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Low Complexity Wireless Interference Identification in Antagonistic Environments Based on Multi-Task Learning

IEEE Transactions on Vehicular Technology 2025年

作者： Zhao, Ruosi Jiang, Weiheng Li, Donggen Niyato, Dusit Xiong, Zehui Fu, Shu Lu, Jie Chongqing University School of Microelectronics and Communication Engineering Chongqing400044 China Nanyang Technological University School of Computer Science and Engineering Singapore Singapore University of Technology and Design Pillar of Information Systems Technology and Design Singapore Singapore

As the fundamental premise of anti-interference communication, wireless interference identification (WII) has garnered extensive research and yielded substantial results, especially for deep learning (DL)-enabled WII. However, existing studies are conducted typically under the closed-set assumption, whereby the type of interference is known. This aspect poses a challenge in identifying the unknown interference signals under the d-set assumption. To tackle this issue, this paper proposes a multi-task learning-enabled WII (MTL-WII) algorithm in antagonistic environments. Firstly, we generate the semantic feature space of known classes, forming the mapping relationship from the original signal data to the semantic feature space while calculating the semantic center vectors of each known class through multi-task learning. Secondly, we obtain the semantic feature space of the test set incorporating interference signals of known and unknown classes through the established mapping relationships. Subsequently, the similarity between the semantic feature vectors of the interference signals under test and the semantic center vectors of the interference signals within each class gets calculated using the clustering method. Finally, the attributed class is determined by comparing the similarity against a threshold. Nevertheless, the computational complexity of the proposed method limits its applicability across various scenarios. To this end, this paper further proposes a binarized MTL-WII (BMTL-WII) algorithm. By binarizing the semantic spatial generative network in MTL-WII, both the weights and activation of the semantic spatial generative network replace the 32-bit floating-point numbers with 1-bit fixed-point numbers, which can save memory and improve the speed of the operations. Experimental results show that the MTL-WII model achieves an average identification accuracy of 95.4%, with an 89.3% accuracy for unknown interference signals. Compared to the MTL-WII algorithm, th

关键词： Multi-task learning

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Adaptive Subarray Segmentation: A New Paradigm of Spatial Non-Stationary Near-Field Channel Estimation for XL-MIMO systems

arXiv

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arXiv 2025年

作者： Yang, Shuhang An, Puguang Yang, Peng Cao, Xianbin Wu, Dapeng Oliver Quek, Tony Q.S. School of Electronic and Information Engineering Beihang University Beijing100191 China Department of Computer Science City University of Hong Kong Kowloon Hong Kong Information Systems Technology and Design Pillar Singapore University of Technology and Design Singapore487372 Singapore

To tackle the complexities of spatial non-stationary (SnS) effects and spherical wave propagation in near-field channel estimation (CE) for extremely large-scale multiple-input multiple-output (XL-MIMO) systems, this paper introduces an innovative SnS near-field CE framework grounded in adaptive subarray partitioning. Conventional methods relying on equal subarray partitioning often lead to suboptimal divisions, undermining CE precision. To overcome this, we propose an adaptive subarray segmentation approach. First, we develop a spherical-wave channel model customized for line-of-sight (LoS) XL-MIMO systems to capture SnS traits. Next, we define and evaluate the adverse effects of over-segmentation and under-segmentation on CE efficacy. To counter these issues, we introduce a novel dynamic hybrid beamforming-assisted power-based subarray segmentation paradigm (DHBF-PSSP), which merges cost-effective power measurements with a DHBF structure, enabling joint subarray partitioning and decoupling. A robust partitioning algorithm, termed power-adaptive subarray segmentation (PASS), exploits statistical features of power profiles, while the DHBF utilizes subarray segmentation-based group time block code (SS-GTBC) to enable efficient subarray decoupling with limited radio frequency (RF) chain resources. Additionally, by utilizing angular-domain block sparsity and inter-subcarrier structured sparsity, we propose a subarray segmentation-based assorted block sparse Bayesian learning algorithm under the multiple measurement vectors framework (SS-ABSBL-MMV), employing discrete Fourier transform (DFT) codebooks to lower complexity. Extensive simulation results validate the exceptional performance of the proposed framework over its counterparts. Copyright © 2025, The Authors. All rights reserved.

关键词： Channel estimation

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QTER: QoS-Aware Three-Dimensional Efficient and Reliable Routing for LEO Satellite Networks

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IEEE Internet of Things Journal 2025年

作者： Zheng, Jinkai Luan, Tom H. Li, Guanjie Zhang, Yanfeng Yuan, Mingfeng Pan, Jianping Dongguan University of Technology School of Electrical Engineering and Intelligentization China Xi’an Jiaotong University China Xidian University School of Cyber Engineering China Singapore University of Technology and Design Information Systems Technology and Design Pillar Singapore University of Toronto Institute for Aerospace Studies University of Toronto Robotics Institute Canada University of Victoria Department of Computer Science Canada

Low Earth Orbit (LEO) satellite networks offer a promising approach for achieving global coverage and high-speed Internet access. However, the existing communication frameworks within these networks may result in low robustness and reliability. This paper introduces QTER, a novel and lightweight routing framework specifically designed for LEO networks, aimed at addressing these challenges. Our contributions are threefold. First, QTER establishes a cost-effective and reliable routing framework that accommodates a variety of applications with distinct Quality of Service (QoS) requirements. By enabling multi-path routing, the framework minimizes costs while ensuring end-to-end reliability, thus enhancing adaptability to diverse service demands. Second, we leverage the unique structural characteristics of LEO satellite networks by modeling the satellite constellation as a three-dimensional network, wherein higher-shell satellites serve as management satellite nodes (MSNs) to coordinate routing strategies among lower-shell satellites. This architecture significantly improves system efficiency and adaptability. Third, we propose a failure recovery mechanism that allows MSNs to relay packets when lower-orbit satellites are rendered unavailable due to environmental factors, thereby enhancing system robustness. Extensive simulations demonstrate that QTER exhibits resilience to node failures and dynamic network conditions, achieving reductions in average cost and delay by 59.4% and 38.9%, respectively, compared to baselines. © 2014 IEEE.

关键词： Resource allocation

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Radar Point Cloud-Based Deep Learning Approach for High-Capacity Urban V2I Communications

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IEEE Wireless Communications Letters 2025年

作者： Chen, Leyan Liu, Kai Yang, Peng Xiong, Zehui Quek, Tony Q. S. Zhang, Zhibo Beihang University School of Electronics and Information Engineering Beijing100191 China State Key Laboratory of CNS/ATM Beijing100191 China Singapore University of Technology and Design Information Systems Technology and Design Pillar 487372 Singapore

With the evolution of 5G and 6G communication systems, sensing-assisted communication technology becomes essential for improving the operational efficiency of vehicle-to-infrastructure (V2I) systems. This letter proposes a radar point cloud-based deep learning (PDL) flow to achieve higher channel capacity between vehicles and base stations (BSs). Firstly, target-clutter segmentation is conducted using the proposed PDL based on the radar point clouds. With the segmentation results, a target clustering and tracking module is applied for a further step of false vehicle trajectory elimination. Finally, the deep learning-based method is deployed for intelligent communication beam classification of vehicles with the input of associated radar point cloud features. Numerical results show that the proposed approach improves channel capacity by approximately 10% over the deep neural network (DNN) and 2% over the DNN-based long short-term memory (LSTM) network. Meanwhile, it reduces the parameter count by about 85% compared to DNN and by 88% compared to LSTM-DNN, respectively. © 2012 IEEE.

关键词： Signaling

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Generative AI in Data Center Networking: Fundamentals, Perspectives, and Case Study

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IEEE Network 2025年

作者： Liu, Yinqiu Du, Hongyang Niyato, Dusit Kang, Jiawen Xiong, Zehui Wen, Yonggang Kim, Dong In Nanyang Technological University College of Computing and Data Science Singapore University of Hong Kong Department of Electrical and Electronic Engineering Pok Fu Lam Hong Kong Guangdong University of Technology School of Automation China Singapore University of Technology and Design Pillar of Information Systems Technology and Design Singapore Sungkyunkwan University College of Information and Communication Engineering Korea Republic of

Generative AI (GenAI), exemplified by Large Language Models (LLMs), such as OpenAI’s ChatGPT, is revolutionizing various fields. Central to this transformation is Data Center Networking (DCN), which not only provides the infrastructure support for GenAI operation, but also provisions GenAI services to users. Hence, this article explores the interplay between GenAI and DCNs, analyzing their symbiotic relationship and mutual advances. We begin by reviewing the current challenges of DCNs and GenAI-based solutions, such as data augmentation, process automation, and domain transfer. We then discuss the distinctive characteristics of GenAI workloads on DCNs, gaining insights that catalyze the evolution of DCNs to more effectively support GenAI. Moreover, to illustrate the seamless integration of GenAI with DCNs, we present a case study on GenAI-empowered DCN digital twins. Specifically, we employ an LLM equipped with retrieval augmented generation to formulate optimization problems for DCNs (e.g., resource allocation and routing) and adopt diffusion-deep reinforcement learning to solve optimization. The experimental results on a representative DCN optimization problem, i.e., knowledge placement, demonstrate the validity and efficiency of our proposals. We anticipate that this article can promote further research to enhance the virtuous interaction between GenAI and DCNs. © 1986-2012 IEEE.

关键词： Generative adversarial networks

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ISAR Sensing Based on MUSIC Algorithm in Integrated Sensing and Communications

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IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Zhang, Ruiyun Wang, Zhaolin Wei, Zhiqing Liu, Yuanwei Xiong, Zehui Feng, Zhiyong Beijing University of Posts and Telecommunications Key Laboratory of Universal Wireless Communications Ministry of Education School of Information and Communication Engineering Beijing100876 China Queen Mary University of London School of Electronic Engineering and Computer Science LondonE1 4NS United Kingdom Singapore University of Technology and Design Information Systems Technology and Design Pillar Singapore

Inverse synthetic aperture radar (ISAR) sensing is poised to become a key technology in integrated sensing and communication (ISAC) systems due to its high cross-range resolution for extended targets. However, existing ISAR-based orthogonal frequency-division multiplexing (OFDM) ISAC works are limited and primarily rely on discrete Fourier transform (DFT) techniques, which suffer from inherent resolution constraints, making them inadequate for resolving closely spaced scatterers. We investigate ISAR sensing within OFDM ISAC systems from a novel perspective. First, we model and derive the frequency domain reconstruction of ISAR OFDM received signals, proving that the main parameters range and cross-range of scatterers are decoupled. Building on this insight, we extend and improve two one-dimensional multiple signal classification (1D-MUSIC) algorithms to independently estimate them. Specifically: 1) We consider all possible scatterer relationships and comprehensively classify scatterers into uncorrelated and correlated by providing clear definition;2) Correlated scatterers cause both range and cross-range steering matrices to be rank-deficient and both affect the rank of autocorrelation matrices. Therefore, we propose a spatial smoothing range estimation (SSR) method and a spatial smoothing cross-range estimation (SSC) method to restore the rank of steering matrixes;3) 1D estimation cannot effectively associate the estimated range and cross-range values, and correlated scatterers cause the number of spectral peaks obtained by SSR and SSC methods to be less than the actual number of scatterers. Therefore, we develop a novel point-matching method to pair the decoupled range and cross-range values for each scatterer, while also accurately estimating the total number of scatterers. Finally, the numerical results demonstrate that: 1) The range and cross-range spatial spectrum peaks achieved by the proposed SSR and SSC methods are 10dB higher than those obtained with the c

关键词： Discrete Fourier transforms

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Staying Fresh: Efficient Algorithms for Timely Social Information Distribution

引用

IEEE Transactions on Network Science and engineering 2025年

作者： Li, Songhua Duan, Lingjie Singapore University of Technology and Design Pillar of Engineering Systems and Design Singapore 487372 Singapore

In location-based social networks (LBSNs), users sense urban point-of-interest (PoI) information in the vicinity and share such information with friends in online social networks. Given users' limited social connections and severe lags in disseminating fresh PoI to all, major LBSNs aim to enhance users' social PoI sharing by selecting k out of m users as hotspots and broadcasting their fresh PoI information to the entire user community. This motivates us to study a new combinatorial optimization problem that involves the interplay between an urban sensing network and an online social network. We prove that this problem is NP-hard and also renders existing approximation solutions not viable. Through analyzing the interplay effects between the two networks, we successfully transform the involved PoI-sharing process across two networks to matrix computations for deriving a closed-form objective to hold desirable properties (e.g., submodularity and monotonicity). This finding enables us to develop a polynomial-time algorithm that guarantees a (Formula presented) approximation of the optimum. Furthermore, we allow each selected user to move around and sense more PoI information to share and propose an augmentation-adaptive algorithm with decent performance guarantees. Finally, our theoretical results are corroborated by our simulation findings using both synthetic and real-world datasets. © 2013 IEEE.

关键词： approximation algorithm location-based social network (LBSN) NP-hard Point of interest (PoI) resource augmentation technique

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Proactive Transport with High Link Utilization Using Opportunistic Packets in Cloud Data Centers

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IEEE Transactions on Mobile Computing 2025年

作者： Hu, Jinbin Huang, Jiawei Li, Zhaoyi Li, Yijun Rao, Shuying Jiang, Wenchao Chen, Kai Wang, Jianxin He, Tian Changsha University of Science and Technology School of Computer Science and Technology Changsha410114 China Central South University School of Computer Science and Engineering Changsha410083 China Singapore University of Technology and Design Pillar of Information Systems Technology and Design Singapore Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong Hong Kong University of Minnesota Department of Computer Science and Engineering MinneapolisMN55455 United States

To meet the stringent demanding low latency and high throughput of cloud datacenter applications, recent receiver-driven transport protocols transmit only one packet once receiving each credit packet from the receiver to achieve ultra-low queueing delay. However, the round-trip time variation and the highly dynamic background traffic significantly deteriorate the performance of receiver-driven transport protocols, resulting in under-utilized bandwidth. This paper designs a simple yet effective solution called RPO, which retains the advantages of receiver-driven transmission while efficiently utilizing the available bandwidth. Specifically, RPO rationally uses low-priority opportunistic packets to ensure high network utilization without increasing the queueing delay of high-priority normal packets. Furthermore, to tackle the queueing buildup due to line-rate transmission in the first RTT, we design a selective dropping mechanism called SDM to help the majority of small flows complete within only one RTT by prioritizing the first-RTT bursty packets over the packets triggered by grants. We implement RPO in Linux hosts with DPDK. The experimental results show that RPO significantly improves the network utilization by up to 35% over the state-of-the-art schemes, without introducing additional queueing delay. Moreover, RPO integrated with SDM reduces the AFCT of small flows by up to 45% compared with RPO integrated with Aeolus. © 2002-2012 IEEE.

关键词： Bandwidth

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Task-oriented Age of Information for Remote Inference with Hybrid Language Models

arXiv

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arXiv 2025年

作者： Gan, Shuying Wang, Xijun Feng, Chenyuan Xu, Chao Yang, Howard H. Chen, Xiang Quek, Tony Q.S. School of Electronics and Information Engineering Sun Yat-sen University Guangzhou China Department of Communication Systems EURECOM Sophia Antipolis France School of Information Engineering Northwest A&F University Yangling China ZJU-UIUC Institute Zhejiang University Haining China Information System and Technology Design Pillar Singapore University of Technology and Design Singapore

Large Language Models (LLMs) have revolutionized the field of artificial intelligence (AI) through their advanced reasoning capabilities, but their extensive parameter sets introduce significant inference latency, posing a challenge to ensure the timeliness of inference results. While Small Language Models (SLMs) offer faster inference speeds with fewer parameters, they often compromise accuracy on complex tasks. This study proposes a novel remote inference system comprising a user, a sensor, and an edge server that integrates both model types alongside a decision maker. The system dynamically determines the resolution of images transmitted by the sensor and routes inference tasks to either an SLM or LLM to optimize performance. The key objective is to minimize the Task-oriented Age of Information (TAoI) by jointly considering the accuracy and timeliness of the inference task. Due to the non-uniform transmission time and inference time, we formulate this problem as a Semi-Markov Decision Process (SMDP). By converting the SMDP to an equivalent Markov decision process, we prove that the optimal control policy follows a threshold-based structure. We further develop a relative policy iteration algorithm leveraging this threshold property. Simulation results demonstrate that our proposed optimal policy significantly outperforms baseline approaches in managing the accuracy-timeliness trade-off. Copyright © 2025, The Authors. All rights reserved.

关键词： Markov processes

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User Grouping-Based Hierarchical Rate-Splitting Multiple Access for Reconfigurable Intelligent Surface-Assisted Downlink Communication

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IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Yang, Zhixiang Feng, Lei Zhou, Fanqin Xie, Kunyi Qiu, Xuesong Xiong, Zehui Yuen, Chau Han, Zhu Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China Singapore University of Technology and Design Pillar of Information Systems Technology and Design Singapore487372 Singapore Nanyang Technological University School of Electrical and Electronic Engineering Singapore639798 Singapore University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

The joint optimization of rate-splitting multiple access (RSMA) and reconfigurable intelligent surface (RIS) offers a mutually beneficial solution. In this paper, we study a RIS-assisted downlink user grouping-based hierarchical RSMA communication network, where the base station transmits the common streams and private streams simultaneously to serve multiple users. The sum rate (SR) optimization problem is constructed to jointly optimize user grouping, beamforming, and rate splitting. To tackle the NP-hard problem, a user grouping algorithm is first provided to form the hierarchical RSMA model. Next, an equivalent weighted minimum mean-square error (WMMSE) problem is constructed and a WMMSE-based block coordinate descent (BCD) algorithm is designed to update the remaining variables in an iterated way. Specifically, the optimization of transmit beamforming and the rate splitting matrix is reduced to solving a typical convex quadratically constrained quadratic program problem. The solution for passive beamforming is recovered from the intermediate result obtained by the manifold optimization method. Finally, the process of the BCD algorithm is presented. The numerical results indicate that the proposed scheme evidently surpasses one-layer RSMA scheme, NOMA scheme, SDMA scheme, and no RIS scheme, especially in overloaded scenarios. The proposed scheme further increases the SR of common steams compared to the hierarchical RSMA scheme and significantly reduces computational complexity compared to the generalized RSMA scheme. © 2015 IEEE.

关键词： Mean square error

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