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Over-the-air Wireless Federated Learning Model for Generative AI

IEEE Network 2025年

作者： Zheng, Jie Niyato, Dusit Zhang, Haijun Du, Hongyang Wang, Jiacheng Kang, Jiawen Xiong, Zehui Northwest University State-Province Joint Engineering and Research Center of Advanced Networking and Intelligent Information Services School of Information Science and Technology Shaanxi 710127 China Nanyang Technological University School of Computer Science and Engineering Singapore639798 Singapore University of Science and Technology Beijing Institute of Artificial Intelligence Beijing100083 China University of Hong Kong Department of electrical and electronic engineering Hong Kong Hong Kong Guangdong University of Technology Automation of School Guangzhou510006 China Singapore University of Technology and Design Pillar of Information Systems Technology and Design Singapore

Generative artificial intelligence (GenAI) technologies represent an important advancement in the field of AI, particularly for their capabilities in text and image generation. Over-the-air wireless federated learning (OA-WFL) can utilize the wireless waveform superposition property to achieve efficient model aggregation, thereby providing support for GenAI training and deployment. Therefore, this article investigates the support of OA-WFL for GenAI, focusing on potential applications and specific examples. We first discuss the OA-WFL and GenAI models, emphasizing their functionalities and the potential benefits arising from their interaction. We then explore its application in various GenAI scenarios, including large-scale edge device content generation, efficient distributed training of GenAI models, and reduction of bandwidth requirements and device load. Next, a framework is proposed to apply OA-WFL to diffusion models, such as those used in image generation, and validate its effectiveness through simulation results. Finally, we discuss prospective research directions for the application of OA-WFL for GenAI. © 2025 IEEE.

关键词： Federated learning

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3D-Printed Stainless Steel Electrodes for Advancing Mems Microbial Fuel Cells Toward Sustainable on-chip Energy

3D-Printed Stainless Steel Electrodes for Advancing Mems Mic...

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IEEE International Conference on Micro Electro Mechanical Systems

作者： Anwar Elhadad Guangfa Li Jiaqi Yang Dehao Liu Seokheun Choi Department of Electrical & Computer Engineering Bioelectronics & Microsystems Laboratory State University of New York at Binghamton Binghamton New York USA Department of Mechanical Engineering Integrated Computational Materials Design Lab State University of New York at Binghamton Binghamton New York USA Center for Research in Advanced Sensing Technologies and Environmental Sustainability State University of New York at Binghamton Binghamton New York USA

ISBN: (数字)9798331508890

ISBN: (纸本)9798331508906

This study introduces a stainless steel microbial anode fabricated via advanced laser powder bed fusion, marking a transformative step in microbial fuel cell (MFC) technology. By leveraging this cutting-edge metal 3D printing approach, we achieve a 3D microporous structure with superior electrical conductivity, outstanding corrosion resistance in aquatic environments, robust mechanical strength, and adaptability for surface modification—essential attributes for sustainable, high-performance MFCs. The assembled MFC demonstrates unparalleled power output, along with exceptional durability and stability over multiple cycles, outperforming existing MFCs based on alternative 3D-printed substrates. This advancement not only solidifies the role of MFCs as a sustainable energy solution for powering on-chip electronics but also opens new avenues for scalable, cost-effective bioelectronic systems that align with the demands of next-generation technology.

关键词： Powders Catalysts Fuel cells Laser stability Three-dimensional printing System-on-chip Steel Anodes Laser fusion Surface treatment

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Synthesized Acoustic Vortex-Frequency Comb via Rotational Doppler Effect

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Physical Review Letters 2025年第13期134卷 137001-137001页

作者： Yurou Jia Xuan Zhang Suying Zhang Guoqiang Xu Taoyu Chen Hongtao Zhou Yechao Bai Ying Cheng Dajian Wu Department of Physics MOE Key Laboratory of Modern Acoustics Collaborative Innovation Center of Advanced Microstructures Jiangsu Physical Science Research Center Nanjing University Nanjing 210093 China Department of Electrical and Computer Engineering National University of Singapore Singapore Department of Mechanics School of Mechanical Engineering Tianjin University Tianjin 300350 China School of Electronic Science and Engineering Nanjing University Nanjing 210023 China Jiangsu Key Lab on Opto-Electronic Technology School of Physics and Technology Nanjing Normal University Nanjing 210023 China

The synergistic control of light’s frequency and orbital angular momentum (OAM) via integrated nonlinear ring microresonators is crucial for creating spatiotemporal optical waveforms and advancing optical metrology. A direct analog of this capability in acoustics is challenging due to the nonlinearity-based mechanisms’ dependence on external driving and limited frequency conversion efficiency. Here, we present an acoustic synthesized comb structure derived from spatiotemporal metasurfaces, concurrently controlling frequency and OAM characteristics with high conversion efficiency and removing the constraints of critical driving frequency and power level in nonlinear mechanisms. By optimizing comb structures, energy distributions among OAM modes are adjusted to different frequency lines. We finally propose spatiotemporal metalens devices based on vortex-frequency combs, enabling synchronously multiple-order spatial differential operations for parallel data processing. This Letter is groundbreaking in integrating spectral- and spatial-domain acoustic waves, promising multifunctional imaging and advantages in spatiotemporal beams.

关键词： Vortex flow

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Compact Millimeter Wave Massive MIMO System Utilizing ESPAR

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IEEE Transactions on Communications 2025年

作者： Zhang, Chi Shen, Shanpu Li, Hongyu Ma, Dingfei Han, Zixiang Qian, Jun Clerckx, Bruno Murch, Ross Peng Cheng Laboratory Department of Broadband Communication Shenzhen518055 China University of Liverpool Department of Electrical Engineering and Electronics LiverpoolL69 3GJ United Kingdom Guangzhou511400 China Guangdong University of Technology College of Information Engineering Guangzhou China China Mobile Research Institute Future Research Lab Beijing100053 China The Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Clear Water Bay Kowloon Hong Kong Imperial College London Department of Electrical and Electronic Engineering LondonSW7 2AZ United Kingdom The Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Institute for Advanced Study Clear Water Bay Kowloon Hong Kong

In this work, we propose a compact millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) system utilizing electronically steerable parasitic array radiator (ESPAR). We analyze the system and channel models for the compact mmWave massive MIMO system using a beamspace formulation and demonstrate that we can optimize the spectral efficiency of the compact mmWave massive MIMO system by jointly adjusting the variable reactive loads in the ESPAR with a digital beamformer. We formulate the compact mmWave massive MIMO system optimization problem to maximize spectral efficiency and propose an unconstrained optimization based algorithm with an initialization method. The spectral and energy efficiencies of the compact mmWave massive MIMO system are evaluated in comparison to conventional mmWave massive MIMO systems of the same antenna size using full-digital, fully-connected hybrid, and partially-connected hybrid beamforming. The results show that the compact mmWave massive MIMO system provides higher spectral efficiency than partially-connected hybrid beamforming. On top of this, it can provide higher energy efficiencies of around 3.21, 2.88, and 1.22 times compared to full-digital, fully-connected hybrid, and partially-connected hybrid beamforming but with lower hardware complexity and lower cost. Therefore, compact mmWave massive MIMO systems are a promising and effective alternative to conventional massive MIMO systems in millimeter wave (mmWave) communications. © 1972-2012 IEEE.

关键词： Beamforming

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6G-AUTOR: Autonomic CSI-Free Transceiver via Realtime On-Device Signal Analytics

arXiv

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arXiv 2022年

作者： Lin, Shih-Chun Lin, Chia-Hung Rohit, K.V.S. Chu, Liang C. Intelligent Wireless Networking Lab Department of Electrical and Computer Engineering North Carolina State University RaleighNC27695 United States Lockheed Martin Space Systems Company Lockheed Martin Corporation United States

Next-generation wireless systems aim at fulfilling diverse application requirements but fundamentally rely on point-to-point transmission qualities. Aligning with recent AI-enabled wireless implementations, this paper introduces autonomic radios, 6G-AUTOR, that leverage novel algorithm-hardware separation platforms, softwarization of transmission (TX) and reception (RX) operations, and automatic reconfiguration of RF frontends, to support link performance and resilience. As a comprehensive transceiver solution, our design encompasses several ML-driven models, each enhancing a specific aspect of either TX or RX, leading to robust transceiver operation under tight constraints of future wireless systems. A data-driven radio management module was developed via deep Q-networks to support fast-reconfiguration of TX resource blocks (RB) and proactive multi-agent access. Also, a ResNet-inspired fast-beamforming solution was employed to enable robust communication to multiple receivers over the same RB, which has potential applications in realisation of cell-free infrastructures. As a receiver the system was equipped with a capability of ultra-broadband spectrum recognition. Apart from this, a fundamental tool - automatic modulation classification (AMC) which involves a complex correntropy extraction, followed by a convolutional neural network (CNN)based classification, and a deep learning-based LDPC decoder were added to improve the reception quality and radio performance. Simulations of individual algorithms demonstrate that under appropriate training, each of the corresponding radio functions have either outperformed or have performed on-par with the benchmark solutions. Copyright © 2022, The Authors. All rights reserved.

关键词： Decoding

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Leveraging Randomization-Inspired Hybrid Deep Learning for Enhanced Diabetic Retinopathy Detection Via Multi-Scale Image Analysis

SSRN

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SSRN 2024年

作者： Mutawa, A.M. Hemalakshmi, G.R. Prakash, N.B. Murugappan, M. Computer Engineering Department College of Engineering and Petroleum Kuwait University Safat13060 Kuwait School of Computing Science and Engineering VIT Bhopal University Madhya Pradesh India Department of Electrical and Electronics Engineering National Engineering College Tamilnadu Kovilpatti India Research Lab Department of Electronics and Communication Engineering Kuwait College of Science and Technology Block 4 Doha13133 Kuwait Department of Electronics and Communication Engineering Faculty of Engineering Vels Institute of Sciences Technology and Advanced Studies Tamilnadu Chennai600117 India Universiti Malaysia Perlis Perlis Kangar02600 Malaysia

Diabetic retinopathy (DR), a critical complication of diabetes, significantly increases the risk of vision loss. Prompt detection is essential for effective intervention, yet existing diagnostic practices are resource-intensive, inconsistent, and often inaccessible. This study pioneers an innovative framework, leveraging a hybrid Convolutional Neural Network-Radial Basis Function (CNN-RBF) classifier, synergized with Multi-Scale Discriminative Robust Local Binary Pattern (MS-DRLBP) features, to fortify the detection of DR. Inspired by principles of randomization-based learning, our approach incorporates elements of stochastic modeling within the CNN-RBF architecture to optimize feature extraction and classification, mirroring the efficiency of non-iterative training processes. Through sophisticated image preprocessing, including advanced noise reduction and morphological techniques, complemented by Otsu’s thresholding for precise blood vessel segmentation, we enhance the model's diagnostic capability. Our methodology exhibits superior performance in DR screening, achieving benchmarks of 96.10% precision, 95.35% sensitivity, 97.06% specificity, and 96.10% accuracy, markedly surpassing conventional diagnostic methods. Applied across various publicly available datasets, this research not only advances the precision of DR diagnostics but also contributes to the broader discourse on the potential of hybrid, randomization-inspired neural networks in medical imaging. This fusion of deep learning innovation with the principles of randomization-based algorithms opens new avenues for developing accessible, accurate diagnostic tools, potentially alleviating the global impact of diabetic vision loss.. © 2024, The Authors. All rights reserved.

关键词： Stochastic systems

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Statistical estimation in the spiked tensor model via the quantum approximate optimization algorithm 24

Statistical estimation in the spiked tensor model via the qu...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Leo Zhou Joao Basso Song Mei Walter Burke Institute for Theoretical Physics and the IQIM California Institute of Technology Pasadena CA and Electrical and Computer Engineering Department University of California Los Angeles CA Department of Mathematics University of California Berkeley CA and Quantum Artificial Intelligence Lab (QuAIL) NASA Ames Research Center Moffett Field CA and Research Institute for Advanced Computer Science (RIACS) USRA Mountain View CA Department of Statistics and Department of EECS University of California Berkeley CA

ISBN: (纸本)9798331314385

The quantum approximate optimization algorithm (QAOA) is a general-purpose algorithm for combinatorial optimization that has been a promising avenue for near-term quantum advantage. In this paper, we analyze the performance of the QAOA on the spiked tensor model, a statistical estimation problem that exhibits a large computational-statistical gap classically. We prove that the weak recovery threshold of 1-step QAOA matches that of 1-step tensor power iteration. Additional heuristic calculations suggest that the weak recovery threshold of p-step QAOA matches that of p-step tensor power iteration when p is a fixed constant. This further implies that multi-step QAOA with tensor unfolding could achieve, but not surpass, the asymptotic classical computation threshold Θ(n(q-2)/4) for spiked q-tensors. Meanwhile, we characterize the asymptotic overlap distribution for p-step QAOA, discovering an intriguing sine-Gaussian law verified through simulations. For some p and q, the QAOA has an effective recovery threshold that is a constant factor better than tensor power iteration. Of independent interest, our proof techniques employ the Fourier transform to handle difficult combinatorial sums, a novel approach differing from prior QAOA analyses on spin-glass models without planted structure.

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Block-Wise Index Modulation and Receiver Design for High-Mobility OTFS Communications

arXiv

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arXiv 2023年

作者： Qian, Mi Ji, Fei Ge, Yao Wen, Miaowen Cheng, Xiang Poor, H. Vincent The School of Electronic and Information Engineering South China University of Technology Guangzhou510641 China The Continental-NTU Corporate Lab Nanyang Technological University Singapore The State Key Laboratory of Advanced Optical Communication Systems and Networks Department of Electronics School of Electronics Engineering and Computer Science Peking University Beijing100871 China The Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States

As a promising technique for high-mobility wireless communications, orthogonal time frequency space (OTFS) has been proved to enjoy excellent advantages with respect to traditional orthogonal frequency division multiplexing (OFDM). Although multiple studies have considered index modulation (IM) based OTFS (IM-OTFS) schemes to further improve system performance, a challenging and open problem is the development of effective IM schemes and efficient receivers for practical OTFS systems that must operate in the presence of channel delays and Doppler shifts. In this paper, we propose two novel block-wise IM schemes for OTFS systems, named delay-IM with OTFS (DeIM-OTFS) and Doppler-IM with OTFS (DoIM-OTFS), where a block of delay/Doppler resource bins are activated simultaneously. Based on a maximum likelihood (ML) detector, we analyze upper bounds on the average bit error rates for the proposed DeIM-OTFS and DoIM-OTFS schemes, and verify their performance advantages over the existing IM-OTFS systems. We also develop a multi-layer joint symbol and activation pattern detection (MLJSAPD) algorithm and a customized message passing detection (CMPD) algorithm for our proposed DeIM-OTFS and DoIM-OTFS systems with low complexity. Simulation results demonstrate that our proposed MLJSAPD and CMPD algorithms can achieve desired performance with robustness to the imperfect channel state information (CSI). © 2023, CC BY.

关键词： Message passing

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Performance Optimization for Intelligent Reflecting Surface Assisted Multicast MIMO Networks

Performance Optimization for Intelligent Reflecting Surface ...

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GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Songling Zhang Zhaohui Yang Mingzhe Chen Danpu Liu Kai-Kit Wong H. Vincent Poor Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing China Zhejiang University Hangzhou China Zhejiang Lab Hangzhou China Department of Electrical and Computer Engineering and Institute for Data Science and Computing University of Miami Coral Gables FL USA Department of Electronic and Electrical Engineering University College London London UK Department of Electrical and Computer Engineering Princeton University Princeton NJ USA

ISBN: (纸本)9781665435413

In this paper, the problem of maximizing the sum rate of all users in an intelligent reflecting surface (IRS)-assisted millimeter wave multicast multiple-input multiple-output communication system is studied. In the considered model, one IRS is deployed to assist the communication from a multi-antenna base station (BS) to the multi-antenna users that are clustered into several groups. Our goal is to maximize the sum rate of all users by jointly optimizing the transmit beamforming matrices of the BS, the receive beamforming matrices of the users, and the phase shifts of the IRS. To solve this non-convex problem, we first use a block diagonalization method to represent the beamforming matrices of the BS and the users by the phase shifts of the IRS. Then, substituting the expressions of the beamforming matrices of the BS and the users, the original sum-rate maximization problem can be transformed into a problem that only needs to optimize the phase shifts of the IRS. To solve the transformed problem, a manifold method is used. Simulation results show that the proposed scheme can achieve up to 13.3 % gain in terms of the sum rate of all users compared to the algorithm that optimizes the hybrid beamforming matrices of the BS and the users using our proposed scheme and randomly determines the phase shifts of the IRS.

关键词： Manifolds Multicast algorithms Array signal processing Surface waves Simulation Performance gain Matrices

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Distributed Feedback Laser (DFB) for Signal Power Amplitude Level Improvement in Long Spectral Band

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Journal of Optical Communications 2023年第s1期44卷 S125-S130页

作者： Samanta, Debabrata Sivaram, M. Rashed, Ahmed Nabih Zaki Boopathi, C.S. Amiri, I.S. Yupapin, P. Computational Optics Research Group Advanced Institute of Materials Science Ton Duc Thang University Ho Chi Minh City Viet Nam Faculty of Applied Sciences Ton Duc Thang University Ho Chi Minh City Viet Nam Bangalore Bangalore India Department of Computer Networking Lebanese French University Kurdistan Region Erbil Iraq Electronics and Electrical Communications Engineering Department Faculty of Electronic Engineering Menoufia University Menouf32951 Egypt Department of EEE SRM Institute of Science and Technology Chennai India

This study outlines the distributed feedback laser for signal power amplitude level improvement in the long spectral band of 1550 nm wavelength within supporting pumped wavelength of 1480 nm. The bias and modulation peak currents based distributed feedback laser are varied in order to test the signal power level, peak signal amplitude variations after the fiber-optic channel and light detectors. The signal power level, peak signal amplitude is measured against spectral wavelength and time bit period variations. The study emphasis the signal power level, peak signal amplitude are enhanced for the best selection values of both a bias current at 45 mA and modulation peak current at 0.5 mA. © 2023 Walter de Gruyter GmbH. All rights reserved.

关键词： Distributed feedback lasers

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