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IEEE Global Communications Conference

作者： Xi Zhang Jingqing Wang H. Vincent Poor Department of Electrical and Computer Engineering Networking and Information Systems Laboratory Texas A&M University College Station TX USA Department of Electrical Engineering Princeton University Princeton NJ USA

ISBN: (数字)9781728109626

ISBN: (纸本)9781728109633

Non-orthogonal multiple access (NOMA) has been designed to significantly enhance spectral efficiency for massive connections of mobile devices. Moreover, to guarantee the short latency requirements for 5G multimedia wireless services, researchers have designed statistical delay-bounded quality-of-service (QoS) provisioning for supporting video transmissions over statistically varying wireless channels. Accordingly, researchers have proposed finite blocklength coding (FBC) techniques to model the relationship between data transmission rate and channel capacity in the non-asymptotic regime while supporting short-packet communications under QoS constraints. Due to its potential to significantly improve spectral efficiency and reduce the transmission latency, a NOMA system can be exploited while being integrated with FBC to guarantee the QoS requirements for both latency and reliability over mobile wireless ad-hoc networks. However, because of the complexity of the effective-capacity maximization problem in the non-asymptotic regime, the FBC based NOMA scheme imposes new challenges for determining the convexity of the optimization problem and deriving optimal resource allocation policies for NOMA subject to statistical delay-bounded and error-rate bounded QoS constraints. In order to solve the above-mentioned problems, we define a new concept of ε-effective capacity and propose a corresponding system architecture model for NOMA systems under FBC. In particular, we characterize the FBC based NOMA system models in wireless ad hoc networks. Considering the statistical delay-bounded and error-rate bounded QoS constraints, we formulate and solve the max-min fairness problem under FBC. Simulation results are included, which evaluate and validate our proposed FBC based NOMA scheme subject to statistical delay-bounded and error-rate bounded QoS constraints.

关键词： NOMA Quality of service Ad hoc networks Error probability Wireless communication Delays Interference

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Scaling Law Based D2D Wireless Ad-Hoc Networks in the Finite Blocklength Regime

Scaling Law Based D2D Wireless Ad-Hoc Networks in the Finite...

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IEEE Global Communications Conference

作者： Xi Zhang Qixuan Zhu H. Vincent Poor Department of Electrical and Computer Engineering Networking and Information Systems Laboratory Texas A&M University College Station TX USA Department of Electrical Engineering Princeton University Princeton NJ USA

ISBN: (数字)9781728109626

ISBN: (纸本)9781728109633

A device-to-device (D2D) wireless ad hoc network architecture enables dynamic self-organizing communications among mobile users who can directly exchange information with their peers without a pre-determined network infrastructure. Moreover, finite blocklength coding (FBC) is the promising candidate technique to support time sensitive multimedia wireless networks services, where mobile users transmit short packets to upper-bound the transmission delay of video/audio traffic. The scaling law technique models the maximum D2D channel capacity as a function of the density of mobile users. Recent studies have integrated D2D wireless ad hoc networks with FBC theory to further improve the performance of 5G wireless ad hoc networks. However, how to model and analyze the capacity of D2D wireless ad hoc networks under the finite blocklength regime is not well understood and has not been thoroughly studied. To overcome these challenges, applying the scaling law technique, we derive upper-bounds on the coding rate of each D2D channel and the number of time slots needed to complete all D2D transmissions. Combining the D2D channel's coding rate with the number of time slots needed for all D2D transmissions, we derive the maximum aggregate throughput for wireless ad hoc networks with all mobile users using D2D communications while mitigating interference. We also develop a model where each D2D channel follows the Nakagami-m distribution, under which we derive the average aggregate throughput and its upper-bound. Finally, we evaluate our derived results in the D2D wireless ad hoc networks over finite blocklength regime through numerical analyses.

关键词： Device-to-device communication Mobile ad hoc networks Encoding Throughput Wireless communication Aggregates

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Minimum-Energy and Error-Rate for URLLC Networks Over Nakagami-m Channels: A Finite-Blocklength Analysis

Minimum-Energy and Error-Rate for URLLC Networks Over Nakaga...

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IEEE Global Communications Conference

ISBN: (数字)9781728109626

ISBN: (纸本)9781728109633

The fifth generation (5G) wireless networks aim at providing a wide range of time-sensitive multimedia services and applications to satisfy users' stringent requirements on delay-bounded quality of service (QoS). One of the important requirements for 5G multimedia wireless networks is to efficiently support the ultra-reliable and low-latency communications (URLLC). Finite blocklength coding (FBC) is a promising candidate technique to support URLLC services, where mobile users transmit short packets to upper-bound the transmission delay of multimedia traffic. In this paper, we model and analyze the packet-block error rate for URLLC under the Nakagami-m fading channel in the finite blocklength regime. Then, we derive the minimum energy per bit for data transmissions in the Nakagami-m fading channel under the constraint of block error rate. To derive closed-form expressions for the minimum energy per bit, we obtain achievability and converse bounds on the minimum energy per bit, and show that these two bounds are equal to each other. Finally, we evaluate our derived minimum energy per bit in the finite blocklength regime through numerical analyses, and compare it with those under the Rayleigh fading channel and the wideband approximation, validating our derived analytical results.

关键词： Fading channels Error analysis Decoding Wireless networks Encoding Antennas 5G mobile communication

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CNN-based Broad Learning with Efficient Incremental Reconstruction Model for Facial Emotion Recognition ⁎

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IFAC-PapersOnLine 2020年第2期53卷 10236-10241页

作者： Luefeng Chen Min Li Xuzhi Lai Kaoru Hirota Witold Pedrycz School of Automation China University of Geosciences Wuhan 430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan 430074 China Tokyo Institute of Technology Yokohama 226-8502 Japan Department of Electrical and Computer Engineering University of Alberta Edmonton AB T6R 2G7 Canada

Convolutional neural network-based broad learning with efficient incremental reconstruction model (CNNBL) is proposed to recognize emotions in human-robot interaction. It aims to extract deep and abstract features from facial emotional images, and reduce the influence of the complex structure and slow network updates on facial emotion recognition in deep learning. Feature extraction is carried out by convolution and maximum pooling, and then the ridge regression algorithm is used for emotion recognition. When the network needs to expand, the network is dynamically updated by incremental learning algorithm. We verified the experimental performance through k -fold cross validation. According to the recognition results, the accuracy on JAFFE database of our proposal is greater than that of the state of the art, such as the Local Binary Patterns with Softmax and Deep Attentive Multi-path convolutional neural network.

关键词： Convolution Neural Networks Broad Learning Emotion Recognition Human-Robot Interaction

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Hybrid Robust Optimal Resource Allocation with Momentum

Hybrid Robust Optimal Resource Allocation with Momentum

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IEEE Annual Conference on Decision and Control

作者： Daniel E. Ochoa Jorge I. Poveda Cesar A. Uribe Nicanor Quijano Department of Electrical Computer and Energy Engineering at the University of Colorado Boulder Laboratory for Information and Decision Systems Massachusetts Institute of Technology Department of Electronics and Electrical Engineering University of Los Andes Bogota Colombia

ISBN: (数字)9781728113982

ISBN: (纸本)9781728113999

We study the problem of robust resource allocation with momentum following a dynamical systems point of view. Motivated by a class of existing optimization dynamics with no momentum defined on the general m-simplex, we propose a class of time-varying differential equations with momentum that achieve acceleration and preserve most of the asymptotic properties of its time-invariant counterpart. Since time-varying dynamics with momentum in continuous-time usually lack of structural robustness properties, we present a hybrid regularization that induces the property of uniform asymptotic stability in the system. We show this by using the invariance principle for well-posed hybrid dynamical systems, and we establish the existence of strictly positive margins of robustness with respect to arbitrarily small disturbances. We illustrate our results via numerical simulations.

关键词： Robustness Acceleration Resource management Asymptotic stability Mathematical model Cost function

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Reinforcement Learning Based QoS-Provisioning Over Energy-Harvesting 5G Wireless Ad-Hoc Networks

Reinforcement Learning Based QoS-Provisioning Over Energy-Ha...

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IEEE Global Communications Conference

ISBN: (数字)9781728109626

ISBN: (纸本)9781728109633

To relieve the energy scarcity problem introduced by the explosively growing number of wireless multimedia services, researchers have proposed and developed the energy harvesting (EH) technique over 5G wireless ad-hoc networks where the mobile devices can harvest energy from various external sources. Moreover, statistical delay-bounded quality-of-service (QoS) provisioning has been developed and extensively studied for guaranteeing wireless multimedia data transmissions over 5G ad-hoc networks. However, because of the stochastic nature of EH and data arrival processes, it is challenging to design an optimal EH policy as well as resource allocation policies to guarantee statistical delay-bounded QoS requirements. Along this direction, the reinforcement learning algorithms have been designed to select optimal EH and resource allocation policies by allowing the mobile users to learn from the different network states and historical behavior until an optimal response set is reached. By leveraging the benefits of reinforcement learning, in this paper we propose a reinforcement learning based algorithm for designing optimal EH and resource allocation policies while satisfying heterogeneous statistical delay-bounded QoS constraints over 5G wireless ad-hoc networks. In particular, we first establish system models for EH and wireless data transmissions. Then, we formulate the effective-capacity maximization problem to find optimal power-subchannel allocation policies over EH based 5G wireless ad-hoc networks. To solve such a maximization problem, we apply a reinforcement learning based EH algorithm for deriving an optimal EH policy and a corresponding optimal resource allocation policies. We also conduct a set of performance evaluations to show that our proposed reinforcement learning based EH scheme outperforms existing schemes subject to the heterogeneous statistical delay-bounded QoS constraints over 5G wireless ad-hoc networks.

关键词： Ad hoc networks Wireless communication Quality of service 5G mobile communication Resource management Wireless fidelity Learning (artificial intelligence)

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Optimal QoS-Driven Power Allocation for Energy Harvesting Wireless Ad-Hoc Networks Using FBC

Optimal QoS-Driven Power Allocation for Energy Harvesting Wi...

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IEEE Global Communications Conference

ISBN: (数字)9781728109626

ISBN: (纸本)9781728109633

In the last few decades, the statistical delay-bounded quality-of-service (QoS) technique has been proposed and investigated to support delay-bounded 5G multimedia wireless services In addition, energy harvesting (EH) based systems, which enable mobile devices to harvest energy from various external sources, have been designed to address the energy scarcity problem. Researchers have recently integrated EH based systems with short-packet communications for their potential benefits of small payloads and low latency to support reliable multimedia data transmissions. Under the short delay requirements for the low-latency 5G multimedia wireless services, it is not ideal to apply the traditional Shannon capacity for modeling the data rate under the error-rate bounded in the non-asymptotic regime. Accordingly, researchers have developed the finite wireless data transmission rate in the non-asymptotic regime. However, it is challenging to determine the convexity of the effective-capacity maximization problem subject to the statistical delay-bounded and error-rate bounded QoS constraints due to the complexity of analyzing the maximization problem in the non-asymptotic regime. To overcome these challenges, we design the EH based cross-layer optimization scheme in supporting the statistical delay-bounded and error-rate bounded QoS requirements in the finite blocklength regime in the following steps. First, we characterize the EH based system models under finite blocklength coding (FBC). Second, we derive and analyze the convexity of the e-effective capacity maximization problem for our proposed EH scheme in the finite blocklength regime. Finally, we conduct several simulation evaluations which validate our proposed EH scheme under the statistical delay-bounded and error-rate bounded QoS constraints in the finite blocklength regime.

关键词： Quality of service Ad hoc networks Wireless communication Data communication Error probability Energy harvesting 5G mobile communication

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Time Series Forecasting of New Cases and New Deaths Rate for COVID-19 using Deep Learning Methods

arXiv

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arXiv 2021年

作者： Ayoobi, Nooshin Sharifrazi, Danial Alizadehsani, Roohallah Shoeibi, Afshin Gorriz, Juan M. Moosaei, Hossein Khosravi, Abbas Nahavandi, Saeid Chofreh, Abdoulmohammad Gholamzadeh Goni, Feybi Ariani Klemeš, Jiří Jaromír Mosavi, Amir Department of Mathematics Savitribai Phule Pune University Pune411007 India Department of Computer Engineering School of Technical and Engineering Shiraz Branch Islamic Azad University Shiraz Iran Institute for Intelligent Systems Research and Innovation Deakin University GeelongVIC3216 Australia Computer Engineering Department Ferdowsi University of Mashhad Mashhad Iran Faculty of Electrical and Computer Engineering Biomedical Data Acquisition Lab K. N. Toosi University of Technology Tehran Iran Department of Signal Theory Networking and Communications Universidad de Granada Spain Department of Mathematics Faculty of Science University of Bojnord Iran Sustainable Process Integration Laboratory - SPIL NETME Centre Faculty of Mechanical Engineering Brno University of Technology - VUT Brno Technická 2896/2 Brno616 69 Czech Republic Department of Management Faculty of Business and Management Brno University of Technology VUT Brno Kolejní 2906/4 Brno612 00 Czech Republic Faculty of Civil Engineering Technische Universität Dresden Dresden01069 Germany John von Neumann Faculty of Informatics Obuda University Budapest1034 Hungary School of Economics and Business Norwegian University of Life Sciences 1430 Norway

The first known case of Coronavirus disease 2019 (COVID-19) was identified in December 2019. It has spread worldwide, leading to an ongoing pandemic, imposed restrictions and costs to many countries. Predicting the number of new cases and deaths during this period can be a useful step in predicting the costs and facilities required in the future. The purpose of this study is to predict new cases and deaths rate one, three and seven-day ahead during the next 100 days. The motivation for predicting every n days (instead of just every day) is the investigation of the possibility of computational cost reduction and still achieving reasonable performance. Such a scenario may be encountered real-time forecasting of time series. Six different deep learning methods are examined on the data adopted from the WHO website. Three methods are LSTM, Convolutional LSTM, and GRU. The bidirectional extension is then considered for each method to forecast the rate of new cases and new deaths in Australia and Iran countries. This study is novel as it carries out a comprehensive evaluation of the aforementioned three deep learning methods and their bidirectional extensions to perform prediction on COVID-19 new cases and new death rate time series. To the best of our knowledge, this is the first time that Bi-GRU and Bi-Conv-LSTM models are used for prediction on COVID-19 new cases and new deaths time series. The evaluation of the methods is presented in the form of graphs and Friedman statistical test. The results show that the bidirectional models have lower errors than other models. Several error evaluation metrics are presented to compare all models, and finally, the superiority of bidirectional methods is determined. This research could be useful for organizations working against COVID-19 and determining their long-term plans. Copyright © 2021, The Authors. All rights reserved.

关键词： Coronavirus

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Neyman-Pearson Criterion Based Optimal Hierarchical Caching Over D2D Wireless Ad-Hoc Networks

Neyman-Pearson Criterion Based Optimal Hierarchical Caching ...

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IEEE Global Communications Conference

ISBN: (数字)9781728109626

ISBN: (纸本)9781728109633

Caching in mobile devices is a new paradigm to revolutionize the traditional data content (i.e., files) sharing methods in wireless cellular networks. By caching popular data content items in mobile devices and disseminating them through device-to-device (D2D) communications, the wireless cellular network can improve its area spectral efficiency, save the base station bandwidth consumption, and reduce the transmission delay of downloading. Caching is closely related to data popularity prediction, but how to accurately predict the future popularity profile has not been well understood. Moreover, although the hierarchical caching architecture has been shown to yield more benefits than flat caching strategies, the challenge of designing an optimal hierarchical caching scheme has not been thoroughly addressed. In this paper, we use a Neyman-Pearson hypothesis testing mechanism to predict the future data popularity, and also develop optimal hierarchical caching schemes over D2D wireless ad-hoc networks. The key of Neyman-Pearson hypothesis testing in our proposed scheme is to derive a closed form expression for the decision threshold, which is a function of costs of choosing the incorrect hypothesis. We formulate these costs as sums of time durations for content placement and content delivery phases. We derive closed form expressions for these two phases respectively and obtain a closed form of an optimal decision rule, which maximizes the cache hitting probability and upper-bounds the prediction error probability. Finally, we evaluate and validate our proposed Neyman-Pearson hypothesis testing based hierarchical caching schemes through numerical analyses.

关键词： Testing Ad hoc networks Mobile handsets Device-to-device communication Wireless communication Delays Error probability

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mmWave-MIMO Based 5G Wireless Ad-Hoc Networks in the Finite Blocklength Regime

mmWave-MIMO Based 5G Wireless Ad-Hoc Networks in the Finite ...

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2019 IEEE Global Communications Conference (GLOBECOM)

ISBN: (数字)9781728109626

ISBN: (纸本)9781728109633

The integration of millimeter wave (mmWave) and multiple-input and multiple-output (MIMO) techniques has been designed to provide reliable communications with large degrees of freedom while supporting the explosively growing number of mobile users. Under stringent requirements in terms of latency and reliability, due to the infinite blocklength assumption of the Shannon's capacity result, researchers have investigated new methods to characterize wireless data transmissions considering the block error probability. The finite blocklength coding (FBC) technique has been developed to model the finite blocklength coding rate in the non-asymptotic regime while supporting short-packet communications over 5G wireless ad-hoc networks. However, because of the design complexity when characterizing the second-order coding rate over mmWave MIMO based wireless channels while being integrated with FBC, how to accurately derive the finite blocklength coding rate over mmWave MIMO wireless fading channels is still an open problem over 5G wireless ad-hoc networks. To tackle the above-mentioned challenges, we propose and develop a system model that can efficiently integrate mmWave-MIMO techniques with finite blocklength coding over 5G wireless ad-hoc networks. In particular, we derive system equations that characterize the foundational informationtheoretic relationship between the finite blocklength channel capacity and the coding rate over our proposed mmWave MIMO based 5G wireless ad-hoc networks in the finite blocklength regime. Also conducted is a MATLAB-based performance evaluation, which validates and analyzes our proposed schemes over mmWave MIMO based 5G wireless ad-hoc networks in the finite blocklength regime.

关键词： Ad hoc networks Wireless communication MIMO communication 5G mobile communication Channel coding Mathematical model

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