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Learning-Based Compress-and-Forward Schemes for the Relay Channel

IEEE Journal on Selected Areas in Communications 2025年第7期43卷 2393-2404页

作者： Ozyilkan, Ezgi Carpi, Fabrizio Garg, Siddharth Erkip, Elza New York University Department of Electrical and Computer Engineering BrooklynNY United States

The relay channel, consisting of a source-destination pair along with a relay, is a fundamental component of cooperative communications. While the capacity of a general relay channel remains unknown, various relaying strategies, including compress-and-forward (CF), have been proposed. In CF, the relay forwards a quantized version of its received signal to the destination. Given the correlated signals at the relay and destination, distributed compression techniques, such as Wyner–Ziv coding, can be harnessed to utilize the relay-to-destination link more efficiently. Leveraging recent advances in neural network-based distributed compression, we revisit the relay channel problem and integrate a learned task-aware Wyner–Ziv compressor into a primitive relay channel with a finite-capacity out-of-band relay-to-destination link. The resulting neural CF scheme demonstrates that our compressor recovers binning of the quantized indices at the relay, mimicking the optimal asymptotic CF strategy, although no structure exploiting the knowledge of source statistics was imposed into the design. The proposed neural CF, employing finite order modulation, operates closely to the rate achievable in a primitive relay channel with a Gaussian codebook. We showcase the advantages of exploiting the correlated destination signal for relay compression through various neural CF architectures that involve end-to-end training of the compressor and the demodulator components. Our learned task-oriented compressors provide the first proof-of-concept work toward interpretable and practical neural CF relaying schemes. © 1983-2012 IEEE.

关键词： Compressors

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A multi-objective optimization approach for overlapping dynamic community detection

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Soft Computing 2024年第19期28卷 11323-11342页

作者： Bahadori, Sondos Mirzaie, Mansooreh Nooraei Abadeh, Maryam Department of Computer Engineering Ilam Branch Islamic Azad University Ilam Iran Department of Electrical and Computer Engineering Golpayegan College of Engineering Isfahan University of Technology Isfahan Iran Department of Computer Engineering Abadan Branch Islamic Azad University Abadan Iran

Community detection is a valuable tool for studying the function and dynamic structure of most real-world networks. Existing techniques either concentrate on the network's topological structure or node properties without adequately addressing the dynamic aspect. As a result, in this research, we present a unique technique called Multi-Objective Optimization Overlapping Dynamic Community Detection (MOOODCD) that leverages both the topological structure and node attributes of dynamic networks. By incorporating the Dirichlet distribution to control network dynamics, we formulate dynamic community detection as a non-negative matrix factorization problem. The block coordinate ascent method is used to estimate the latent elements of the model. Our experiments on artificial and real networks indicate that MOOODCD detects overlapping communities in dynamic networks with acceptable precision and scalability. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Multiobjective optimization

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UAV-Assisted Internet of Vehicles over Licensed and Unlicensed Spectrum: Architecture, Intelligent Resource Management, and Challenges

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IEEE Internet of Things Magazine 2023年第3期6卷 78-84页

作者： Su, Yuhan Liwang, Minghui Chen, Zhong Wang, Xianbin School of Electronic Science and Engineering Xiamen University China School of Informatics Xiamen University China Western University Department of Electrical and Computer Engineering London Canada

Benefited from their flexibility and on-demand deployment capability, unmanned aerial vehicles (UAVs) have emerged as critical aerial communication platforms in future Internet of Vehicles (IoV). However, limited spectrum resources can lead to unsatisfying data rate of IoV, which thus incur large latency, especially under congested IoV network conditions. Although UAVs and road side units (RSUs) can work within the same spectrum and increase spectral efficiency, mutual interference becomes unavoidable. To this end, this article develops a heterogeneous network architecture, in which a UAV-assisted IoV system coexists with a Wi-Fi system: the RSUs can properly occupy unlicensed spectrum to increase the capacity of the UAV-assisted IoV system while mitigating interference, without affecting the performance of the Wi-Fi system. A case study of resource management over licensed and unlicensed spectrum is investigated under the proposed architecture, where time and power are jointly optimized to maximize the sum user satisfaction of the system. We further provide an intelligent solution to tackle the problem in the considered case study. Simulations demonstrate that our proposed case can efficiently improve the sum user satisfaction of the system. Key challenges and opportunities for UAV-assisted IoV over licensed and unlicensed spectrum are discussed, while recommendable future research directions are investigated. © 2018 IEEE.

关键词： Wi-Fi

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Switching Dynamic State Estimation and Event Detection for Inverter-Based Resources with Multiple Control Modes

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IEEE Transactions on Power Systems 2024年第4期40卷 3439-3451页

作者： Huang, Heqing Lin, Yuzhang Department of Electrical and Computer Engineering New York University Brooklyn NY USA

The Dynamic State Estimation (DSE) for Inverter-Based Resources (IBRs) is an emerging topic as IBRs gradually replace synchronous generators (SGs) in power systems. Unlike SGs, the dynamic models of IBRs heavily depend on their control algorithms, and conventional DSE methods for SGs, which assume a unchanged state space and dynamic model, cannot handle IBRs with control mode changes in real time, particularly when the power grid operators are unaware of the current control mode of the IBRs. In response to these challenges, an Expectation-Maximization Sliding-Window Iterated Extended Kalman Filter (EM-SW-IEKF) method is proposed in this paper. It theoretically achieves maximum likelihood estimation under different modes through the EM algorithm, providing the most probable control mode of the system as well as the corresponding state estimate. This method is validated in various IBR systems (battery energy storage systems and solar photovoltaic systems) and under different control mode transitions (switching between grid-following and grid-forming controls and between low voltage ride through and maximum power point tracking controls). © 1969-2012 IEEE.

关键词： Expectation maximization algorithm

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Consistent Action for Stable Training in Reinforcement Learning–based Gain Tuning of Linear Feedback Controller

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Journal of Institute of Control, Robotics and Systems 2024年第9期30卷 965-972页

作者： Byun, Hyungjo ASRI Department of Electrical and Computer Engineering Seoul National University Korea Republic of

Controlling nonlinear systems with linear feedback controller after linearization is a widely used method. This paper proposes a new method to efficiently train a reinforcement learning agent to select the control gain of the linear feedback controller. The proposed method involves the agent selecting a consistent gain by choosing between a new and an old gain. Since the linear feedback controller can operate with this consistent gain, the agent does not need to select the optimal gain, thus simplifying the training process. Numerical simulations were performed by applying the proposed method to a longitudinal model of a nonlinear missile with a 3-loop autopilot. The proposed method enables training with a wide range of gains and enhances the reference tracking performance compared to classical gain scheduling. © ICROS 2024.

关键词： Reinforcement learning

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Incorporation of plasmonic metal-metal and metal-oxide configurations in a polymer solar cell: introducing inorganic features to organic photovoltaics

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Optical and Quantum Electronics 2025年第4期57卷 1-16页

作者： Heidarzadeh, Hamid Department of Electrical and Computer Engineering University of Mohaghegh Ardabili Ardabil Iran

In thicker polymer active layers charge collection efficiency suffers due to low carrier mobility and increased recombination losses. In thin absorber polymer solar cell to increase absorption, light-trapping techniques and plasmonic structures are essential. This study investigates the effect of shell thickness on the photocurrent density of a poly(3-hexylthiophene): phenyl-C61- butyric acid methyl ester (P3HT:PCBM) polymer based solar cell incorporating core–shell nanoparticles with configurations of Au–Ag and Ag-Au core–shell nanoparticles. Through a series of simulation, the photocurrent density was calculated as a function of shell thickness. The results demonstrate that, for both nanoparticle configurations, the photocurrent density generally increases with shell thickness, reaching an optimal point before stabilizing or slightly decreasing. Additionally, the effects of dielectric shells made of SiO₂ and Al₂O₃ on its performance parameters were analyzed. The study also found that the photocurrent decreases with increasing shell thickness for both SiO₂ and Al₂O₃ shells, with a more pronounced decrease for SiO₂ due to its smaller refractive index and greater change in shorter wavelengths. The photocurrent density of 13.74 mA/cm2 is achieved for a cell with a thickness of 80 nm without nanoparticles. This value increases to 16.62 mA/cm2 for a cell incorporating Ag nanoparticles and reaches 19.3 mA/cm2 for a cell with Au–Ag core–shell nanoparticles at the optimal shell thickness. The power conversion efficiency of the polymer solar cell increases from 7.02% without nanoparticles to 8.67% with Ag, 8.45% with Au, and reaches the highest value of 10.26% with Au–Ag core–shell nanoparticles, highlighting the superior performance of the core–shell configuration. This superior performance is attributed to the enhanced plasmonic effects of the Au–Ag combination, which facilitates better light trapping and absorption. These findings underscore the importance of optimizing

关键词： Plasmonics

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Deep convolutional generative adversarial networks: performance analysis in wireless systems

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Discover Internet of Things 2024年第1期4卷 1-8页

作者： Arnab, Shams Fardous Alam, S. M. Ibnul Ul Mahmud, Tasfin Sabuj, Saifur Rahman Ahmed, Shakil Department of Electrical and Electronic Engineering Brac University Dhaka1212 Bangladesh Department of Electrical and Computer Engineering Iowa State University AmesIA United States

This paper introduces Deep Convolutional Generative Adversarial Networks (DCGAN) as a potential solution for wireless systems aiming to enhance the Block Error Rate (BLER). The DCGAN under consideration consists of a generator and a discriminator, which utilizes the same network to differentiate between true and synthetic messages while simultaneously reconstructing the transmitted message. The wireless network is modeled as a DCGAN, where the desirable communication link is modeled between the transmitter and receiver. In addition, the expression for the transmitter and receiver of DCGAN is analytically derived. Based on the simulation outcomes, the proposed DCGAN has investigated a significantly lower BLER than the other models, namely convolutional neural networks, deep learning, and conditional GAN. © The Author(s) 2024.

关键词： Generative adversarial networks

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Laser Sintering Direct Ink Write Silver Nanoflake Ink for On-Demand Manufacturing of Electronics in Space

IEEE Journal on Flexible Electronics

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IEEE Journal on Flexible Electronics 2024年第6期3卷 242-251页

作者： Schlake, Ellie Kandadai, Nirmala Group Department of Electrical Engineering and Computer Science CorvallisOR97331 United States

Developing manufacturing methods for flexible electronics will enable and improve the large-scale production of flexible, spatially efficient, and lightweight devices. Laser sintering is a promising postprocessing method to produce consolidated films for flexible electronic devices while reducing power consumption compared to standard thermal sintering. This work further explores laser sintering of direct ink write (nScrypt) printed silver as a continuation of previous studies on nScrypt-printed silver and aerosol jet-printed gold, platinum, and titanium dioxide for manufacturing printed electronics (PEs) on the International Space Station (ISS). Four different laser wavelengths are studied for laser sintering nScrypt silver nanoflake ink on flexible polyimide and rigid glass substrates. The laser systems investigated are continuous wave (CW) 808 nm, CW 445 nm, CW 1064 nm, and pulsed femtosecond (fs) 1040-nm lasers. The laser power and scanning speed are varied to compare the laser systems and optimize laser sintering parameters for the silver ink. The resistivity of the laser-sintered silver NPs is compared to the resistivity of the unsintered silver to demonstrate the effectiveness of laser sintering. An optimal resistivity of 5.81× 10-8,Ω ·m is achieved for the nScrypt silver laser sintered using a fs 1040-nm laser. © 2022 IEEE.

关键词： Sintering

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Hermite broad-learning recurrent neural control with adaptive learning rate for nonlinear systems

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Soft Computing 2024年第7-8期28卷 6307-6326页

作者： Hsu, Chun-Fei Chen, Bo-Rui Department of Electrical Engineering Tamkang University New Taipei City25137 Taiwan Department of Electrical and Computer Engineering Institute of Electrical and Control Engineering National Yang Ming Chiao Tung University Hsinchu30010 Taiwan

Although conventional control systems are simple and widely used, they may not be effective for complex and uncertain systems. This study proposes a Hermite broad-learning recurrent neural network (HBRNN) with a wide network structure and an internal feedback loop that enables good approximation of system dynamics without the need for a large number of training parameters. Furthermore, a Hermite broad-learning recurrent neural control (HBRNC) with HBRNN as the main controller is proposed, which requires no prior knowledge about the system dynamics and has no off-line learning phase. All the HBRNN network parameters are updated according to parameter learning laws through the gradient descent approach. To prevent network parameter overtraining of the HBRNN, a discrete-type Lyapunov function is used to determine the least upper bound for the learning rates. Additionally, an adaptive learning rate (ALR) approach is designed to dynamically fine-tune the learning rates within these specified limits, thereby achieving an optimal convergence speed between network parameters and tracking error. Finally, the HBRNC system with ALR is applied to a chaotic circuit and a reaction wheel pendulum, and its effectiveness is validated through simulation and experimentation. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023.

关键词： Learning systems

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Noninvasive estimation of blood potassium concentration using ECG and FCM-ANFIS model

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Research on Biomedical engineering 2024年第3-4期40卷 647-658页

作者： Samandari, Zeynab Molaeezadeh, Seyyedeh Fatemeh Department of Electrical and Computer Engineering Jundi-Shapur University of Technology Dezful Iran

Purpose: Potassium imbalance, often symptomless but potentially fatal, is prevalent in patients with kidney or heart conditions. Traditional laboratory tests for potassium measurement are costly and require skilled technicians. Additionally, phlebotomy is challenging and painful for high-risk patients. To address these issues, this study proposes a noninvasive and bloodless approach that leverages the electrocardiogram (ECG) signal, which may be sensitive to potassium changes. The research introduces a data-driven framework for ECG-based potassium measurement. Methods: The proposed framework comprises five main components: Cohort Selection—Among 461,178 patients in the ECG-ViEW II database, those with simultaneous ECG, Age-adjusted Charlson Comorbidity Index (ACCI), and potassium measurements within a 5-min interval are chosen;Data Labeling—Three labels—hypokalemia, normal, and hyperkalemia—are defined;Feature Extraction—Relevant features such as RR interval, PR interval, QRS duration, QT interval, QTc interval, P axis, QRS axis, T axis, and ACCI are extracted;Feature Selection—The Kruskal–Wallis technique evaluates feature importance and selects discriminative ones;and Model Design—An ANFIS model based on FCM clustering (FCM-ANFIS) is developed using the selected features. Results: Among the 42 patients selected during cohort filtering, T axis demonstrated significant association with potassium levels (P © The Author(s), under exclusive licence to The Brazilian Society of Biomedical engineering 2024.

关键词： Electrocardiograms

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