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Cross-subcarrier precoder design for massive MIMO-OFDM downlink with symplectic optimization

Science China(Information Sciences) 2025年第2期68卷 216-229页

作者： Yuxuan ZHANG An-An LU Xiang-Gen XIA Xiqi GAO National Mobile Communications Research Laboratory Southeast University Purple Mountain Laboratories Department of Electrical and Computer Engineering University of Delaware

We propose a cross-subcarrier precoder design(CSPD) for massive multiple-input multiple-output(MIMO) orthogonal frequency division multiplexing(OFDM) downlink. This work aims to significantly improve the channel estimation and signal detection performance by enhancing the smoothness of the frequency domain effective channel. This is accomplished by designing a few vectors known as the transform domain precoding vectors(TDPVs), which are then transformed into the frequency domain to generate the precoders for a set of subcarriers. To combat the effect of channel aging, the TDPVs are optimized under imperfect channel state information(CSI). The optimal precoder structure is derived by maximizing an upper bound of the ergodic weighted sum-rate(WSR) and utilizing the a posteriori beam-based statistical channel model(BSCM). To avoid the large-dimensional matrix inversion, we propose an algorithm with symplectic optimization. Simulation results indicate that the proposed cross-subcarrier precoder design significantly outperforms conventional methods.

关键词： massive MIMO-OFDM cross subcarrier precoder design transform domain precoding vectors effective channel symplectic optimization

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Integration of data science with the intelligent IoT(IIoT): Current challenges and future perspectives

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Digital communications and Networks 2025年第2期11卷 280-298页

作者： Inam Ullah Deepak Adhikari Xin Su Francesco Palmieri Celimuge Wu Chang Choi Department of Computer Engineering Gachon University University of Electronic Science and Technology of China College of Information Science and Engineering Hohai University Department of Computer Science University of Salerno Graduate School of Informatics and Engineering The University of Electro-Communications

The Intelligent Internet of Things(IIoT) involves real-world things that communicate or interact with each other through networking technologies by collecting data from these “things” and using intelligent approaches, such as Artificial Intelligence(AI) and machine learning, to make accurate decisions. Data science is the science of dealing with data and its relationships through intelligent approaches. Most state-of-the-art research focuses independently on either data science or IIoT, rather than exploring their integration. Therefore, to address the gap, this article provides a comprehensive survey on the advances and integration of data science with the Intelligent IoT(IIoT) system by classifying the existing IoT-based data science techniques and presenting a summary of various characteristics. The paper analyzes the data science or big data security and privacy features, including network architecture, data protection, and continuous monitoring of data, which face challenges in various IoT-based systems. Extensive insights into IoT data security, privacy, and challenges are visualized in the context of data science for IoT. In addition, this study reveals the current opportunities to enhance data science and IoT market development. The current gap and challenges faced in the integration of data science and IoT are comprehensively presented, followed by the future outlook and possible solutions.

关键词： Data science Internet of things (IoT) Big data Communication systems Networks Security Data science analytics

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Information Rates of Successive Interference Cancellation for Optical Fiber

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IEEE Journal on Selected Areas in communications 2025年第5期43卷 1484-1497页

作者： Jager, Alex Kramer, Gerhard Technical University of Munich Institute of Communications Engineering Department of Computer Engineering School of Computation Information and Technology Munich80333 Germany

Joint detection and decoding (JDD) achieves rates based on information theory but is too complex to implement for many channels with memory or nonlinearities. Successive interference cancellation (SIC) at the receiver, combined with multistage encoding at the transmitter, is a method that lets one use coded modulation for memoryless channels to approach JDD rates. A SIC-based receiver is presented to compensate for interchannel interference in long-haul optical fiber links. Simulations for 1000 km of standard single-mode fiber with ideal distributed Raman amplification, single-polarization transmission, and circularly symmetric complex Gaussian (CSCG) modulation show that SIC attains the achievable information rates (AIRs) of JDD using surrogate channel models with correlated phase and additive noise (CPAN). Moreover, the AIRs of ring constellations are compared to those of CSCG modulation. Simulations show that 32 rings, 16 SIC-stages, and Gaussian message passing on the factor graph of the CPAN surrogate model achieve the JDD rates of CSCG modulation. The computational complexity scales in proportion to the number of SIC-stages, where one stage has complexity similar to separate detection and decoding. © 1983-2012 IEEE.

关键词： Decoding

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Blockchain-based knowledge-aware semantic communications for remote driving image transmission

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Digital communications and Networks 2025年第2期11卷 317-325页

作者： Yangfei Lin Tutomu Murase Yusheng Ji Wugedele Bao Lei Zhong Jie Li Department of Computer and Network Engineering University of Electro-Communications Information Technology Center Nagoya University National Institute of Informatics(NII) Hohhot Minzu College Toyota Motor Corporation Department of Computer Science and Engineering Shanghai Jiaotong University

Remote driving, an emergent technology enabling remote operations of vehicles, presents a significant challenge in transmitting large volumes of image data to a central server. This requirement outpaces the capacity of traditional communication methods. To tackle this, we propose a novel framework using semantic communications, through a region of interest semantic segmentation method, to reduce the communication costs by transmitting meaningful semantic information rather than bit-wise data. To solve the knowledge base inconsistencies inherent in semantic communications, we introduce a blockchain-based edge-assisted system for managing diverse and geographically varied semantic segmentation knowledge bases. This system not only ensures the security of data through the tamper-resistant nature of blockchain but also leverages edge computing for efficient management. Additionally, the implementation of blockchain sharding handles differentiated knowledge bases for various tasks, thus boosting overall blockchain efficiency. Experimental results show a great reduction in latency by sharding and an increase in model accuracy, confirming our framework's effectiveness.

关键词： Semantic communication Remote driving Semantic segmentation Blockchain Knowledge base management

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Neutrosophic Fuzzy Power Management (NFPM): Tackling Uncertainty in Energy Harvesting for Sensor Networks

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Neutrosophic Sets and Systems 2025年 76卷 99-114页

作者： Alzubi, Musallam M. Mohamed, Mohamed A. Amer, Hanan M. Salama, A.A. Electronics and Communications Engineering Department Faculty of Engineering Mansoura University Mansoura Egypt Department of Mathematics and Computer Science Faculty of Science Port Said University Egypt

This paper presents a novel approach known as Neutrosophic Fuzzy Power Management (NFPM) aimed at addressing the critical challenge of uncertain energy availability in Energy Harvesting Sensor Networks (EHWSNs). The main objective of this research is to enhance the management of energy resources within these networks, which traditional fuzzy logic methods often fail to do, leading to power failures and reduced reliability. NFPM utilizes neutrosophic logic to effectively model uncertainty by representing the degrees of truth, indeterminacy, and falsity of both harvested and residual energy levels. Through a fuzzy inference system, NFPM dynamically allocates energy budgets for each time slot based on these neutrosophic sets, resulting in more adaptive and conservative energy distribution. The results are validated through numerical examples and extensive simulations, demonstrating NFPM's superiority over traditional fuzzy logic, with significant improvements such as a 25% reduction in power failures, 95% enhanced network connectivity, a 15% increase in data transmission success rates, and overall improvements in energy efficiency and robustness to fluctuations and noise. This research establishes NFPM as a promising solution to the uncertainties inherent in EHWSNs. Future research directions include exploring the integration of NFPM with machine learning algorithms for predictive energy management, assessing its scalability in larger networks, and examining its applicability in other domains requiring energy management under uncertainty. © 2025, University of New Mexico. All rights reserved.

关键词： Budget control

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Anomalous IoT Behavior Detection by LSTM-Based Power Waveform Prediction 10

Anomalous IoT Behavior Detection by LSTM-Based Power Wavefor...

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10th International Conference on Internet of Things, Big Data and Security, IoTBDS 2025

作者： Eda, Ryusei Togawa, Nozomu Department of Computer Science and Communications Engineering Waseda University Japan

ISBN: (纸本)9789897587504

Internet of Things (IoT) devices have very rapidly spread out in recent years. In IoT devices where applications run on operating system (OS), the power consumption of the OS and the power consumption of the applications overlap, resulting in complex power waveform. Previous methods need to explicitly extract the application power waveform from the multiple signal sources in the measured power waveform, which often fail to detect anomalous behaviors. In this paper, we propose a method to detect anomalous behaviors by using LSTM (Long Short Term Memory). The proposed method learns power waveform containing multiple signal sources and compares the predicted waveform and the actual one. Then, we can successfully detect anomalous behaviors, even though the measured power waveform is composed of multiple signal sources. Experimental results show that anomalous behavior can be successfully detected from an IoT device built with Raspberry Pi4. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Malware

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Federated Blockchain-Based Clustering Protocol for Enhanced Security and Connectivity in FANETs With CF-mMIMO

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IEEE Internet of Things Journal 2025年第10期12卷 14016-14035页

作者： Pramitarini, Yushintia Perdana, Ridho Hendra Yoga Shim, Kyusung An, Beongku Hongik University Graduate School Department of Software and Communications Engineering Sejong30016 Korea Republic of Hankyong National University School of Computer Engineering and Applied Mathematics Korea Republic of Hongik University Department of Software and Communications Engineering Sejong30016 Korea Republic of

In this paper, we propose a novel federated blockchain (FedChain)-based clustering protocol to enhance network security and connectivity in flying ad hoc networks (FANETs) with cell-free massive MIMO (CF-mMIMO). By leveraging blockchain technology and federated learning (FL), the cluster can be protected against Sybil attacks, enabling secure cluster formation without increasing the number of control packets. We formulate the cost function maximization problem based on cross-layer design, which integrates physical layer information (mobility, position, channel capacity, and remaining energy) and network layer parameters (connectivity) to optimize the formation of stable clusters with minimal control overhead. Furthermore, we select the optimal cluster heads (CHs) based on the highest remaining energy and velocity-constrained criteria, ensuring long-term stability. To solve the security issue, blockchain technology is adopted to validate transactions among nodes and ensure secure formation by distinguishing legitimate users and Sybil attack nodes. Additionally, we develop a novel FL framework to predict and distinguish node status in real time without additional control packets, improving security and control overhead performance during cluster formation. Simulation results demonstrate that the proposed FedChain-based clustering protocol outperforms the lowest ID (LI), high connectivity degree (HCD), and conventional blockchain-based clustering (CBC) protocols in terms of connectivity, control overhead, and security performance. The results highlight that the FedChain-based clustering protocol provides robust security and connectivity, making it well-suited for dynamic FANET environments. © 2014 IEEE.

关键词： Cost functions

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Random Frame: a Data Augmentation for Glass Detection 27th

Random Frame: a Data Augmentation for Glass Detection

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Liang, Yiming Ishikawa, Hiroshi Department of Computer Science and Communications Engineering Waseda University Tokyo Japan

ISBN: (纸本)9783031801358

Glass, though ubiquitous, is difficult to recognize in an image due to its transparency. Fine-grained low-level features indicating the presence of glass, such as refraction and reflection, are weak and subtle. This causes difficulties for existing glass detection models in learning those features, pushing them to rely on more overt cues, especially the frame surrounding the glass. Consequently, they can be fooled easily by frame-like objects. Here, we propose a simple data augmentation scheme called Random Frame to address this problem. Random Frame inserts a frame into an image to create an area with a frame but no glass. The model will receive a penalty if it only relies on the frame. The performances of existing models on various datasets improve when Random Frame is applied while being trained. Our comprehensive experiments demonstrate that our data augmentation can make models utilize more low-level features with more confidence in their predictions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Image recognition

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Optimized Resource Allocation in Vehicle Edge Computing through Platoon Collaboration

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IEEE Internet of Things Journal 2025年第11期12卷 16129-16141页

作者： Zhao, Liang Feng, Yuhang Hawbani, Ammar Xu, Lexi Liu, Zhi Bi, Yuanguo Shenyang Aerospace University School of Computer Science Shenyang110136 China China Unicom China United Network Communications Corporation Research Institute Beijing100048 China University of Electro-Communications Department of Computer and Network Engineering Japan Northeastern University School of Computer Science and Engineering Shenyang110819 China

In modern vehicular networks, the absence of infrastructure support such as Roadside Units (RSUs) presents significant challenges for efficient task offloading and allocation. Limited computational capabilities of individual vehicles, combined with task allocation imbalances caused by varying task complexity and vehicle capacities, further complicate the process. Additionally, the formation of vehicular platoons requires accurate future route and destination information to ensure stable collaboration and effective coordination. However, such information can be challenging to obtain due to dynamic and unpredictable road environments, hindering the reliability of platoon formation. To address these challenges, we propose a platoon-based offloading strategy that integrates Deep Reinforcement Learning (DRL) and Long Short-Term Memory (LSTM) networks to enhance task allocation efficiency. This approach also leverages the Convoy Formation Algorithm considering Future Positions (CFA-FP) to manage platoon constraints effectively. Experimental results demonstrate that our method significantly improves key performance metrics, including total computation cost, latency, and offloading success rate, compared to other task offloading strategies. © 2014 IEEE.

关键词： Deep reinforcement learning

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Message Passing Detector for Phase-Shifter-Aided Spatial Multiplexing over Frequency Selective Channels

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IEEE Transactions on Vehicular Technology 2025年第5期74卷 8273-8278页

作者： Liu, Min Zhang, Lechen Chen, Jiangong Zammit, Saviour Xiao, Yue University of Electronic Science and Technology of China National Key Laboratory of Wireless Communications Chengdu611731 China University of Malta Department of Communications and Computer Engineering MsidaMSD 2080 Malta

Reducing the cost of radio frequency (RF) chains remains a critical challenge in multiple-input multiple-output (MIMO) systems. In this paper, we investigate a novel low-RF-chain technique, termed as phase-shifter-aided spatial multiplexing (PSSM), in the context of frequency-selective channels. Base on the single-carrier frequency-domain equalization (SC-FDE) concept, we develop a message-passing detector specifically tailored for PSSM systems. This detector integrates belief propagation (BP) and expectation propagation (EP) to efficiently leverage prior constraints as well as handling the linear inverse problem. To further reduce computational complexity, a simplification method is then introduced. Simulation results demonstrate that the proposed framework achieves superior performance compared to conventional approaches. © 1967-2012 IEEE.

关键词： Inverse problems

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