837 research outputs found
Towards Deterministic Communications in 6G Networks: State of the Art, Open Challenges and the Way Forward
Over the last decade, society and industries are undergoing rapid
digitization that is expected to lead to the evolution of the cyber-physical
continuum. End-to-end deterministic communications infrastructure is the
essential glue that will bridge the digital and physical worlds of the
continuum. We describe the state of the art and open challenges with respect to
contemporary deterministic communications and compute technologies: 3GPP 5G,
IEEE Time-Sensitive Networking, IETF DetNet, OPC UA as well as edge computing.
While these technologies represent significant technological advancements
towards networking Cyber-Physical Systems (CPS), we argue in this paper that
they rather represent a first generation of systems which are still limited in
different dimensions. In contrast, realizing future deterministic communication
systems requires, firstly, seamless convergence between these technologies and,
secondly, scalability to support heterogeneous (time-varying requirements)
arising from diverse CPS applications. In addition, future deterministic
communication networks will have to provide such characteristics end-to-end,
which for CPS refers to the entire communication and computation loop, from
sensors to actuators. In this paper, we discuss the state of the art regarding
the main challenges towards these goals: predictability, end-to-end technology
integration, end-to-end security, and scalable vertical application
interfacing. We then present our vision regarding viable approaches and
technological enablers to overcome these four central challenges. Key
approaches to leverage in that regard are 6G system evolutions, wireless
friendly integration of 6G into TSN and DetNet, novel end-to-end security
approaches, efficient edge-cloud integrations, data-driven approaches for
stochastic characterization and prediction, as well as leveraging digital twins
towards system awareness.Comment: 22 pages, 8 figure
Learning-based hybrid TDMA-CSMA MAC protocol for virtualized 802.11 WLANs
This paper presents an adaptive hybrid TDMA-CSMA MAC protocol to improve network performance and isolation among service providers (SPs) in a virtualized 802.11 network. Aiming to increase network efficiency, wireless virtual-ization provides the means to slice available resources among different SPs, with an urge to keep different slices isolated. Hybrid TDMA-CSMA can be a proper MAC candidate in such scenario benefiting from both the TDMA isolation power and the CSMA opportunistic nature. In this paper, we propose a dynamic MAC that schedules high-traffic users in the TDMA phase with variable size to be determined. Then, the rest of active users compete to access the channel through CSMA. The objective is to search for a scheduling that maximizes the expected sum throughput subject to SP reservations. In the absence of arrival traffic statistics, this scheduling is modeled as a multi-armed bandit (MAB) problem, in which each arm corresponds to a possible scheduling. Due to the dependency between the arms, existing policies are not directly applicable in this problem. Thus, we present an index-based policy where we update and decide based on learning indexes assigned to each user instead of each arm. To update the indexes, in addition to TDMA information, observations from CSMA phase are used, which adds a new exploration phase for the proposed MAB problem. Throughput and isolation performance of the proposed self-exploration-aided index-based policy (SIP) are evaluated by numerical results
vrAIn: a deep learning approach tailoring computing and radio resources in virtualized RANs
Proceeding of: 25th Annual International Conference on Mobile Computing and Networking (MobiCom'19), October 21-25, 2019, Los Cabos, Mexico.The virtualization of radio access networks (vRAN) is the
last milestone in the NFV revolution. However, the complex
dependencies between computing and radio resources make
vRAN resource control particularly daunting. We present
vrAIn, a dynamic resource controller for vRANs based on
deep reinforcement learning. First, we use an autoencoder
to project high-dimensional context data (traffic and signal
quality patterns) into a latent representation. Then, we use a
deep deterministic policy gradient (DDPG) algorithm based
on an actor-critic neural network structure and a classifier
to map (encoded) contexts into resource control decisions.
We have implemented vrAIn using an open-source LTE
stack over different platforms. Our results show that vrAIn
successfully derives appropriate compute and radio control
actions irrespective of the platform and context: (i) it provides
savings in computational capacity of up to 30% over
CPU-unaware methods; (ii) it improves the probability of
meeting QoS targets by 25% over static allocation policies
using similar CPU resources in average; (iii) upon CPU capacity
shortage, it improves throughput performance by 25%
over state-of-the-art schemes; and (iv) it performs close to optimal
policies resulting from an offline oracle. To the best of
our knowledge, this is the first work that thoroughly studies
the computational behavior of vRANs, and the first approach
to a model-free solution that does not need to assume any
particular vRAN platform or system conditions.The work of
University Carlos III of Madrid was supported by H2020 5GMoNArch
project (grant agreement no. 761445) and H2020
5G-TOURS project (grant agreement no. 856950). The work
of NEC Laboratories Europe was supported by H2020 5GTRANSFORMER
project (grant agreement no. 761536) and
5GROWTH project (grant agreement no. 856709). The work
of University of Cartagena was supported by Grant AEI/FEDER
TEC2016-76465-C2-1-R (AIM) and Grant FPU14/03701.Publicad
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