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IEEE Networking Letters 2024年第3期6卷 168-172页

作者： Politis, Anastasios C. Hilas, Constantinos S. Department of Computer Informatics and Telecommunications Engineering Serres621 24 Greece

This letter evaluates the performance of the slotted Aloha protocol defined by the European Telecommunication Standard Institute (ETSI) SmartBAN specification, under saturation conditions. For this purpose, we develop a two-dimensional discrete time Markov chain (DTMC) to model the operational details of the protocol and assess its performance in terms of saturation throughput and average end-to-end delay. The accuracy of the proposed model is validated by means of simulation which reveals a very good match among theoretical and simulation results. The model can be used for protocol performance prediction and optimization purposes. © 2019 IEEE.

关键词： Markov chains

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Solving differential equations with global optimisation techniques

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International Journal of Computational Intelligence Studies 2024年第1-2期13卷 1-22页

作者： Tsoulos, Ioannis G. Tzallas, Alexandros Tsalikakis, Dimitrios Department of Informatics and Telecommunications University of Ioannina Arta47100 Greece Department of Engineering Informatics and Telecommunications University of Western Macedonia Kozani50100 Greece

The solution of differential equations finds many applications in a huge range of problems, and many techniques have been developed to approximate their solutions. For example, differential equations can be applied to physics problems, chemistry problems, economics, modelling, etc. This manuscript presents a number of global optimisation techniques that have been successfully applied to train machine learning models to approximate differential equation solutions. More specifically, two modified versions of genetic algorithms and particle swarm optimisation methods are proposed here. These methods have been successfully applied to solving ordinary differential equations and systems of differential equations as well as partial differential equations with Dirichlet boundary conditions. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Particle swarm optimization (PSO)

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A low-complexity AMP detection algorithm with deep neural network for massive mimo systems

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Digital Communications and Networks 2024年第5期10卷 1375-1386页

作者： Zufan Zhang Yang Li Xiaoqin Yan Zonghua Ouyang School of Communication and Information Engineering Chongqing University of Posts and TelecommunicationsChongqing400065China International College Chongqing University of Posts and TelecommunicationsChongqing400065China College of Engineering Informaticsand Applied SciencesNorthern Arizona UniversityArizonaAZ86011USA

Signal detection plays an essential role in massive Multiple-Input Multiple-Output(MIMO)***,existing detection methods have not yet made a good tradeoff between Bit Error Rate(BER)and computational complexity,resulting in slow convergence or high *** address this issue,a low-complexity Approximate Message Passing(AMP)detection algorithm with Deep Neural Network(DNN)(denoted as AMP-DNN)is investigated in this ***,an efficient AMP detection algorithm is derived by scalarizing the simplification of Belief Propagation(BP)***,by unfolding the obtained AMP detection algorithm,a DNN is specifically designed for the optimal performance *** the proposed AMP-DNN,the number of trainable parameters is only related to that of layers,regardless of modulation scheme,antenna number and matrix calculation,thus facilitating fast and stable training of the *** addition,the AMP-DNN can detect different channels under the same distribution with only one *** superior performance of the AMP-DNN is also verified by theoretical analysis and *** is found that the proposed algorithm enables the reduction of BER without signal prior information,especially in the spatially correlated channel,and has a lower computational complexity compared with existing state-of-the-art methods.

关键词： Massive MIMO system Approximate message passing(AMP)detection algorithm Deep neural network(DNN) Bit error rate(BER) Low-complexity

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Comparative Analysis of 5G New Radio (NR) Network Planning on Low-, Mid-, and High-Band in the Bandung City Area 10

Comparative Analysis of 5G New Radio (NR) Network Planning o...

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10th International Conference on Wireless and Telematics, ICWT 2024

作者： Immanuel, David A. Iskandar School of Electrical Engineering and Informatics Bandung Institute of Technology Telecommunications Engineering Bandung Indonesia

ISBN: (纸本)9798350377057

In this research, 5G New Radio (NR) network planning is conducted using the coverage planning method in a variety of scenarios with varying frequency bands, including the low-band (700 MHz), mid-band (2.3 GHz), and high-band (26 GHz) with channel bandwidths of 10 MHz, 100 MHz, and 800 MHz, respectively. This 5G NR network planning was conducted by simulating the Atoll 3.4 software in the Bandung City area with an area of 167.31 square kilometers. The process carried out in the planning coverage in the form of a Link Budget to determine the Maximum Allowable Path Loss (MAPL) and identify the number of gNodeB sites required for each scenario. The propagation model employed in this 5G NR planning is UMa (Urban Macro) with the limitation of each scenario carried out only in Downlink Outdoor-to-Outdoor (O2O) conditions. The results of the parameters analyzed in this study are presented in terms of Coverage including SS-RSRP, and SS-SINR, and in terms of Capacity including Data Rate (Cell Capacity). The observation results of SS-RSRP mean from highest to lowest, are as follows: at low-band is -62.98 dBm, at high-band is -65.47 dBm, and at mid-band is -69.24 dBm. The observation results of SS-SINR mean from lowest to highest, are as follows: at low-band is 22.04 dB, at mid-band is 24.3 dB, and at high-band is 31.06 dB. The observation results of Cell Capacity mean from lowest to highest, are as follows: at low-band is 71.645 Mbps, at mid-band is 178.712 Mbps, and at high-band is 1139.837 Mbps. © 2024 IEEE.

关键词： Budget control

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Designing a skilled soccer team for RoboCup: exploring skill-set-primitives through reinforcement learning

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Neural Computing and Applications 2025年 1-36页

作者： Abreu, Miguel Reis, Luís Paulo Lau, Nuno LIACC/LASI/FEUP Artificial Intelligence and Computer Science Laboratory Faculty of Engineering University of Porto Porto Portugal IEETA/LASI/DETI Institute of Electronics and Informatics Engineering of Aveiro Department of Electronics Telecommunications and Informatics University of Aveiro Aveiro Portugal

The RoboCup 3D soccer simulation league serves as a competitive platform for showcasing innovation in autonomous humanoid robot agents through simulated soccer matches. Our team, FC Portugal, developed a new codebase from scratch in Python after RoboCup 2021. The team’s performance relies on a set of skills centered around novel unifying primitives and a custom, symmetry-extended version of the proximal policy optimization algorithm. Our methods have been thoroughly tested in official RoboCup matches, where FC Portugal has won the last two main competitions, in 2022 and 2023. This paper presents our training framework, as well as a timeline of skills developed using our skill-set-primitives, which considerably improve the sample efficiency and stability of skills, and motivate seamless transitions. We start with a significantly fast Sprint-Kick developed in 2021 and progress to the most recent skill set, including a multi-purpose omnidirectional walk, a dribble with unprecedented ball control, a solid kick, and a push skill. The push addresses low-level collision scenarios and high-level strategies to increase ball possession. We address the resource-intensive nature of this task through an innovative multi-agent learning approach. Finally, we release the team’s codebase to the RoboCup community, providing other teams with a robust and modern foundation upon which they can build new features. © The Author(s) 2025.

关键词： Reinforcement learning

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Addressing Scalability Issues of Blockchains with Hypergraph Payment Networks

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IEEE Transactions on Network and Service Management 2025年第3期22卷 2427-2440页

作者： Kotzer, Arad Ladoczki, Bence Tapolcai, Janos Rottenstreich, Ori Technion -Israel Institute of Technology Haifa Israel Budapest University of Technology and Economics Department of Telecommunications and Artificial Intelligence Faculty of Electrical Engineering and Informatics Hungary

Payment channels are auspicious candidates in layer-2 solutions to reduce the number of on-chain transactions on traditional blockchains and increase transaction throughput. To construct payment channels, peers lock funds on 2-of-2 multisig addresses and open channels between one another to transact via instant peer-to-peer transactions. Transactions between peers without a direct channel are made possible by routing the payment over a series of adjacent channels. In certain cases, this can lead to relatively low transaction success rates and high transaction fees. In this work, we introduce pliability to constructing payment channels and graft edges with more than two endpoints into the payment graph. We refer to these constructions as hyperedges. We present hyperedge-based topologies to form hypergraphs and compare them to Bitcoin's Lightning network and other state-of-the-art solutions. The results demonstrate that hyperedge-based implementations can both increase transaction success rate, in addition to decreasing the network cost by more than 50% compared to that of the Lightning Network. © 2004-2012 IEEE.

关键词： Blockchain

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Recognition of Dog Motion States: Ensemble vs Deep Learning Models 15

Recognition of Dog Motion States: Ensemble vs Deep Learning ...

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15th International Conference on Information, Intelligence, Systems and Applications, IISA 2024

作者： Davoulos, George Lalakou, Iro Hatzilygeroudis, Ioannis University of Patras Computer Engineering and Informatics Department Patras Greece University of Peloponnese Department of Informatics and Telecommunications Tripolis Greece

ISBN: (纸本)9798350368833

Dog activities recognition, especially dog motion status recognition, is an active research area. Although several machine learning and deep learning approaches have been used for dog motion states recognition, use of ensemble learning methods is rather missing as well as a comparison with deep learning ones. This paper focuses on the use of ensemble classifiers and deep learning neural networks in recognizing dog motion states, and their comparison. Our results show a slight superiority in accuracy (94.7% vs 92.8%) of deep learning, which is quite comparable to the state-of-the-art, but at the cost of larger complexity and training time, which makes ensemble techniques still attractive. © 2024 IEEE.

关键词： Contrastive Learning

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CNN Architectures for Vehicle Detection in V2X Environments: A Comparative Study

CNN Architectures for Vehicle Detection in V2X Environments:...

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2024 IEEE Conference on Standards for Communications and Networking, CSCN 2024

作者： Efthymiou, Vasileios Basagiannis, Stylianos Petridou, Sophia University of Macedonia Department of Applied Informatics Greece International Hellenic University Department of Computer Informatics & Telecommunications Engineering Greece

ISBN: (纸本)9798331507428

Leveraging convolutional neural networks (CNNs) to Vehicle-to-Everything (V2X) communication systems offers a promising approach to vehicle detection, which is essential for improving road safety and traffic efficiency. At the same time, parameterization of CNNs' architectures in order to achieve high accuracy in moving objects' detection becomes an engineering challenge, especially when data pre- and post-processing solutions need to respect timing requirements. Our study evaluates the performance of five CNN architectures, namely YOLO in v5, v8, and v9, Faster R-CNN, and VGG16, for car detection. We utilize an open dataset including images derived from road-traffic environments and assess the models' performance using benchmark evaluation metrics, i.e., precision, recall, mean Average Precision (mAP50, mAP50 - 95), and F1-score. Furthermore, we analyze their computational requirements, in terms of execution time and resource demands, which are critical factors when considering real-time applications in V2X systems, such as autonomous driving and traffic management. Our evaluation results indicate that, while the YOLO architecture exhibits the best balance of speed and accuracy, Faster R-CNN has higher detection performance at the cost of greater computational demand. This paper provides insights into each model's strengths and limitations offering guidance for selecting optimal architectures tailored to V2X-related applications. © 2024 IEEE.

关键词： Convolutional neural networks

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Hybrid pedestrian positioning system using wearable inertial sensors and ultrasonic ranging

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Defence Technology（防务技术） 2024年第3期33卷 327-338页

作者： Lin Qi Yu Liu Chuanshun Gao Tao Feng Yue Yu School of Communication and Information Engineering Chongqing University of Posts and TelecommunicationsChongqing 400065China Department of Land Surveying and Geo-Informatics The Hong Kong Polytechnic UniversityHong Kong 999077China

Pedestrian positioning system(PPS)using wearable inertial sensors has wide applications towards various emerging fields such as smart healthcare,emergency rescue,soldier positioning,*** performance of traditional PPS is limited by the cumulative error of inertial sensors,complex motion modes of pedestrians,and the low robustness of the multi-sensor collaboration *** paper presents a hybrid pedestrian positioning system using the combination of wearable inertial sensors and ultrasonic ranging(H-PPS).A robust two nodes integration structure is developed to adaptively combine the motion data acquired from the single waist-mounted and foot-mounted node,and enhanced by a novel ellipsoid constraint *** addition,a deep-learning-based walking speed estimator is proposed by considering all the motion features provided by different nodes,which effectively reduces the cumulative error originating from inertial ***,a comprehensive data and model dual-driven model is presented to effectively combine the motion data provided by different sensor nodes and walking speed estimator,and multi-level constraints are extracted to further improve the performance of the overall *** results indicate that the proposed H-PPS significantly improves the performance of the single PPS and outperforms existing algorithms in accuracy index under complex indoor scenarios.

关键词： Pedestrian positioning system Wearable inertial sensors Ultrasonic ranging Deep-learning Data and model dual-driven

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High-Precision Antenna Modeling Using Recurrent Neural Networks with Bidirectional LSTM Layers and Dimensionality Reduction

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IEEE Transactions on Antennas and Propagation 2025年第6期73卷 4011-4025页

作者： Sahu, Kaustab C. Koziel, Slawomir Pietrenko-Dabrowska, Anna Reykjavik University Engineering Optimization and Modeling Center Reykjavik102 Iceland Gdansk University of Technology Faculty of Electronics Telecommunications and Informatics Gdansk80-233 Poland

This research introduces a novel methodology for high-accuracy modeling of antenna characteristics. It is centered around a recurrent neural network (RNN) optimized through Bayesian Optimization (BO). The RNN architecture includes - among others - a feature input layer, a Long Short-Term Memory (LSTM) layer, a Gated Recurrent Unit (GRU) layer, and a bidirectional LSTM layer. BO is employed to fine-tune the network's hyperparameters, such as the number of units in each layer and the learning rate. The LSTM, GRU, and bidirectional LSTM layers are designed to capture frequency-wise dependencies in antenna responses. A distinctive feature of this approach is the utilization of frequency as a sequential parameter processed by the RNN, which enhances reliability and cost efficiency. These advantages are further bolstered by dimensionality reduction techniques based on rapid global sensitivity analysis. The proposed method has undergone extensive validation across four antennas and has been compared against several benchmark methods, including kernel-based regression models and various neural networks. The results demonstrate that our surrogates achieve outstanding accuracy, significantly surpassing the benchmark, and can construct reliable metamodels even with low-cardinality training datasets. Design applications and experimental validation have also been included. © 1963-2012 IEEE.

关键词： Sensitivity analysis

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