is defined based on the time frame used to
measure the reliability of the packet transmission. Two types of URC problems
are identified: long-term URC (URC-L) and short-term URC (URC-S). The second
dimension is represented by the type of reliability impairment that can affect
the communication reliability in a given scenario. The main objective of this
paper is to create the context for defining and solving the new engineering
problems posed by URC in 5G.Comment: To be presented at the 1st International Conference on 5G for
Ubiquitous Connectivit
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
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