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Dielectric Resonator Magnetoelectric Dipole Arrays with Low Cross Polarization,Backward Radiation,and Mutual Coupling for MIMO Base Station Applications

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China communications 2024年第6期21卷 290-304页

作者： Song Simin Da Yiran Qian Bingyi Huang Xiaoyu Chen Xiaoming Li Yingsong Ahmed A.Kishk School of Information and Communications Engineering Xi’an Jiaotong UniversityXi’an 710049China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui UniversityHefei 230039China Department of Electrical and Computer Engineering Concordia UniversityMontrealQC H3G 1M8Canada

Dielectric resonator magnetoelectric dipole(DRMED)arrays with enhanced isolation,reduced cross-polarization,and backward radiation are proposed for base station(BS)*** proposed antenna comprises an elevated dielectric resonator antenna(DRA)on a small metal plate above a sizeable common ground *** DRA is designed in its T Eδ11 mode,acting like a magnetic *** surface current excited by the differential probes flowing on the small ground plane is equivalent to an electric *** these two equivalent dipoles are orthogonal,they have the magnetoelectric dipole characteristics with reduced backward ***,the small ground planes can be treated as decoupling structures to provide a neutralization path to cancel the original coupling path.A linearly-polarized 4-element prototype array was verified experimentally in previous ***,a dual-polarized DRMED antenna is presented to construct a 2-element and 4×4 array for BS *** investigate its MIMO performance,sophisticated multi-cell scenario simulations are carried *** using the proposed dualpolarized DRMED array,the cellular system capacityis improved by 118.6%compared to a conventional DRA *** significant MIMO system improvement is mainly due to the reduced backward radiation and,therefore,reduced inter-cell *** align well with the simulations.

关键词： backward radiation dielectric resonator antennas(DRAs) isolation magnetoelectric-dipole(ME-dipole)antennas

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Smoothing Neural Network for Non-Lipschitz Optimization with Linear Inequality Constraints

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Chinese Journal of Electronics 2021年第4期30卷 634-643页

作者： YU Xin WU Lingzhen XIE Mian WANG Yanlin XU Liuming LU Huixia XU Chenhua Department of Computer and Electronic Information Guangxi University The Guangxi Key Laboratory of Multimedia Communications and Network Technology School of Computer Science and Engineering Guilin University of Aerospace Technology Department of Electrical Engineering Guangxi University

This paper presents a smoothing neural network to solve a class of non-Lipschitz optimization problem with linear inequality constraints. The proposed neural network is modelled with a differential inclusion equation, which introduces the smoothing approximate techniques. Under certain conditions, we prove that the trajectory of neural network reaches the feasible region in finite time and stays there thereafter, and that any accumulation point of the solution is a stationary point of the original optimization problem. Furthermore, if all stationary points of the optimization problem are isolated,then the trajectory converges to a stationary point of the optimization problem. Two typical numerical examples are given to verify the effectiveness of the proposed neural network.

关键词： Non-Lipschitz optimization Neural networks Stationary points Convergence

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Parameterized and Custom Loss Functions: A Case for Network Traffic Prediction 100

Parameterized and Custom Loss Functions: A Case for Network ...

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100th IEEE Vehicular Technology Conference, VTC 2024-Fall

作者： Tuna, Evren Soysal, Alkan Ulak Communications Inc. Department of Research & Development Ankara06510 Turkey Virginia Tech Wireless@VT Department of Electrical and Computer Engineering BlacksburgVA24061 United States

ISBN: (纸本)9798331517786

We show the importance of designing and using application-specific, parameterized, custom loss functions in predictive Deep Neural Network (DNN) applications. We consider a cellular traffic prediction application in next-generation communication systems where predicting less than the actual traffic value is more harmful than predicting more than the actual traffic value. Cellular network operators aim to maintain service-level agreements (SLAs) that carry high penalty costs for violations, i.e., negative prediction errors. Conversely, the cost of overprovisioning, i.e., positive prediction errors, is less significant to operators compared to SLA violations. Standard loss functions, being symmetric, cannot account for the differing importance of positive and negative prediction errors. In this work, we introduce and compare several custom loss functions where the weight of the negative prediction error is determined by the SLA violation rate. We find that our proposed weighted Mean Exponential Error (wMEE) based loss function results in the smallest overprovisioning volume while adhering to a given SLA violation rate. More crucially, our findings suggest that custom loss function parameter design should be incorporated into the hyperparameter tuning process for DNN applications. © 2024 IEEE.

关键词： Deep neural networks

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A Comprehensive Analysis on Clustering and Routing Techniques in Wireless Sensor Network

A Comprehensive Analysis on Clustering and Routing Technique...

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2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2024

作者： Samson Paul, K. Prabhu Chakkaravarthy, A. Veeresha, M. Srm Institute of Science and Technology Department of Networking and Communications Kattankulathur India Dr.K.V.Subba Reddy Institute of Technology Department of Computer Science and Engineering Kurnool India

ISBN: (数字)9798350360660

ISBN: (纸本)9798350360660

Wireless Sensor Networks (WSN) is utilized as front-end communication systems in home automation, environment monitoring, industry, Internet of Things (IoT), etc. The extension of the lifetime of WSNs enhances the utilization of various applications and systems to communicate and interact efficiently. However, cluster heads (CHs) and relay nodes face the difficulties of energy balancing, nodes having limited capability for storage systems, communication, resource management and computational power which further results in corrupted nodes and data becoming vulnerable to improper access. The optimization algorithms namely Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), and Butterfly Optimization Mechanism (BOM), and Fuzzy Logic (FL)-based techniques are used for reduce the delay in CH selection and routing protocols. This research aims to research the strengths, relationships, and challenges of delay-aware clustering and routing for WSN-based approaches, thereby minimizing the EC and increasing network lifetime. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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Unsourced Random Access in MIMO Quasi-Static Rayleigh Fading Channels: Finite Blocklength and Scaling Law Analyses

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IEEE Transactions on Information Theory 2025年第6期71卷 4342-4373页

作者： Gao, Junyuan Wu, Yongpeng Caire, Giuseppe Yang, Wei Vincent Poor, H. Zhang, Wenjun Department of Electronic Engineering Shanghai Jiao Tong University Minhang Shanghai200240 China Technische Universität Berlin Communications and Information Theory Group Berlin10587 Germany Qualcomm Technologies Inc. San DiegoCA92121 United States Princeton University Department of Electrical and Computer Engineering PrincetonNJ08544 United States

This paper considers the unsourced random access (URA) problem with a random and unknown number of active users in multiple-input multiple-output (MIMO) quasi-static Rayleigh fading channels. We derive non-asymptotic achievability bounds on the probability of incorrectly estimating the number of active users, and provide scaling laws on the gap between the estimated and true numbers of active users. We prove that the error probability reaches a plateau as the power P and blocklength n increase, whereas it decays exponentially with the number L of receive antennas and eventually vanishes. Then, we explore the fundamental limits of URA by deriving non-asymptotic achievability bounds and converse bounds (including two single-user converse bounds and one multi-user ensemble converse bound) on the minimum energy-per-bit required by each active user to transmit J bits with blocklength n under misdetection and false-alarm constraints. Numerical results show that the extra required energy-per-bit due to the uncertainty in the number Ka of active users decreases as L and E[Ka] increase and the error requirement becomes milder. In the non-asymptotic regime, using codewords distributed on a sphere outperforms Gaussian random coding. Existing schemes are shown to exhibit a large gap to our bounds when the number of active users is large, calling for more advanced schemes that perform energy-efficiently in this case. In the asymptotic regime with n → ∞, we establish scaling laws on the minimum required P and L to reliably support Ka active users as functions of n, which highlight the potential of MIMO in enabling low-cost communication and indicate that it is possible for the minimum required P and L to remain on the same order when the number of active users increases but stays below a threshold. © 1963-2012 IEEE.

关键词： Receiving antennas

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An Efficient Dilated DTCN with Residual LSTM-based Allergenicity Prediction for Chemical Compounds using Optimal Weighted Feature Selection and Improved Optimization Mechanism 2

An Efficient Dilated DTCN with Residual LSTM-based Allergeni...

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2nd IEEE International Conference on Networking and communications, ICNWC 2024

作者： Padmavathy, T. Thenmozhi, M. Madhavan, Thirumurthy SRM Institute of Science and Technology Department of Computer Science and Engineering Chennai India SRM Institute of Science and Technology Department of Networking and Communications Chennai India SRM Institute of Science and Technology Department of Genetic Engineering Chennai India

ISBN: (纸本)9798350365269

Allergy is the sudden response of the immunity system which arises later the disclosure of allergens like chemicals, proteins and peptides. In the early times, multiple techniques were formed for allergenicity forecasting of peptides and proteins. However, there is no technique to anticipate the chemical and potential allergenic. The standard food allergen recognition mainly depends on vitro and vivo research, which frequently needs more time and is expensive. Artificial Intelligence (AI) operates a quick food allergen detection model, which solves the above-defined problems and becomes an effective auxiliary tool. Evaluation of potential allergenicity of protein is required at any movement of transgenic proteins implemented in the food chain. Therefore, it is necessary to solve more complications involved in the conventional allergenicity prediction technique for chemical compounds. Thus, a new allergenicity prediction framework for chemical compounds is designed using deep learning techniques. Initially, essential allergenicity data are accumulated from the standard measures. Next, the acquired data are presented for the optimal weighted feature selection stage. In this phase, the weights and features are selected optimally using Opposition Tasmanian Devil Optimization (OTDO). Further, the adopted optimal weighted features are offered to the allergenicity forecasting stage. Here, the allergenicity is predicated on using the developed "Dilated Deep Temporal Context Networks with Residual Long Short-Term Memory Networks (DiDNet-ResLSTM)". Hence, the recommended allergenicity prediction model effectively higher performance rate in different experimental observations. © 2024 IEEE.

关键词： Forecasting

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An Adaptive Framework for Generalizing Network Traffic Prediction Towards Uncertain Environments 58

An Adaptive Framework for Generalizing Network Traffic Predi...

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58th Asilomar Conference on Signals, Systems and computers, ACSSC 2024

作者： Downey, Alexander Tuna, Evren Soysal, Alkan Virginia Tech Wireless@VT Department of Electrical and Computer Engineering BlacksburgVA24061 United States Ulak Communications Inc. Department of Research & Development Ankara06510 Turkey

ISBN: (纸本)9798350354058

We developed a new framework using time-series analysis for dynamically assigning mobile network traffic pre-diction models in previously unseen wireless environments. Our framework selectively employs learned behaviors, outperforming any single model with over a 50% improvement relative to current studies. More importantly, it surpasses traditional approaches without needing prior knowledge of a cell, all while remaining model agnostic. The framework begins with unsupervised clustering of time-series data to identify unique trends. Subsequently, we apply supervised learning for traffic volume prediction within each cluster. This specialization towards specific traffic behaviors occurs without penalties from spatial and temporal variations. Finally, the framework adaptively assigns trained models to new, previously unseen cells. By analyzing real-time measurements of a cell, our framework intelligently selects the most suitable cluster for that cell at any given time, with cluster assignment dynamically adjusting to spatio-temporal fluctuations. © 2024 IEEE.

关键词： Time series

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An Intelligent Deep Neural Sentiment Classification Network

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Intelligent Automation & Soft Computing 2023年第5期36卷 1733-1744页

作者： Umamaheswari Ramalingam Senthil Kumar Murugesan Karthikeyan Lakshmanan Chidhambararajan Balasubramaniyan Department of Electronics and Instrumentation Engineering SRM Valliammai Engineering CollegeKattankulathurTamilnaduIndia Department of Computer Science and Engineering SRM Valliammai Engineering CollegeKattankulathurTamilnaduIndia Department of Electronics and Communications Engineering SRM Valliammai Engineering CollegeKattankulathurTamilnaduIndia

A Deep Neural Sentiment Classiﬁcation Network(DNSCN)is devel-oped in this work to classify the Twitter data *** attempts to extract the negative and positive sentiments in the Twitter *** main goal of the system is toﬁnd the sentiment behavior of tweets with minimum ambiguity.A well-deﬁned neural network extracts deep features from the tweets *** extracting features deeper and deeper,the text in each tweet is represented by Bag-of-Words(BoW)and Word Embeddings(WE)*** effectiveness of DNSCN architecture is analyzed using Twitter-Sanders-Apple2(TSA2),Twit-ter-Sanders-Apple3(TSA3),and Twitter-DataSet(TDS).TSA2 and TDS consist of positive and negative tweets,whereas TSA3 has neutral tweets ***,the proposed DNSCN acts as a binary classiﬁer for TSA2 and TDS databases and a multiclass classiﬁer for *** performances of DNSCN architecture are evaluated by F1 score,precision,and recall rates using 5-fold and 10-fold *** show that the DNSCN-WE model provides more accuracy than the DNSCN-BoW model for representing the tweets in the feature *** F1 score of the DNSCN-BW based system on the TSA2 database is 0.98(binary classiﬁcation)and 0.97(three-class classiﬁcation)for the TSA3 *** system provides better a F1 score of 0.99 for the TDS database.

关键词： Deep neural network word embeddings bag-of-words sentiment analysis text classiﬁcation

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Joint Design of Probabilistic Constellation Shaping and Precoding for Multi-user VLC Systems

Joint Design of Probabilistic Constellation Shaping and Prec...

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2024 IEEE Global communications Conference, GLOBECOM 2024

作者： Nguyen, Thang K. Pham, Thanh V. Le, Hoang D. Nguyen, Chuyen T. Pham, Anh T. The University of Aizu Computer Communications Lab. Japan Shizuoka University Department of Mathematical and Systems Engineering Japan Hanoi University of Science & Technology School of Electrical and Electronic Engineering Viet Nam

ISBN: (纸本)9798350351255

This paper proposes a joint design of probabilistic constellation shaping (PCS) and precoding to enhance the sum-rate performance of multi-user visible light communications (VLC) broadcast channels subject to signal amplitude constraint. In the proposed design, the transmission probabilities of bipolar M-pulse amplitude modulation (M-PAM) symbols for each user and the transmit precoding matrix are jointly optimized to improve the sum-rate performance. The joint design problem is shown to be a complex non-convex problem due to the non-convexity of the objective function. To tackle the problem, the firefly algorithm (FA), a nature-inspired heuristic optimization approach, is employed to solve a local optima to the original non-convex optimization problem. The FA-based approach, however, suffers from high computational complexity. Therefore, we propose a low-complexity design based on zero-forcing (ZF) precoding, which is solved using an alternating optimization (AO) approach. Simulation results reveal that the proposed joint design with PCS significantly improves the sum-rate performance compared to the conventional design with uniform signaling. Some insights into the optimal symbol distributions of the two joint design approaches are also provided. © 2024 IEEE.

关键词： Convex optimization

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Spectrum Sensing Using Cooperative Energy and Matched Filter Detectors in Cognitive Radio 15

Spectrum Sensing Using Cooperative Energy and Matched Filter...

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15th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2023

作者： Ayoob, Ayoob Khalil, Ghaith Ayoub, Zozan Iraqi Ministry of Communications Nineveh Iraq University of Melbourne Faculty of Engineering and Information Technology VIC Australia College of Engineering University of Mosul Computer Engineering Department Iraq

ISBN: (纸本)9798350324433

The rapid growth in the population of internet connected devices needs more freer spectrum. Therefore, cognitive radio has previously been presented as a way to solve the issue of limited spectrum resources through making use of spectrum bands that are designated for primary users. This technique enables the secondary user to access the spectrum while the primary user is not utilizing it, without getting involved with the primary user. The main issue for the secondary users when detecting the spectrum is that the primary use is not visible due to complexity of sensing results and poor noise value. In this paper, cooperative energy and matched filter detectors using an adaptive threshold have been proposed to handle this issue. The metrics that have been used to evaluate the performance of the proposed work are the probabilities of detection, misdetection, and false alarm. In addition, we simulate the work with the MATLAB R2020a software and compare it to other related works in the literature. © 2023 IEEE.

关键词： Signal to noise ratio

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