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Group Information Geometry Approach for Ultra-Massive MIMO Signal Detection

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IEEE Transactions on Signal Processing 2025年

作者： Yang, Jiyuan Fan, Mingrui Chen, Yan Gao, Xiqi Xia, Xiang-Gen Slock, Dirk Southeast University National Mobile Communications Research Laboratory Nanjing210096 China Purple Mountain Laboratories Nanjing211111 China University of Delaware Department of Electrical and Computer Engineering NewarkDE19716 United States EURECOM Department of Communication Systems Biot06410 France

We propose a group information geometry approach (GIGA) for ultra-massive multiple-input multiple-output (MIMO) signal detection. The signal detection task is framed as computing the approximate marginals of the a posteriori distribution of the transmitted data symbols of all users. With the approximate marginals, we perform the maximization of the a posteriori marginals (MPM) detection to recover the symbol of each user. Based on the information geometry theory and the grouping of the components of the received signal, three types of manifolds are constructed and the approximate a posteriori marginals are obtained through m-projections. The Berry-Esseen theorem is introduced to offer an approximate calculation of the m-projection, while its direct calculation is exponentially complex. In most cases, increasing the number of groups tends to reduce the computational complexity of GIGA. However, when the number of groups exceeds a certain threshold, the complexity of GIGA starts to increase. Simulation results confirm that the proposed GIGA achieves better bit error rate (BER) performance within a small number of iterations, which demonstrates that it can serve as an efficient detection method in ultra-massive MIMO systems. © 1991-2012 IEEE.

关键词： Computational geometry

Industrial Internet of Things Intelligence Empowering Smart Manufacturing: A Literature Review

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arXiv 2023年

作者： Hu, Yujiao Jia, Qingmin Yao, Yuao Lee, Yong Lee, Mengjie Wang, Chenyi Zhou, Xiaomao Xie, Renchao Yu, F. Richard Future Network Research Center Purple Mountain Laboratories Nanjing211111 China Department of Computer Science Northwestern Polytechnical University Xi’an710029 China School of Mechanical and Electronic Engineering Wuhan University of Technology Wuhan430070 China Department of Computer Science Peking University Beijing100091 China State Key Laboratory of networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China Department of Systems and Computer Engineering Carleton University Ottawa Canada

The fiercely competitive business environment and increasingly personalized customization needs are driving the digital transformation and upgrading of the manufacturing industry. IIoT intelligence, which can provide innovative and efficient solutions for various aspects of the manufacturing value chain, illuminates the path of transformation for the manufacturing industry. It's time to provide a systematic vision of IIoT intelligence. However, existing surveys often focus on specific areas of IIoT intelligence, leading researchers and readers to have biases in their understanding of IIoT intelligence, that is, believing that research in one direction is the most important for the development of IIoT intelligence, while ignoring contributions from other directions. Therefore, this paper provides a comprehensive overview of IIoT intelligence. We first conduct an in-depth analysis of the inevitability of manufacturing transformation and study the successful experiences from the practices of Chinese enterprises. Then we give our definition of IIoT intelligence and demonstrate the value of IIoT intelligence for industries in fucntions, operations, deployments, and application. Afterwards, we propose a hierarchical development architecture for IIoT intelligence, which consists of five layers. The practical values of technical upgrades at each layer are illustrated by a close look on lighthouse factories. Following that, we identify seven kinds of technologies that accelerate the transformation of manufacturing, and clarify their contributions. The ethical implications and environmental impacts of adopting IIoT intelligence in manufacturing are analyzed as well. Finally, we explore the open challenges and development trends from four aspects to inspire future researches. Copyright © 2023, The Authors. All rights reserved.

关键词： Environmental impact

Affine Frequency Division Multiplexing: Extending OFDM for Scenario-Flexibility and Resilience

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arXiv 2025年

作者： Yin, Haoran Tang, Yanqun Bemani, Ali Kountouris, Marios Zhou, Yu Zhang, Xingyao Liu, Yuqing Chen, Gaojie Yang, Kai Liu, Fan Masouros, Christos Li, Shuangyang Caire, Giuseppe Xiao, Pei School of Electronics and Communication Engineering Sun Yat-sen University China Mathematical and Algorithmic Sciences Laboratory Huawei France Research and Development France Communication Systems Department EU-RECOM France Department of Computer Science and Artificial Intelligence University of Granada Spain Sun Yat-sen University China School of Information and Electronics Beijing Institute of Technology China National Mobile Communications Research Laboratory Southeast University Bangladesh Department of Electrical and Electronic Engineering University College London United Kingdom Department of Electrical Engineering and Computer Science Technical University of Berlin Germany 5G/6G Innovation Centre University of Surrey United Kingdom

Next-generation wireless networks are conceived to provide reliable and high-data-rate communication services for diverse scenarios, such as vehicle-to-vehicle, unmanned aerial vehicles, and satellite networks. The severe Doppler spreads in the underlying time-varying channels induce destructive inter-carrier interference (ICI) in the extensively adopted orthogonal frequency division multiplexing (OFDM) waveform, leading to severe performance degradation. This calls for a new air interface design that can accommodate the severe delay-Doppler spreads in highly dynamic channels while possessing sufficient flexibility to cater to various applications. This article provides a comprehensive overview of a promising chirp-based waveform named affine frequency division multiplexing (AFDM). It is featured with two tunable parameters and achieves optimal diversity order in doubly dispersive channels (DDC). We study the fundamental principle of AFDM, illustrating its intrinsic suitability for DDC. Based on that, several potential applications of AFDM are explored. Furthermore, the major challenges and the corresponding solutions of AFDM are presented, followed by several future research directions. Finally, we draw some instructive conclusions about AFDM, hoping to provide useful inspiration for its development. Copyright © 2025, The Authors. All rights reserved.

关键词： Orthogonal frequency division multiplexing

Power and Voltage regulation of a Solid-State Transformer based Quad-Active Bridge DC-DC Converter using Adaptive Linear Quadratic Regulator and Nonlinear Model Predictive Control 12

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Power and Voltage regulation of a Solid-State Transformer ba...

12th Annual IEEE Energy Conversion Congress and Exposition, ECCE 2020

作者： Hatatah, Mohammed Grainger, Brandon M. University of Pittsburgh Electric Power Systems Laboratory Department of Electrical and Computer Engineering PittsburghPA United States

ISBN: (纸本)9781728158266

Solid-state transformer (SST) has gained increasing attention in the electrical grid. One of the SST architectures is a three stages SST where quadrable active bridge (QAB) can be used in the isolation stage. This work investigates power and voltage regulation of the QAB module-based SST design under unbalanced loading conditions. The proposed control system applied in the QAB implements an adaptive linear quadratic regulator (ALQR) and nonlinear model predictive control (NMPC) to achieve the regulation. The dynamic performance of the QAB based SST is proven analytically and supported by simulation results. © 2020 IEEE.

关键词： Model predictive control

Regional Medium-Term Hourly Electricity Demand Forecasting Based on LSTM

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Regional Medium-Term Hourly Electricity Demand Forecasting B...

IEEE General Meeting Power& Energy Society

作者： Hongfei Sun Dongliang Duan Hongming Zhang Seong Choi Jie Rockey Luo Liuqing Yang Center of Power Systems Engineering National Renewable Energy Laboratory (NREL) Golden CO Department of Electrical and Computer Engineering University of Wyoming Laramie WY Chief Modeling Engineer with NREL Lower Colorado River Authority in Texas Department of Electrical and Computer Engineering Colorado State University Fort Collins CO Internet of Things Thrust and Intelligent Transportation Thrust The Hong Kong University of Science and Technology (Guangzhou) Guangzhou China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong SAR China

This paper aims to forecast high-resolution (hourly) aggregated load for a certain region in the medium term (a few days to over a year). One region is defined as some places with similar climate characteristics because the climate influences people’s daily lifestyles and hence the electric usage. We decompose the electric usage records into two parts: base load and seasonal load. Considering both temperature and time factors, different deep learning methods are adopted to characterize them. The first goal of our approach is to predict the peak load which is critical for power system planning. Furthermore, our proposed forecast method can provide the depiction of the hourly load profile to provide customized load curves for high-level real-time applications. The proposed method is tested on real-world historical data collected by CAISO, BPA, and PACW. The experimental results show that trained by three years of data, our method could reduce the prediction error for one-year lead hourly load below 5% MAPE, and predict the occurrence of the peak load for next year in CAISO with an error within three days. Furthermore, as a byproduct, an interesting observation on the impact of COVID-19 on human life was made and discussed based on these case studies.

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EAPS: Edge-Assisted Privacy-Preserving Federated Prediction systems

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EAPS: Edge-Assisted Privacy-Preserving Federated Prediction ...

IEEE Conference on Wireless Communications and networking

作者： Daquan Feng Guanxin Huang Chenyuan Feng Bin Cao Zhenzhong Wang Xiang-Gen Xia Guangdong-Hong Kong Joint Laboratory for Big Data Imaging and Communication Shenzhen University Shenzhen China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China Technical Management Center China Media Group Beijing China Department of Electrical and Computer Engineering University of Delaware Newark USA

To reduce the delay and network congestion for content delivery in wireless networks, proactive caching scheme has attracted lots of attentions from both academia and industry. However, traditional caching prediction methods require to collect user data in a centralized server, which is becoming unreliable and impractical due to regulatory restrictions. To circumvent this issue, deploying caching prediction system in a federated learning (FL) fashion becomes a promising solution. However, there still exist privacy risks, and even worse, the FL is vulnerable to low-cost attacks. To solve this problem, a novel federated prediction system (FPS) is studied to provide high robustness and privacy. Firstly, to keep a balance between further enhancing privacy protection and alleviating the performance degradation caused by additional protection schemes, we propose an edge-assisted, robust and privacy-preserving FPS framework based on the local differential privacy (LDP) scheme. Secondly, to mitigate the impact of heterogeneous data, we add a regularization term to the local loss function. Furthermore, an attention-based aggregation scheme is proposed to defend against Byzantine attacks during the training process. Finally, the experiment results are provided to show the superiority of our proposed algorithm in terms of prediction accuracy and robustness.

关键词： Training Industries Privacy Federated learning Wireless networks Predictive models Prediction algorithms

A Wideband Decoupling Technique for Stacked Patch Antenna Array By Using Triangular Layout

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A Wideband Decoupling Technique for Stacked Patch Antenna Ar...

IEEE Conference on Antenna Measurements & Applications (CAMA)

作者： Yulin Fang Zijian Shao Yue Ping Zhang Key Laboratory of High-Speed Electronic Systems Shanghai Jiao Tong University Shanghai China Department of Electrical and Computer Engineering Princeton University Princeton USA School of Electrical and Electronic Engineering Nanyang Technological University Singapore

ISBN: (纸本)9781665490382

A wideband stacked patch antenna decoupling technique is proposed in this paper. The shorted stacked microstrip patch antenna (SS-MPA) achieves excellent isolation between E-plane coupled elements while the coupling between H-plane coupled elements is relatively large. By employing the triangular layout, the H-plane coupling is lessened to a certain extent. To further reduce the mutual coupling between the adjacent elements, a pair of twisted slits on the ground along the non-radiating edge of the SS-MPA are etched, which introduces a new decoupling null in the coupling curve of the intersecting elements, achieving a good decoupling performance in the SSMPA array. Results indicate that good decoupling performance can be realized.

关键词： Couplings Antenna measurements Mutual coupling Patch antennas Layout Microstrip antennas Microstrip

Asynchronous Multiuser Detection for SCMA with Unknown Delays: A Compressed Sensing Approach

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arXiv 2022年

作者： Wheeler, Dylan Tripp, Erin E. Natarajan, Balasubramaniam Department of Electrical and Computer Engineering Kansas State University United States The High Performance Systems Branch The Air Force Research Laboratory Information Directorate United States

Despite being the subject of a growing body of research, non-orthogonal multiple access has failed to garner sufficient support to be included in modern standards. One of the more promising approaches to non-orthogonal multiple access is sparse code multiple access, which seeks to utilize non-orthogonal, sparse spreading codes to share bandwidth among users more efficiently than traditional orthogonal methods. Nearly all of the studies regarding sparse code multiple access assume synchronization at the receiver, which may not always be a practical assumption. In this work, we aim to bring this promising technology closer to a practical realization by dropping the assumption of synchronization. We therefore propose a compressed sensing-based delay estimation technique developed specifically for an uplink sparse code multiple access system. The proposed technique can be used with nearly all of the numerous decoding algorithms proposed in the existing literature, including the popular message passing approach. Furthermore, we derive a theoretical bound regarding the recovery performance of the proposed technique, and use simulations to demonstrate its viability in a practical uplink system. Copyright © 2022, The Authors. All rights reserved.

关键词： Compressed sensing

A Survey of Recent Advances in Optimization Methods for Wireless Communications

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arXiv 2024年

作者： Liu, Ya-Feng Chang, Tsung-Hui Hong, Mingyi Wu, Zheyu So, Anthony Man-Cho Jorswieck, Eduard A. Yu, Wei The State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China The School of Science and Engineering The Chinese University of Hong Kong Shenzhen Shenzhen China Shenzhen Research Institute of Big Data China The Department of Electrical and Computer Engineering University of Minnesota MinneapolisMN55455 United States The Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong Hong Kong The Institute for Communication Technology Technische Universitat Braunschweig Germany The Edward S. Rogers Sr. Department of Electrical and Computer Engineering University of Toronto TorontoONM5S 3G4 Canada

Mathematical optimization is now widely regarded as an indispensable modeling and solution tool for the design of wireless communications systems. While optimization has played a significant role in the revolutionary progress in wireless communication and networking technologies from 1G to 5G and onto the future 6G, the innovations in wireless technologies have also substantially transformed the nature of the underlying mathematical optimization problems upon which the system designs are based and have sparked significant innovations in the development of methodologies to understand, to analyze, and to solve those problems. In this paper, we provide a comprehensive survey of recent advances in mathematical optimization theory and algorithms for wireless communication system design. We begin by illustrating common features of mathematical optimization problems arising in wireless communication system design. We discuss various scenarios and use cases and their associated mathematical structures from an optimization perspective. We then provide an overview of recently developed optimization techniques in areas ranging from nonconvex optimization, global optimization, and integer programming, to distributed optimization and learning-based optimization. The key to successful solution of mathematical optimization problems is in carefully choosing or developing suitable algorithms (or neural network architectures) that can exploit the underlying problem structure. We conclude the paper by identifying several open research challenges and outlining future research directions. Copyright © 2024, The Authors. All rights reserved.

关键词： Beamforming