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Toward Extra Large-Scale MIMO: New Channel Properties and Low-Cost designs

IEEE Internet of Things Journal 2023年第16期10卷 14569-14594页

作者： Han, Yu Jin, Shi Matthaiou, Michail Quek, Tony Q. S. Wen, Chao-Kai Southeast University National Mobile Communications Research Laboratory Nanjing210096 China Queen's University Belfast Centre for Wireless Innovation BelfastBT3 9DT United Kingdom Singapore University of Technology and Design Information Systems Technology and Design Pillar Tampines487372 Singapore Kyung Hee University Department of Electronic Engineering Yongin17104 Korea Republic of National Sun Yat-sen University Institute of Communications Engineering Kaohsiung80424 Taiwan

Extra large-scale multiple-input-multiple-output (MIMO) has been recognized as one of the potential development directions of massive MIMO. By employing even more antennas than massive MIMO in the fifth-generation era, extra large-scale MIMO can further exploit the spatial domain resources and enable ultra-high data rates, low latency communications as well as emerging applications, such as sensing and localization, in sixth-generation mobile communication systems. However, with the increase of the size of the antenna array, and the decrease of the distance between a user and the array, new channel properties, that did not manifest in conventional massive MIMO, start to kick in. Most importantly, existing research strategies pertaining to massive MIMO cannot be directly applied or simply extended to fit the extra large-scale MIMO case. Moreover, increasing the number of antennas will inevitably boost the total cost, which refers to not only the high hardware cost, but also the burden of vast processing and computations as well as the substantial training overhead. In this article, we make a survey on the state-of-the-art on the new channel properties of and low-cost designs for extra large-scale MIMO systems. Particularly, we pursue a mathematical analysis to explain why the new features appear and illustrate how they affect the system model. Furthermore, we summarize and compare the low-cost designs from various perspectives and give our suggestions from a practical deployment point of view. © 2014 IEEE.

关键词： Antenna arrays

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Exploiting human-AI dependence for learning to defer 24

Exploiting human-AI dependence for learning to defer

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Proceedings of the 41st International Conference on Machine Learning

作者： Zixi Wei Yuzhou Cao Lei Feng College of Computer Science Chongqing University China School of Computer Science and Engineering Nanyang Technological University Singapore Information Systems Technology Design Pillar Singapore University of Technology and Design Singapore

The learning to defer (L2D) framework allows models to defer their decisions to human experts. For L2D, the Bayes optimality is the basic requirement of theoretical guarantees for the design of consistent surrogate loss functions, which requires the minimizer (i.e., learned classifier) by the surrogate loss to be the Bayes optimality. However, we find that the original form of Bayes optimality fails to consider the dependence between the model and the expert, and such a dependence could be further exploited to design a better consistent loss for L2D. In this paper, we provide a new formulation for the Bayes optimality called dependent Bayes optimality, which reveals the dependence pattern in determining whether to defer. Based on the dependent Bayes optimality, we further present a deferral principle for L2D. Following the guidance of the deferral principle, we propose a novel consistent surrogate loss. Comprehensive experimental results on both synthetic and real-world datasets demonstrate the superiority of our proposed method.

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Semantic Communication with Entropy-and-Channel-Adaptive Rate Control over Multi-User MIMO Fading Channels

arXiv

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arXiv 2025年

作者： Chen, Weixuan Yang, Qianqian Chen, Yuhao Huang, Chongwen Wang, Qian Xiong, Zehui Zhang, Zhaoyang College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China College of Information Engineering Zhejiang University of Technology Hangzhou310023 China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore

Although significant improvements in transmission efficiency have been achieved, existing semantic communication (SemCom) methods typically use a fixed transmission rate for varying channel conditions and transmission contents, leading to performance degradation under harsh channel conditions. To address these challenges, we propose a novel SemCom method for wireless image transmission that integrates entropy-and-channel-adaptive rate control mechanism, specifically designed for multi-user multiple-input multiple-output (MU-MIMO) fading channels. Unlike existing methods, our system dynamically adjusts transmission rates by leveraging the entropy of feature maps, channel state information (CSI), and signal-to-noise ratio (SNR), ensuring optimal communication resource usage. It incorporates feature map pruning, channel attention, spatial attention, and multi-head self-attention (MHSA) to effectively prioritize critical semantic features while minimizing unnecessary transmission overhead. Experimental results demonstrate that the proposed system outperforms separated source and channel coding and deep joint source and channel coding (Deep JSCC), in terms of rate-distortion performance, flexibility, and robustness, particularly in challenging scenarios such as low SNR, imperfect CSI, and inter-user interference. Copyright © 2025, The Authors. All rights reserved.

关键词： Fading channels

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Predictive Covert Communication Against Multi-UAV Surveillance Using Graph Koopman Autoencoder

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IEEE Transactions on Vehicular Technology 2025年

作者： Krishnan, Sivaram Park, Jihong Sherman, Gregory Campbell, Benjamin Choi, Jinho The University of Adelaide School of Electrical and Mechanical Engineering Adelaide5005 Australia Singapore University of Technology and Design Information Systems Technology and Design pillar 487372 Singapore Defence Science and Technology Group Mission Autonomy Platforms Division Canberra2610 Australia

Low Probability of Detection (LPD) communication aims to obscure the presence of radio frequency (RF) signals to evade surveillance. In the context of mobile surveillance utilizing unmanned aerial vehicles (UAVs), achieving LPD communication presents significant challenges due to the UAVs' rapid and continuous movements, which are characterized by unknown nonlinear dynamics. Therefore, accurately predicting future locations of UAVs is essential for enabling real-time LPD communication. In this paper, we introduce a novel framework termed predictive covert communication, aimed at minimizing detectability in terrestrial ad-hoc networks under multi-UAV surveillance. Our data-driven method synergistically integrates graph neural networks (GNN) with Koopman theory to model the complex interactions within a multi-UAV network and facilitating long-term predictions by linearizing the dynamics, even with limited historical data. Extensive simulation results substantiate that the predicted trajectories using our method result in at least 63%-75% lower probability of detection when compared to well-known state-of-the-art baseline approaches, showing promise in enabling low-latency covert operations in practical scenarios. © 2025 IEEE.

关键词： Graph neural networks

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Blind Training for Channel-Adaptive Digital Semantic Communications

arXiv

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arXiv 2025年

作者： Oh, Yongjeong Park, Joohyuk Choi, Jinho Park, Jihong Jeon, Yo-Seb Department of Electrical Engineering POSTECH Gyeongbuk Pohang37673 Korea Republic of School of Electrical and Mechanical Engineering The University of Adelaide SA5005 Australia Information Systems Technology and Design Pillar Singapore University of Technology and Design Singapore487372 Singapore

Semantic encoders and decoders for digital semantic communication (SC) often struggle to adapt to variations in unpredictable channel environments and diverse system designs. To address these challenges, this paper proposes a novel framework for training semantic encoders and decoders to enable channel-adaptive digital SC. The core idea is to use binary symmetric channel (BSC) as a universal representation of generic digital communications, eliminating the need to specify channel environments or system designs. Based on this idea, our framework employs parallel BSCs to equivalently model the relationship between the encoder’s output and the decoder’s input. The bit-flip probabilities of these BSCs are treated as trainable parameters during end-to-end training, with varying levels of regularization applied to address diverse requirements in practical systems. The advantage of our framework is justified by developing a training-aware communication strategy for the inference stage. This strategy makes communication bit errors align with the pre-trained bit-flip probabilities by adaptively selecting power and modulation levels based on practical requirements and channel conditions. Simulation results demonstrate that the proposed framework outperforms existing training approaches in terms of both task performance and power consumption. Copyright © 2025, The Authors. All rights reserved.

关键词： Decoding

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Spatio–Temporal Mixing for Computational Offloading in Satellite Edge Networks with Channel Uncertainty

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IEEE Transactions on Wireless Communications 2025年

作者： Zhang, Shengyu Yang, Huiting Wang, Feng Si, Jiangbo Li, Zan Quek, Tony Q.S Singapore University of Technology and Design Information Systems Technology and Design Pillar 487372 Singapore Xidian University State Key Laboratory of Integrated Service Networks School of Telecommunications Engineering Xi’an710071 China Singapore University of Technology and Design 487372 Singapore Kyung Hee University Department of Electronic Engineering Yongin17104 Korea Republic of

In-orbit computation offloading plays a crucial role in enhancing the performance of resource-constrained mobile devices by conserving energy and reducing application latency. However, the inherent channel uncertainty in uplink communications poses a significant challenge, often degrading the Quality of Service (QoS) provided by Satellite Edge Networks (SENs). This uncertainty cannot be effectively captured by static parametric modeling, limiting their applicability in dynamic environments. To address this limitation, we propose an environment-aware computational offloading strategy for SENs. Unlike previous studies that neglect the impact of uplink channel uncertainty, we focus on this key issue by formulating a stochastic optimization problem aimed at minimizing offloading latency. Our approach integrates channel state variability into the decision-making process, ensuring a more realistic and robust model for SEN applications. In particular, we design a novel Spatio-Temporal Mixing (STM) methodology to extract relevant features from both environmental data and historical Channel State Information (CSI). These features are then used to jointly optimize the task scheduling, satellite selection, and beamforming vector design. Extensive simulations demonstrate that the proposed STM approach significantly reduces latency compared to traditional methods. The results highlight the effectiveness of our strategy in addressing the challenges posed by uplink channel uncertainty, ultimately leading to more efficient and reliable SEN operations. © 2002-2012 IEEE.

关键词： Stochastic systems

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Fundamental Limits Analysis of Multiband Sensing

Fundamental Limits Analysis of Multiband Sensing

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IEEE Conference on Wireless Communications and Networking

作者： Yubo Wan An Liu Rui Du Tony Xiao Han Tony Q.S. Quek College of Information Science and Electronic Engineering Zhejiang University. Huawei Technologies Co. Ltd. Information Systems Technology and Design Pillar Singapore University of Technology and Design.

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

Future wireless systems are supposed to provide high-resolution sensing services via communication signals. Under this background, multiband sensing has recently become a promising technology due to that it can improve the sensing performance by jointly utilizing multiple non-contiguous frequency bands at a low cost. However, few studies have investigated the fundamental limits of multiband sensing, especially in the presence of phase distortion factors. In this paper, we investigate the fundamental limits of multiband sensing in terms of time delay. We derive a closed-form expression of the Cramér-Ran bound (CRB) for the delay separation to reveal useful insights. Additionally, a metric called the statistical resolution limit (SRL) is employed to investigate the fundamental limits of delay resolution. The fundamental limits of delay estimation error are also investigated based on the CRB and Ziv-Zakai bound (ZZB). Based on the above derived fundamental limits, numerical results are presented to provide key insights on the performance limits of the multiband sensing.

关键词： Wireless communication Measurement Closed-form solutions Costs Delay effects Delay estimation Apertures

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Large Language Model as a Catalyst: A Paradigm Shift in Base Station Siting Optimization

arXiv

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arXiv 2024年

作者： Wang, Yanhu Afzal, Muhammad Muzammil Li, Zhengyang Zhou, Jie Feng, Chenyuan Guo, Shuaishuai Quek, Tony Q.S. School of Control Science and Engineering Shandong University Jinan250062 China The Department of Communication Systems EURECOM Sophia Antipolis06410 France The Information Systems Technology and Design Pillar Singapore University of Technology and Design Singapore487372 Singapore

Traditional base station siting (BSS) methods rely heavily on drive testing and user feedback, which are laborious and require extensive expertise in communication, networking, and optimization. As large language models (LLMs) and their associated technologies advance, particularly in the realms of prompt engineering and agent engineering, network optimization will witness a revolutionary approach. This approach entails the strategic use of well-crafted prompts to infuse human experience and knowledge into these sophisticated LLMs, and the deployment of autonomous agents as a communication bridge to seamlessly connect the machine language based LLMs with human users using natural language. Furthermore, our proposed framework incorporates retrieval-augmented generation (RAG) to enhance the system’s ability to acquire domain-specific knowledge and generate solutions, thereby enabling the customization and optimization of the BSS process. This integration represents the future paradigm of artificial intelligence (AI) as a service and AI for more ease. This research first develops a novel LLM-empowered BSS optimization framework, and heuristically proposes three different potential implementations: the strategies based on Prompt-optimized LLM (PoL), LLM-empowered autonomous BSS agent (LaBa), and Cooperative multiple LLM-based autonomous BSS agents (CLaBa). Through evaluation on real-world data, the experiments demonstrate that prompt-assisted LLMs and LLM-based agents can generate more efficient and reliable network deployments, noticeably enhancing the efficiency of BSS optimization and reducing trivial manual participation. © 2024, CC BY.

关键词： systems analysis

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Security Enhancing for IRS-Aided NOMA Networks via Constructive Interference Precoding 10

Security Enhancing for IRS-Aided NOMA Networks via Construct...

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10th IEEE International Symposium on Microwave, Antenna, Propagation and EMC Technologies for Wireless Communications, MAPE 2024

作者： Bao, Jingying Cao, Yang Qin, Xiaoqi Xu, Lexi Zhao, Nan Niyato, Dusit School of Information and Communication Engineering Dalian University of Technology Dalian116024 China Singapore University of Technology and Design Engineering Systems and Design Pillar Singapore487372 Singapore Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China Research Institute China United Network Communications Corporation Beijing100048 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore

ISBN: (纸本)9798331533922

In this paper, we propose a constructive interference precoding (CIP) enabled secure intelligent reflecting surface (IRS)-non-orthogonal multiple access (NOMA) scheme, lever-aging both the inter-user interference and artificial noise to boost the legitimate transmission of far user, while inducing the eavesdropper to decode the deceptive information. Based on this CIP-NOMA scheme, we minimize the transmit power under the perfect channel state information (CSI), subject to the CIP constraint for the far user and eavesdropper, while guaranteeing the quality of service and successive interference cancellation for the near user and the IRS unit modulus constraint. To handle this non-convex problem, an alternating optimization algorithm is proposed. Specifically, by alternately optimizing the precoding vectors at the base station and the IRS reflecting matrix via the successive convex approximation and the penalty-based algorithm, respectively, a reliable solution can be obtained. Simulation results demonstrate that the proposed scheme can achieve better security performance with less energy consumption compared to the conventional NOMA. © 2024 IEEE.

关键词： Multiple access interference

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Large-Scale Decentralized Asynchronous Federated Edge Learning with Device Heterogeneity

Large-Scale Decentralized Asynchronous Federated Edge Learni...

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IEEE International Conference on Communications (ICC)

作者： Xuan Liang Jianhua Tang Tony Q. S. Quek Shien-Ming Wu School of Intelligent Engineering South China University of Technology China Pazhou Lab Guangzhou China Information Systems Technology and Design Pillar Singapore University of Technology and Design Singapore

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

In conventional federated learning (FL), there exists a single point of failure in the central server. Thus the studies about decentralized federated learning (DFL) paradigm have become popular recently. In DFL, some clients with poor computation capacity may take a long time to train local models, therefore, the convergence speed of the global model is usually slow in existing synchronous algorithms. In this work, we consider a large-scale system with device heterogeneity. To reduce training time and fully utilize edge node computation capacity, we propose an asynchronous algorithm in a novel multi-cluster decentralized federated edge learning (MD-FEEL) framework, where there are many clusters and each cluster consists of some clients. Our proposed asynchronous MD-FEEL contains four steps, i.e., local stochastic gradient descent (SGD) update, gradient consensus, intra-cluster model aggregation and inter-cluster model aggregation. To measure the staleness of cluster model, we introduce age of update (AoU) in inter-cluster aggregation stage and theoretically prove the convergence of our proposed algorithm on a non-convex setting. We evaluate our asynchronous MD-FEEL on MNIST and CIFAR-10 datasets and the simulation results show it can aggregate to a global model with better accuracy performance and faster convergence speed than some existing synchronous algorithms.

关键词： Training Performance evaluation Federated learning Computational modeling Simulation Clustering algorithms Stochastic processes

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