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AFDM-Enabled Integrated Sensing and Communication: Theoretical Framework and Pilot Design

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arXiv 2025年

作者： Zhang, Fan Wang, Zhaocheng Mao, Tianqi Jiao, Tianyu Zhuo, Yinxiao Wen, Miaowen Xiang, Wei Chen, Sheng Karagiannidis, George K. Department of Electronic Engineering Tsinghua University Beijing100084 China State Key Laboratory of CNS/ATM Beijing Institute of Technology Beijing100081 China Zhuhai519088 China School of Electronic and Information Engineering South China University of Technology Guangzhou China School of Computing Engineering and Mathematical Sciences La Trobe University MelbourneVIC3086 Australia School of Electronics and Computer Science University of Southampton SouthamptonSO17 1BJ United Kingdom Department of Electrical and Computer Engineering Aristotle University of Thessaloniki Greece

The integrated sensing and communication (ISAC) has been envisioned as one representative usage scenario of sixth-generation (6G) network. However, the unprecedented characteristics of 6G, especially the doubly dispersive channel, make classical ISAC waveforms rather challenging to guarantee a desirable performance level. The recently proposed affine frequency division multiplexing (AFDM) can attain full diversity even under doubly dispersive effects, thus becoming a competitive candidate for next-generation ISAC waveforms. Relevant investigations are still at an early stage, which involve only straightforward design lacking explicit theoretical analysis. This paper provides an in-depth investigation on AFDM waveform design for ISAC applications. Specifically, the closed-form Crámer-Rao bounds of target detection for AFDM are derived, followed by a demonstration on its merits over existing counterparts. Furthermore, we formulate the ambiguity function of the pilot-assisted AFDM waveform for the first time, revealing conditions for stable sensing performance. To further enhance both the communication and sensing performance of the AFDM waveform, we propose a novel pilot design by exploiting the characteristics of AFDM signals. The proposed design is analytically validated to be capable of optimizing the ambiguity function property and channel estimation accuracy simultaneously as well as overcoming the sensing and channel estimation range limitation originated from the pilot spacing. Numerical results have verified the superiority of the proposed pilot design in terms of dual-functional performance. Copyright © 2025, The Authors. All rights reserved.

关键词： Cramer-Rao bounds

Uplink Control information Based Clustered Access and Discontinuous Reception Management for mMTC

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IEEE Communications Letters 2025年

作者： Wang, Shao-Di Wang, Chang-Long Wang, Hui-Ming Zhou, Feng Leung, Victor C. M. Xidian University Key Laboratory of Electronic Information Countermeasure and Simulation Technology of Ministry of Education Xi’an710071 China Xi’an Jiaotong University School of Information and Communication Engineering Ministry of Education Key Lab for Intelligent Networks and Network Security Xi’an710049 China Shenzhen MSU BIT University Artificial Intelligence Research Institute Shenzhen518172 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China The University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T 1Z4 Canada

Uplink control information (UCI) and discontinuous reception (DRX) play important roles for massive machine type communication (mMTC). Despite their standalone significance, a conspicuous gap exists in comprehensively exploring the synergy between UCI and DRX concerning both energy conservation and latency reduction for mMTC. In this letter, we propose a UCI-based clustered access and DRX management (UCADM) scheme that strikes a balance between these two critical performance metrics, reducing energy usage while preserving low latency in uplink-downlink interaction for mMTC. Specifically, we present a strictly feasible solution for the proposed UCADM scheme by using the spectral radius and the constraint projection, which can effectively solve DRX switching operations as indicated by scheduling information from UCI. Simulation results confirm the effectiveness of our scheme. © 1997-2012 IEEE.

关键词： information management

Towards Intelligent Antenna Positioning: Leveraging DRL for FAS-Aided ISAC Systems

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arXiv 2025年

作者： Yang, Shunxing Yao, Junteng Tang, Jie Wu, Tuo Elkashlan, Maged Yuen, Chau Debbah, Mérouane Shin, Hyundong Valenti, Matthew China Telecom Research Institute Guangzhou510000 China Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo315211 China School of Electronic and Information Engineering South China University of Technology Guangzhou510640 China School of Electrical and Electronic Engineering Nanyang Technological University 639798 Singapore School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom KU 6G Research Center Department of Computer and Information Engineering Khalifa University Abu Dhabi127788 United Arab Emirates Department of Electronic Engineering Kyung Hee University Gyeonggi-do Yongin-si17104 Korea Republic of Lane Department of Computer Science and Electrical Engineering West Virginia University Morgantown United States

Fluid antenna systems (FAS) enable dynamic antenna positioning, offering new opportunities to enhance integrated sensing and communication (ISAC) performance. However, existing studies primarily focus on communication enhancement or single-target sensing, leaving multi-target scenarios underexplored. Additionally, the joint optimization of beamforming and antenna positions poses a highly non-convex problem, with traditional methods becoming impractical as the number of fluid antennas increases. To address these challenges, this letter proposes a block coordinate descent (BCD) framework integrated with a deep reinforcement learning (DRL)-based approach for intelligent antenna positioning. By leveraging the deep deterministic policy gradient (DDPG) algorithm, the proposed framework efficiently balances sensing and communication performance. Simulation results demonstrate the scalability and effectiveness of the proposed approach. © 2025, CC BY.

关键词： Deep reinforcement learning

Knowledge Augmentation in Federation: Rethinking What Collaborative Learning Can Bring Back to Decentralized Data

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arXiv 2025年

作者： Wu, Wentai He, Ligang Long, Saiqin Abdelmoniem, Ahmed M. Wu, Yingliang Mao, Rui Department of Computer Science College of Information Science Technology Jinan University Guangzhou510632 China Department of Computer Science University of Warwick CoventryCV4 7AL United Kingdom School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom Department of Electronic Business The Institute of Digital Business & Intelligent Logistics South China University of Technology Guangzhou510006 China College of Computer Science and Software Engineering The Shenzhen Institute of Computing Sciences Shenzhen University Shenzhen518060 China

Data, as an observable form of knowledge, has become one of the most important factors of production for the development of Artificial Intelligence (AI). Meanwhile, increasing legislation and regulations on private and proprietary information results in scattered data sources also known as the "data islands". Although some collaborative learning paradigms such as Federated Learning (FL) can enable privacy-preserving training over decentralized data, they have inherent deficiencies in fairness, costs and reproducibility because of being learning-centric, which greatly limits the way how participants cooperate with each other. In light of this, we present a knowledge-centric paradigm termed Knowledge Augmentation in Federation (KAF), with focus on how to enhance local knowledge through collaborative effort. We provide the suggested system architecture, formulate the prototypical optimization objective, and review emerging studies that employ methodologies suitable for KAF. On our roadmap, with a three-way categorization we describe the methods for knowledge expansion, knowledge filtering, and label and feature space correction in the federation. Further, we highlight several challenges and open questions that deserve more attention from the community. With our investigation, we intend to offer new insights for what collaborative learning can bring back to decentralized data. © 2025, CC BY-NC-SA.

关键词： Federated learning

Block Sparse Vector Codes for Ultra-Reliable and Low-Latency Short-Packet Transmission

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IEEE Transactions on Communications 2025年

作者： Zhang, Yanfeng Zhu, Xu Liu, Yujie Fan, Xi'an Guan, Yong Liang Wong, M. L. Dennis Lau, Vincent K. N. Dongguan University of Technology School of Electrical Engineering and Intelligentization Dongguan China Harbin Institute of Technology School of Information Science and Technology Shenzhen518055 China Guangxi Normal University Guangxi Key Laboratory of Brain Inspired Computing and Intelligent Chips Guilin541001 China Shenzhen Municipal Key Laboratory of AIoT Communications Shenzhen518055 China Nanyang Technological University Continental-NTU Corporate Lab Singapore Dongguan University of Technology School of Computer Science and Technology Dongguan China Newcastle University School of Engineering Newcastle upon TyneNE1 7RU United Kingdom The Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong

Sparse vector coding (SVC) is a promising short-packet transmission method for ultra reliable low latency communication (URLLC) in next-generation communication systems. In this paper, a block SVC (BSVC) based short-packet transmission scheme is proposed to further enhance the transmission performance of SVC. The core idea behind the proposed scheme is to transmit short-packet data information after block sparse transformation. At the transmitter, the information bits are divided into two parts: one part is mapped into the non-zero indexes of block sparse vectors, and the other part is mapped into non-zero values through phase or amplitude modulation. After pseudo-random spreading, the block sparse vectors are mapped to time-frequency resources for transmission. At the receiver, the decoding problem is transformed into a block sparse signal recovery problem. A cyclic block orthogonal matching pursuit (CBOMP) algorithm is proposed for decoding by leveraging block-structured sparse prior information. The upper bound of block error rate (BLER) performance over Rayleigh channels is derived to verify the decoding performance of the proposed CBOMP algorithm. Extensive simulation results verify that the proposed BSVC scheme outperforms the existing SVC schemes in terms of BLER, transmission latency and spectral efficiency over fading channels. © 1972-2012 IEEE.

关键词： Fading channels

Rescheduling costs and adaptive asymmetric errors guided closed-loop prediction of power loads in mine integrated energy systems

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Energy and AI

Energy and AI 2025年 21卷

作者： Miao, Qi Sun, Xiaoyan Ma, Chen Zhang, Yong Gong, Dunwei School of Information and Control Engineering China University of Mining and Technology No. 1 Daxue Road Jiangsu Xuzhou221116 China School of Artificial Intelligence and Computer Science Jiangnan University No. 1800 Lihu Avenue Jiangsu Wuxi214122 China Department of Computer Science City University of Hong Kong Tat Chee Avenue 518057 Hong Kong School of Information Science and Technology Qingdao University of Science and technology NO. 99 Songling Road Shandong Qingdao266061 China

The development of an integrated energy system for mining that efficiently recycles multiple resources is a crucial strategy for achieving dual carbon reduction targets in the mining sector. Precise load forecasting is fundamental to ensuring the safe and efficient scheduling of this system. However, existing studies often overlook the coupling between load forecasting and scheduling results, treating them independently, which frequently leads to high rescheduling costs due to forecasting errors. To address this issue, we propose a closed-loop load forecasting algorithm that incorporates rescheduling costs and asymmetric errors. We first proposed a data generation and model construction strategy by using real load, predicted load, and rescheduling costs to capture the relationship between load forecasting and rescheduling costs. Considering the different impacts of under-forecasting and over-forecasting on scheduling results, the rescheduling cost model is further integrated with asymmetric prediction errors to define the loss function of the Bi-LSTM based forecasting model. Additionally, an optimization strategy for self-tuning asymmetric prediction error fusion coefficients is designed to ensure the accuracy of load forecasting. The proposed algorithm is applied to the power load forecasting of an integrated energy system in a coal mine in Shanxi. The results demonstrate the effectiveness of the algorithm in reducing system rescheduling costs while ensuring forecasting accuracy, highlighting its potential application in power load forecasting for mine integrated energy systems. © 2025

关键词： Electric load forecasting

Secure Beamforming for Continuous Aperture Array (CAPA) Systems

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arXiv 2025年

作者： Sun, Mingjun Ouyang, Chongjun Wang, Zhaolin Wu, Shaochuan Liu, Yuanwei School of Electronics and Information Engineering Harbin Institute of Technology Harbin150001 China School of Electrical and Electronic Engineering UCD College of Engineering and Architecture University College DublinD04 V1W8 Ireland School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong

Continuous aperture array (CAPA) is considered a promising technology for 6G networks, offering the potential to fully exploit spatial DoFs and achieve the theoretical limits of channel capacity. This paper investigates the performance gain of a CAPA-based downlink secure transmission system, where multiple legitimate user terminals (LUTs) coexist with multiple eavesdroppers (Eves). The system’s secrecy performance is evaluated using a weighted secrecy sum-rate (WSSR) under a power constraint. We then propose two solutions for the secure current pattern design. The first solution is a block coordinate descent (BCD) optimization method based on fractional programming (FP), which introduces a continuous-function inversion theory corresponding to matrix inversion in the discrete domain. This approach derives a closed-form expression for the optimal source current pattern. Based on this, it can be found that the optimal current pattern is essentially a linear combination of the channel spatial responses, thus eliminating the need for complex integration operations during the algorithm’s optimization process. The second solution is a heuristic algorithm based on Zero-Forcing (ZF), which constructs a zero-leakage current pattern using the channel correlation matrix. It further employs a waterfilling approach to design an optimal power allocation scheme that maximizes the WSSR. In high SNR regions, this solution gradually approaches the first solution, ensuring zero leakage while offering lower computational complexity. Simulation results demonstrate that: 1) CAPA-based systems achieve better WSSR compared to discrete multiple-input multiple-output (MIMO) systems. 2) The proposed methods, whether optimization-based or heuristic, provide significant performance improvements over existing state-of-the-art Fourier-based discretization methods, while considerably reducing computational complexity. Copyright © 2025, The Authors. All rights reserved.

关键词： Heuristic algorithms

From Partial Calibration to Full Potential: A Two-Stage Sparse DOA Estimation for Incoherently-Distributed Sources with Gain-Phase Uncertainty

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arXiv 2025年

作者： Xu, He Wu, Tuo Liu, Wei Elkashlan, Maged Al-Dhahir, Naofal Debbah, Mérouane Yuen, Chau So, Hing Cheung School of Cyber Science and Engineering Ningbo University of Technology Ningbo315211 China School of Electrical and Electronic Engineering Nanyang Technological University 639798 Singapore Department of Electrical and Electronic Engineering Hong Kong Polytechnic University Kowloon Hong Kong School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom Department of Electrical and Computer Engineering The University of Texas at Dallas RichardsonTX75080 United States KU 6G Research Center Department of Computer and Information Engineering Khalifa University Abu Dhabi127788 United Arab Emirates Department of Electrical Engineering City University of Hong Kong China

Direction-of-arrival (DOA) estimation for incoherently distributed (ID) sources is essential in multipath wireless communication scenarios, yet it remains challenging due to the combined effects of angular spread and gain-phase uncertainties in antenna arrays. This paper presents a two-stage sparse DOA estimation framework, transitioning from partial calibration to full potential, under the generalized array manifold (GAM) framework. In the first stage, coarse DOA estimates are obtained by exploiting the output from a subset of partly-calibrated arrays (PCAs). In the second stage, these estimates are utilized to determine and compensate for gain-phase uncertainties across all array elements. Then a sparse total least-squares optimization problem is formulated and solved via alternating descent to refine the DOA estimates. Simulation results demonstrate that the proposed method attained improved estimation accuracy compared to existing approaches, while maintaining robustness against both noise and angular spread effects in practical multipath environments. © 2025, CC BY.

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Towards text-refereed multi-modal image fusion by cross-modality interaction

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Signal Processing 2025年 237卷

作者： Li, Qilei Song, Wenhao Gao, Mingliang Zhai, Wenzhe Zhou, Qiang Huang, Zhao School of Electrical and Electronic Engineering Shandong University of Technology Shandong Zibo255000 China School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom Department of Computer and Information Science Northumbria University Newcastle-upon-TyneNE1 8ST United Kingdom

Multi-modal image fusion aims to generate a fused image that possesses the advantage of the source images in different modalities. The fused image is capable of significantly facilitating high-level vision tasks, e.g., image segmentation and object detection. However, most existing fusion methods generally focus on preserving the structure and detailed representation of the fused images while failing to integrate the high-level semantic information in the source images. To address this problem, we propose a text-guided multi-modal image fusion framework, termed Cross-Modality Interaction (CMI)-Fusion. The proposed model leverages the robust capabilities of a large-scale foundation model, i.e., Contrastive Language–Image Pre-training (CLIP), to achieve efficient interaction between image detail and text prompts. Specifically, a Dual Attention Feature Extraction (DAFE) module is derived to extract representative visual and semantic features. Moreover, a cross-modality Image-Text Interaction (ITI) module is derived to achieve a dynamic interaction between the image and corresponding text features. Extensive experiments on various multi-modal datasets demonstrate that the proposed CMI-Fusion retains image structural details and semantic content compared to the state-of-the-art methods. The code is available at https://***/songwenhao123/CMI-Fusion. © 2025 Elsevier B.V.

关键词： Semantic Segmentation