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Enhancing 5G network Throughput Using Reinforcement Learning

Enhancing 5G Network Throughput Using Reinforcement Learning

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Communication, Computing and Signal Processing (IICCCS), ieee International conference on

作者： Myasar Mundher Adnan Muydinov Firuzjon Farkhodjonovich Rohit Shrivastava Anika Bhandari AR Aravind Sandeep Kumar S Department Of Computers Techniques Engineering College Of Technical Engineering The Islamic University Najaf Iraq Department Of Computers Techniques Engineering College Of Technical Engineering The Islamic University Of Al Diwaniyah Al Diwaniyah Iraq Head Of Educational and Methodological Department Fergana Medical Institute Of Public Health Fergana Uzbekistan Department of Electronics & Communication Engineering IES College of Technology Bhopal M.P. India Department of Computer Application Chandigarh Engineering College Chandigarh Group of Colleges Mohali Punjab India Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Department of Computer and Communication Engineering NMAM Institute of Technology (NITTE Deemed to be University) Udupi Karnataka India

ISBN: (数字)9798350390759

ISBN: (纸本)9798350390766

In this research, RL is proposed as a solution towards achieving the dynamic optimization of 5G network throughput hindered by the complexity of Intersystems environments. To this extent, the optimization problem is formulated and modeled within the framework of Markov Decision Process and the study resorts to techniques from Deep Reinforcement Learning leading to the design of adaptive algorithms that optimise various network parameters in real-time. It uses simulated raw 5G network data and other data open for public usage for training it and the application itself is coded in Python programming language with TensorFlow for implementing different algorithms. As for the performance, it could be seen that the RL-optimized approach is superior to traditional methods and leads to better system performance in terms of throughput, latency, energy efficiency and user satisfaction in a number of scenarios. The study identifies the possibility to apply RL to enable the learning capability of 5G networks in supporting new services and optimizing the network performance. This research finding can be beneficial for the advancement of 5G wireless communication technology by demonstrating the versatility and advantages of utilizing RL in the network.

关键词： Training 5G mobile communication Wireless networks System performance Signal processing Throughput Complexity theory Telecommunication computing Time factors Optimization

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Assessing the Usefulness of a Visual programming IDE for Large-Scale Automation Software 24

Assessing the Usefulness of a Visual Programming IDE for Lar...

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24th International ACM/ieee conference on Model-Driven Engineering Languages and Systems (MODELS)

作者： Wiesmayr, Bianca Zoitl, Alois Rabiser, Rick Johannes Kepler Univ Linz CDL VaSiCS Linz Austria Johannes Kepler Univ Linz LIT CPS Lab Linz Austria

ISBN: (纸本)9781665434959

Industrial control applications are usually designed by domain experts instead of software engineers. These experts frequently use visual programming languages based on standards such as IEC 61131-3 and IEC 61499. The standards apply model-based engineering concepts to abstract from hardware and lowlevel communication. Developing industrial control software is challenging due the fact that such systems are usually oneof-a-kind systems that have to be maintained for many years. These challenges, together with the growing complexity of control software, require very usable model-based development environments for visual programming languages. However, so far only little empirical research exists on the practical usefulness of such environments, i.e., their usability and utility. In this paper, we discuss common control software maintenance tasks and tool capabilities based on existing research and show the realization of these capabilities in 4diac IDE. We first performed a walkthrough of the demonstrated capabilities using the cognitive dimensions of notations framework from the field of humancomputer interaction. We then improved the tool and conducted a user study involving ten industrial automation engineers, who used 4diac IDE in a realistic control software maintenance scenario. Our findings demonstrate how the usefulness of IDEs can be successfully investigated using a multi-phase approach that includes a walkthrough and a user study. We discuss lessons learned and derive general implications with respect to large-scale applications for developers of IDEs that we deem applicable in the context of (visual) model-based engineering tools.

关键词： Usefulness study open source software IEC 61499 Modeling tools Model-driven engineering

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P4Chain: A Multichain Approach for Real-Time Anomaly Traffic Detection in P4 network

P4Chain: A Multichain Approach for Real-Time Anomaly Traffic...

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ieee Annual Ubiquitous Computing, Electronics & Mobile Communication conference (UEMCON)

作者： Oluwaseyi Ajayi Temidayo Akinyemi Tarek Saadawi Engineering Department Vaughn College of Aeronautics and Engineering NY USA Department of Electrical Engineering City University of New York City College NY USA

programming Protocol-independent Packet Processors (P4) is an open-source domain-specific language to aid the data plane devices in programming packet forwarding. It has a variety of constructs optimized for this purpose. With P4, one can program ASICs, PISA chips, FPGAs, and many network devices since the language constructs allow true independence in some aspects that openFlow could not support. However, there are some challenges facing this technology. The first challenge is that P4 does not account for malicious traffic detection in the data plane pipeline. 2. The controllers have no secure medium of attack signature exchange. This ongoing work presents a multichain solution for detecting malicious traffic and exchanging attack signatures among controllers. This architecture uses an Artificial Immune System (AIS) based Intrusion Detection System (IDS), which runs on a distributed blockchain network, to introspect the P4 data plane to analyze and detect anomaly traffic flows. This IDS resides on the SideChain smart contracts and constantly monitors the traffic flow at the data planes based on introspection. Once malicious traffic is detected on any SideChain, the signatures are extracted and passed through the signature forwarding node to the MainChain for real-time storage. The malicious signatures are sent to all controllers via the mainchain network. We minimize the congestion the solution can cause to the P4 network by utilizing a load balancer to serve the SideChain. To evaluate the performance, we evaluate the False Positive Rate (FPR), Detection Rate (DR), and Accuracy (ACC) of the IDS. We also compute the execution time, performance overhead, and scalability of the proposed solution.

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Flexible network Interface (FNI): A Mission-centric Integration Framework for Next Generation DoD SATCOM networks

Flexible Network Interface (FNI): A Mission-centric Integrat...

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ieee Communications Society (ComSoc)/AFCEA/ieee Military Communications conference (MILCOM)

作者： Vanderpoorten, Joseph Lyke, James Brunnenmeyer, David Zhang, Kevin Fronterhouse, Don Kidder, Joseph Laing, Jeffrey USSF Space Syst Command Los Angeles Air Force Ba CA 90009 USA Air Force Res Labs Wright Patterson AFB OH USA LinQuest Corp Los Angeles CA USA Supinf Technol LLC Rockville MD USA PnP Innovat Inc Albuquerque NM USA 5th Layer LLC Phillipsburg NJ USA

ISBN: (纸本)9781665439565

Our warfighters rely on flexible, resilient, high performance, and global DoD SATCOM networks to operate effectively in a contested, degraded and operationally-limited (CDO) environment. Today, a multitude of DoD networks exist as stovepipes with disparate networking architectures, cybersecurity requirements, management and control tools. We believe that Internet Protocol convergence becomes insufficient to achieve interoperability in the military mission networking context. As the future battlespace involves multiple domain capabilities and assets that must be assembled within mission relevant timelines, operate in unison, and be adaptive to battlespace, a new model is needed to deliver high performance multi-mission and multi-domain data transport. The Flexible network Interface (FNI) is a robust integration framework that deploys enabling technologies to operationalize heterogeneous networks (DoD and commercial). It delivers consistent end user experience, architecture resiliency, and optimized performance;leveraging heterogeneous host networks and commercial managed services across space, air, ground, and sea. This paper provides an overview of the USSF SSC FNI concept, which is being matured through a series of capability prototyping and demonstration initiatives. We describe the FNI framework's design considerations, and its associated architecture instantiations, key functional elements and interfaces. Several important technology requirements are highlighted, which motivate targeted research and development. Finally, the paper summarizes future work to address open issues across technology, standards, implementations, and deployment.

关键词： Space Military Satellite Communications networks MILSATCOM COMSATCOM Command and Control FMI FNI Resiliency Enterprise Management and Control

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A deep learning-based multi-model ensemble method for eye state recognition from EEG 11

A deep learning-based multi-model ensemble method for eye st...

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ieee 11th Annual Computing and Communication Workshop and conference (CCWC)

作者： Islalm, Md Shafiqul Rahman, Md Moklesur Rahman, Md Hafizur Hoque, Md Robiul Roonizi, Arman Kheirati Aktaruzzaman, Md Peoples Univ Bangladesh Dept Comp Sci & Engg Dhaka Bangladesh Islamic Univ Dept Elect & Elect Engg Kushtia Bangladesh Islamic Univ Dept Comp Sci & Engg Kushtia Bangladesh Fasa Univ Dept Comp Sci Fasa Iran

ISBN: (纸本)9781665414906

Eye state recognition plays an important role in biomedical informatics e.g., smart home devices controlling, drowsy driving detection, etc. The change in cognitive states is reflected by the changing in electroencephalogram (EEG) signals. There are some works for eye state recognition using traditional shallow neural networks and manually extracted features. The useful features extraction from EEG and the selection of appropriate classifiers are challenging tasks due to the variable nature of EEG signals. The deep learning algorithms automatically extracts features and often reported better performance than traditional classifiers in some recognition and recognition tasks. In this paper, we have proposed three architectures of a deep learning model using ensemble technique: convolution neural network, gated recurrent unit, and long short term memory for eye state recognition (open or close) from EEG directly. The study has been performed on a freely available public EEG eye state dataset of 14980 samples. The individual performance of each classifier has been observed, and also performance of recognition performance of the ensemble networks has also been compared with the existing prominent methods. The average accuracy 99.86% was obtained by the proposed method, and it is the highest performance ever reported in the literature.

关键词： Eye state recognition EEG Convolutional neural network Ensemble Deep learning

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Tenant-Oriented Resource Optimization for Cloud network Slicing with Performance Guarantees 7

Tenant-Oriented Resource Optimization for Cloud Network Slic...

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ieee 7th International conference on network Softwarization (NetSoft) - Accelerating network Softwarization in the Cognitive Age

作者： Beraldo, Lucian Pentelas, Angelos Verdi, Fabio Luciano Papadimitriou, Panagiotis Marcondes, Cesar A. C. Fed Univ Sao Carlos UFSCar Dept Comp Sci Sao Carlos SP Brazil Univ Macedonia Dept Appl Informat Thessaloniki Greece Aeronaut Inst Technol ITA Comp Sci Div Sao Jose Dos Campos SP Brazil

ISBN: (纸本)9781665405225

Cloud network Slicing (CNS), emerging alongside the 5G mobile network, comprises a paradigm shift in the way networks are provisioned, managed, and operated. Fundamentally, CNS fosters the deployment of a multitude of modern applications, e.g., virtual and augmented reality, 4K video streaming, and autonomous vehicles, which require ultra-low latency, high bandwidth consumption, or both. Slicing promotes the realization of such services through the allocation of computing and network resource bundles, which, as CNS mandates, are isolated from the rest of the network. Typically, such resources are arranged into wide geographical areas (e.g., into multiple countries or even continents), which implies that it is possible to allocate from multiple infrastructure providers. This exacerbates the already challenging problem of maximizing resource allocation efficiency, a feature commonly addressed by CNS architectures. In this respect, we study the optimized embedding of slices across multiple domains. Therefore, we account for slices as a collection of computing and network parts. Given specific resource requirements from slice tenants and potentially multiple offers per slice part, we model the problem as a Mixed Integer Linear Program (MILP). We further design two heuristic algorithms in order to mitigate the complex intricacies that would be perceptible in large problem instances. Our evaluation results, based on a simulation environment aligned with the NECOS slicing architecture, indicate that the MILP approach yields better efficiency c ompared t o b oth h euristics, w ith r espect to client expenditure with a fair amount of performance parameters in an adequate execution time. Our main contribution lies in the optimization methods based on the split and combine approach, integrated into the NECOS CNS architecture.

关键词： Cloud computing network softwarization slicing linear programming optimization

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Runtime Performance Prediction for Deep Learning Models with Graph Neural network

Runtime Performance Prediction for Deep Learning Models with...

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ieee/ACM International conference on Software Engineering: Software Engineering in Practice Track (ICSE-SEIP)

作者： Yanjie Gao Xianyu Gu Hongyu Zhang Haoxiang Lin Mao Yang Microsoft Research Beijing China Tsinghua University Beijing China Chongqing University Chongqing China

Deep learning models have been widely adopted in many application domains. Predicting the runtime performance of deep learning models, such as GPU memory consumption and training time, is important for boosting development productivity and reducing resource waste. The reason is that improper configurations of hyperparameters and neural architectures can result in many failed training jobs or unsatisfactory models. However, the runtime performance prediction of deep learning models is challenging because of the hybrid programming paradigm, complicated hidden factors within the framework runtime, enormous model configuration space, and broad differences among models. In this paper, we propose DNNPerf, a novel ML-based tool for predicting the runtime performance of deep learning models using Graph Neural network. DNNPerf represents a model as a directed acyclic computation graph and incorporates a rich set of performance-related features based on the computational semantics of both nodes and edges. We also propose a new Attention-based Node-Edge Encoder for the node and edge features. DNNPerf is evaluated on thousands of configurations of real-world and synthetic deep learning models to predict their GPU memory consumption and training time. The experimental results show that DNNPerf achieves accurate predictions, with an overall error of 7.4% for the training time prediction and an overall error of 13.7% for the GPU memory consumption prediction, confirming its effectiveness.

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Service and network function placement in the edge-cloud continuum

Service and network function placement in the edge-cloud con...

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ieee conference on Standards for Communications and networking (CSCN)

作者： Dimitris Tsolkas Anastastios-Stavros Charsmiadis Dionysis Xenakis Lazaros Merakos Department of Informatics & Telecommunications NKUA Athens Greece FOGUS Innovations & Services P.C Greece

ISBN: (纸本)9781665476225

The virtualization and containerization technologies have paved the way towards a flexible and fully programmable service provisioning chain over mobile telecommunication infrastructures. On top of those technologies the network slicing concept has emerged; a concept that enables the installation and migration of service and network functions on a compute fabric that spans from the central cloud to distributed far-edge hosts. In this context, the efficient placement of service and network functions in the hosts of the continuum is an open and challenging problem. We formulate this problem as a scheduling process and we analyze its optimal performance through the integer linear programming toolkit. The study revealed interesting insights regarding the impact of sequence constraints (i.e., some functions should be allocated according to a specific order) and host-continuity constraints (i.e., some functions should be allocated in the same cloud host with others) to the overall efficiency of the scheduling.

关键词： Knowledge engineering Cloud computing Processor scheduling network slicing Integer linear programming Linear programming Fabrics

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A Study on 5G Technology and Its Applications in Telecommunications

A Study on 5G Technology and Its Applications in Telecommuni...

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International conference on Computational Performance Evaluation (ComPE)

作者： Deepender Manoj Shrivastava, Utpal Verma, Jitendra Kumar LPU Dept Comp Applicat Phagwara Punjab India GJUS&T Dept Comp Sci & Engn Hisar Haryana India GL Bajaj Grp Inst Dept Comp Sci & Engn Mathura India Indian Inst Foreign Trade IT Discipline New Delhi India

ISBN: (纸本)9781665436564

As the fifth generation of mobile networks climbs above the horizon, this technology's transformational impact and is set to have on the world is commendable. The 5G network is a promising technology that revolutionizes and connects the global world through seamless connectivity. This paper presents a survey on 5G networks on how, in particular, it to address the drawbacks of foregoing cellular standards and be a potential key facilitator for the future as well as the extant technologies such as IoT, V2X, etc. and a comprehensive review related to developing and facilitating technologies with the main focus on 5G mobile networks and technologies incorporated with the network for its enhancement. The challenges and open directions of research pertaining to making 5G applications more reliable for future use are also presented with effective context-specific congestion control tools. Hence, the lessons learned, unsolved challenges, and a consolidated review of 5G are presented.

关键词： 5G cellular network 5G services 5G key technologies 5G architectures 5G challenges

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Multiclass Artificial Neural network based Facial Expression Recognition

Multiclass Artificial Neural Network based Facial Expression...

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Innovation in Technology (INOCON), ieee International conference for

作者： Nazmin Begum A. Syed Mustafa Department of ISE H.K.B.K College of Engineering Visvesvaraya Technological University Karnataka India

Facial emotion recognition has emerged as one of the important research area from past few decades. It has wide variety of applications such as marketing research, medical field, security, criminal interrogations, etc. This paper provides a deep learning approach for face and emotion recognition that may be used to create a system that can accurately identify and recognize people.. Computer vision can find and recognize the human face and its emotions along with the numerous algorithms but to get the best results we are using a deep learning technique. The task is divided into three parts, the first of which is the detection of a human face from a camera, and the second of which is the analysis of the acquired data. Keras convolutional neural network was used to support the features and database used. Human faces are validated in the last phase to categorize human emotions such as Happy; neutral, furious, sad, disgusted, and surprised are all emotions that can be felt ising multiclass artificial neural network approach. Face detection, identification, and emotion categorization are the three main aims of the proposed study. For computer vision approaches, the open CV library, dataset, and python programming are employed in support of this work.

关键词： Deep learning Emotion recognition Technological innovation Runtime Face recognition programming Security

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