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arXiv 2025年

作者： Guo, Fei Wang, Zilong Gong, Guang State Key Laboratory of Mathematical Engineering and Advanced Computing Information Engineering University Zhengzhou450001 China State Key Laboratory of Integrated Service Networks Xidian University Xi’an710071 China Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada

In 2017, Zhang et al. proposed a question (not open problem) and two open problems in [IEEE TIT 63 (8): 5336–5349, 2017] about constructing bent functions by using Rothaus’ construction. In this note, we prove that the sufficient conditions of Rothaus’ construction are also necessary, which answers their question. Besides, we demonstrate that the second open problem, which considers the iterative method of constructing bent functions by using Rothaus’ construction, has only a trivial solution. It indicates that all bent functions obtained by using Rothaus’ construction iteratively can be generated from the direct sum of an initial bent function and a quadratic bent function. This directly means that Zhang et al.’s construction idea makes no contribution to the construction of bent functions. To compensate the weakness of their work, we propose an iterative construction of bent functions by using a secondary construction in [DCC 88: 2007–2035, 2020]. © 2025, CC BY-NC-ND.

关键词： Hadamard transforms

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Fundamental Limits of Multiple-Access Integrated Sensing and Communication Systems

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IEEE Transactions on Information Theory 2025年

作者： Liu, Yao Li, Min Liu, An Ong, Lawrence Yener, Aylin Zhejiang University College of Information Science and Electronic Engineering Zhejiang Provincial Key Laboratory of Multi-Modal Communication Networks and Intelligent Information Processing Hangzhou310027 China University of Newcastle School of Engineering CallaghanNSW2308 Australia The Ohio State University Department of Electrical and Computer Engineering ColumbusOH43210 United States

A state-dependent discrete memoryless multiple access channel is considered to model an integrated sensing and communication system, where two transmitters wish to convey messages to a receiver while simultaneously estimating the state parameter sequences through echo signals. In particular, the sensing state parameters are assumed to be correlated with the channel state. In this setup, improved inner and outer bounds for capacity-distortion region are derived. The inner bound is based on an achievable scheme that combines message cooperation and joint compression of past transmitted codewords and echo signals at each transmitter, resulting in unified cooperative communication and sensing. The outer bound is based on the ideas of dependence balance for communication rate, genie-aided state estimator and rate-limited constraints on sensing distortion. The proposed inner and outer bounds are proved to improve the state-of-the-art bounds. Finally, numerical examples are provided to demonstrate that our new inner and outer bounds strictly improve the existing results. © 1963-2012 IEEE.

关键词： Signal distortion

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Near-Far Field Three-Stage Beam Training for RIS-Assisted Wideband OFDM Communications

Near-Far Field Three-Stage Beam Training for RIS-Assisted Wi...

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IEEE Conference on Wireless Communications and Networking

作者： Zhichao Cheng Shupei Zhang Shu Fu Boya Di School of Electronics Engineering and Computer Science Peking University Beijing China State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Peking University Beijing China Department of Microelectronics and Communication Engineering Chongqing University Chongqing China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

Large-scale reconfigurable intelligent surfaces (RISs) have emerged as a promising technology for signal strength enhancement and coverage extension in 6G networks. However, as the antenna scale and the bandwidth increase, large-scale RIS-assisted wideband orthogonal frequency division multiplexing (OFDM) communication systems face new challenges due to the near-field range expansion and the beam split effect, complicating the acquisition of channel state information (CSI). To tackle these challenges, in this paper, we present a wideband beam training scheme by utilizing the beam split effect to bypass the CSI acquisition. Specifically, by analyzing the beam split effect in RIS-assisted OFDM communication systems, we propose a beam-split-aware codebook capable of covering both the near and far fields. The coverage of codewords in the proposed codebook is expanded benefiting from the beam split effect, leading to fewer codewords compared to conventional narrow-band codebooks. Utilizing such a codebook, a three-stage beam training mechanism is performed to obtain the optimal codeword with low time overhead, thereby facilitating the beamforming. Simulation results demonstrate that the proposed scheme outperforms existing codebook-based beam training schemes in terms of training overhead and sum rate in the hybrid near-far field.

关键词： Training Upper bound Array signal processing OFDM Simulation Reconfigurable intelligent surfaces Broadband antennas Wideband Faces Channel state information

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Multi-Feature Interaction and Degradation Estimation Transformer for Spectral Compressive Imaging

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Li, Jiaojiao Zhu, Ding Song, Rui Xu, Haitao Li, Yunsong Du, Qian Xidian University State Key Laboratory of Integrated Service Networks Xi'an710071 China Chinese Academy of Sciences National Space Science Center China Mississippi State University Department of Electrical and Computer Engineering StarkvilleMS39762 United States

Coded Aperture Snapshot Spectral Imaging (CASSI) systems provide an efficient approach to acquiring Hyperspectral Images (HSI), yet the reconstruction process still presents challenges. Traditional Deep Unfolding networks (DUN) applied to CASSI often face constraints due to inadequate feature utilization and poor handling of multi-scale frequency-domain information, leading to the loss of image detail and global information. Furthermore, most DUN methodologies oversimplify degrading factors and fail to account for issues such as distortions found in actual imaging, thus affecting accuracy and robustness. This paper presents MIDET, a novel DUN tailored for CASSI systems, which integrates the fusion of band information, spatial information, and multi-scale information to meaningfully improve feature utilization and information interaction efficiency. Additionally, MIDET introduces a degradation-guided learning strategy and a frequency feature extraction module, enhancing the capability to handle real imaging distortions and preserve more details in HSI reconstruction. Experimental results demonstrate that MIDET significantly outperforms existing technologies on both simulated and real datasets, effectively enhancing the quality of HSI reconstruction. © 1991-2012 IEEE.

关键词： Hyperspectral imaging

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Boosting Semi-Supervised Facial Attribute Recognition with Dynamic Threshold Pairs

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Xu, Yihan Li, Hangyu Zhu, Mingrui Wang, Nannan Gao, Xinbo Xidian University State Key Laboratory of Integrated Services Networks School of Telecommunications Engineering Shaanxi Xi'an710071 China Hong Kong Baptist University TMLR Group Department of Computer Science Hong Kong Chongqing University of Posts and Telecommunications Chongqing Key Laboratory of Image Cognition Chongqing400065 China

Semi-supervised learning (SSL) has proven effective in assigning a pseudo-label to a confident sample whose largest class probability is above a fixed threshold. However, in the context of semi-supervised facial attribute recognition (SSFAR), where a sample is associated with multiple presence and absence pseudo-labels, directly applying existing SSL methods is challenging due to two issues: 1) the lack of a clear boundary between presence and absence predictions for an attribute makes it difficult to distinguish them using a single threshold;2) the learning difficulty varies across attributes, so the fixed strategy fails to adaptively learn different attributes. To address these challenges, we propose Dynamic thrEShold Pairs (DESP), a simple yet effective method to handle the SSFAR problem. Specifically, during each training stage, we derive two sets for each attribute from labeled samples, which contain the predicted probabilities of presence and absence, respectively. We then compute the mid-ranges of the two sets as paired presence and absence thresholds. Finally, we assign a presence or absence pseudo-label for the attribute to an unlabeled sample when its prediction exceeds the presence threshold or falls below the absence threshold. Extensive experiments on the CelebA and LFWA datasets demonstrate that DESP achieves superior performance compared to state-of-the-art methods, especially in the case of scarce labeled samples. Also, DESP performs well on multi-label datasets such as Pascal VOC and MS-COCO. © 1991-2012 IEEE.

关键词： Self-supervised learning

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PAC-MC: An Efficient Password-Based Access Control Framework for Time Sequence Aware Media Cloud

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IEEE Transactions on Mobile Computing 2025年

作者： Wang, Haiyan Liu, Penghui Zhong, Xiaoxiong Luo, Fucai Xiao, Bin Yang, Yuanyuan Peng Cheng Laboratory Department of New Networks Shenzhen518000 China Zhejiang Gongshang University School of Computer Science and Technology Hangzhou China Hong Kong Polytechnic University Department of Computing Hong Kong Stony Brook University Department of Electrical and Computer Engineering Stony BrookNY11794 United States

Cloud storage makes it easier for users to access and share data remotely, but it often requires integration with cryptographic technologies to address consumer-oriented applications, such as fine-grained data access, secure data sharing and retrieval. This paper focuses on the fine-grained access problem of media applications based on time sequence, that is, certain critical media applications based on time sequences should ideally be accessible only to authorized clients. The traditional keyword-based searchable encryption (SE) allows effective search and access over encrypted data while preserving data privacy, but most existing solutions do not support temporal access control (i.e., a mechanism that grants access permissions to users within a specified time range). In this paper, we propose PAC-MC, an efficient password-based access control framework for media cloud relying on content control with the time sequence attribute. PAC-MC not only supports multi-keyword search using any monotonic boolean formulas but also allows media owners to control content-encryption keys for different time periods with an updatable password. Furthermore, it supports the self-retrieval of content-encryption keys. In addition, PAC-MC is provably secure under the standard model. Finally, the detailed performance evaluation results and experimental comparisons indicate that PAC-MC is very efficient and outperforms the previous solutions in terms of computation, communication, and storage costs. © 2025 IEEE.

关键词： Access control content protection media cloud password searchable encryption time sequence

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Synesthesia of Machines (SoM)-Enhanced ISAC Precoding for Vehicular networks with Double Dynamics

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IEEE Transactions on Communications 2025年

作者： Yang, Zonghui Gao, Shijian Cheng, Xiang Yang, Liuqing Peking University State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Beijing100871 China Internet of Things Thrust Guangzhou511400 China Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong

Integrated sensing and communication (ISAC) technology is vital for vehicular networks, yet the time-varying communication channels and rapid movement of targets present significant challenges for real-time precoding design. Traditional optimization-based methods are computationally complex and strongly depend on perfect prior information, which is often unavailable in double-dynamic scenarios. In this paper, we propose a synesthesia of machine (SoM)-enhanced precoding paradigm that leverages modalities such as positioning and initial channel information to adapt to these dynamics. Utilizing a deep reinforcement learning (DRL) framework, our approach pushes ISAC performance boundaries. We also introduce a parameter-shared actor-critic architecture to accelerate training in complex state and action spaces. Extensive experiments validate the superiority of our method over existing approaches. © 1972-2012 IEEE.

关键词： Deep reinforcement learning

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Secure Wireless-Powered zeRIS Communications

arXiv

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arXiv 2025年

作者： Chen, Jingyu Cao, Kunrui Diamantoulakis, Panagiotis D. Lv, Lu Yang, Liang Chi, Haolian Ding, Haiyang School of Information and Communications National University of Defense Technology Wuhan430035 China Department of Electrical and Computer Engineering Aristotle University of Thessaloniki Thessaloniki54124 Greece State Key Laboratory of Integrated Services Networks Xidian University Xi’an710071 China College of Computer Science and Electronic Engineering Hunan University Changsha410082 China

This paper introduces the concept of wireless-powered zero-energy reconfigurable intelligent surface (zeRIS), and investigates a wireless-powered zeRIS aided communication system in terms of security, reliability and energy efficiency. In particular, we propose three new wireless-powered zeRIS modes: 1) in mode-I, N reconfigurable reflecting elements are adjusted to the optimal phase shift design of information user to maximize the reliability of the system;2) in mode-II, N reconfigurable reflecting elements are adjusted to the optimal phase shift design of cooperative jamming user to maximize the security of the system;3) in mode-III, N1 and N2 (N1+N2 = N) reconfigurable reflecting elements are respectively adjusted to the optimal phase shift designs of information user and cooperative jamming user to balance the reliability and security of the system. Then, we propose three new metrics, i.e., joint outage probability (JOP), joint intercept probability (JIP), and secrecy energy efficiency (SEE), and analyze their closed-form expressions in three modes, respectively. The results show that under high transmission power, all the diversity gains of three modes are 1, and the JOPs of mode-I, mode-II and mode-III are improved by increasing the number of zeRIS elements, which are related to N2, N, and N12, respectively. In addition, mode-I achieves the best JOP, while mode-II achieves the best JIP among three modes. We exploit two security-reliability trade-off (SRT) metrics, i.e., JOP versus JIP, and normalized joint intercept and outage probability (JIOP), to reveal the SRT performance of the proposed three modes. It is obtained that mode-II outperforms the other two modes in the JOP versus JIP, while mode-III and mode-II achieve the best performance of normalized JIOP at low and high transmission power, respectively. © 2025, CC BY.

关键词： Outages

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Contrastive MLP Network Based on Adjacent Coordinates for Cross-Domain Zero-Shot Hyperspectral Image Classification

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Li, Jiaojiao Zhang, Zhiyuan Song, Rui Xu, Haitao Li, Yunsong Du, Qian Xidian University State Key Laboratory of Integrated Service Networks Xi’an710071 China CAS Key Laboratory of Spectral Imaging Technology Xi’an710119 China Chinese Academy of Sciences National Space Science Center China Mississippi State University Department of Electrical and Computer Engineering StarkvilleMS39762 United States

With the breakthrough of transfer learning and meta-learning, cross-domain few-shot hyperspectral image classification (CDFSL HSIC) technology has recently achieved satisfactory performance under limited annotations. Nevertheless, the most practical applications are zero-shot scenarios, which are intractable for CDFSL technology, such as the extraterrestrial detection scene, where unexplored objects are recognized by scientists to be more valuable for research. To conquer the zero-shot problem under domain shift, a two-stage contrastive MLP network (MAC-CDZS) is proposed, which constitutes a pioneering effort in the cross-domain zero-shot (CDZS) HSIC task. Firstly, given the remarkable performance of MLPs within a diminutive model size and their enhanced capacity for extracting spatial-spectral features of HSIs, the MLP framework has been strategically chosen as the foundational backbone of the first stage in the MAC-CDZS for facilitating efficient feature extraction. Secondly, to alleviate the potential category collapse, the second-stage fine-tuning framework is introduced, which extends the first-stage backbone by incorporating the elaborate adjacent coordinate module and contrastive learning paradigm for more harmonious classification performance. Specifically, the adjacent coordinate module is creatively designed to adequately mine the adjacent coordinates among samples for ameliorating category collapse from the perspective of grasping more reliable priors. Furthermore, a contrastive learning paradigm is innovatively constructed, comprising a Spatial Augmentation (SA) module tailored for hyperspectral patches and a construction strategy of sample pair under zero-shot conditions, which aims to boost the representation capability and alleviate the class collapse. The superior performance of the MAC-CDZS is demonstrated by experimental results on four benchmark datasets. © 2025 IEEE. All rights reserved.

关键词： Zero-shot learning

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Split Fine-Tuning for Large Language Models in Wireless networks

arXiv

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arXiv 2025年

作者： Zhang, Songge Cheng, Guoliang Huang, Xinyu Li, Zuguang Wu, Wen Song, Lingyang Shen, Xuemin School of Electronic and Computer Engineering Peking University Shenzhen518000 China Frontier Research Center Pengcheng Laboratory Shenzhen518055 China State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Peking University Beijing100871 China School of Electronic and Computer Engineering Peking University Shenzhen Graduate School Shenzhen518055 China Department of Electrical and Computer Engineering University of Waterloo WaterlooN2L 3G1 Canada

Fine-tuning is the process of adapting the pre-trained large language models (LLMs) for downstream tasks. Due to substantial parameters, fine-tuning LLMs on mobile devices demands considerable memory resources, and suffers from high communication overhead and long fine-tuning delay. In this paper, we propose an efficient LLM fine-tuning scheme in wireless networks, named Split Fine-Tuning (SFT), which can accommodate LLM fine-tuning on mobile devices. Specifically, an LLM is split into a server-side part on the edge server and a device-side part on the mobile device to satisfy the device-side memory constraint. All devices share a server-side model and perform parallel fine-tuning to reduce fine-tuning delay. In addition, to reduce significant communication overhead incurred by data exchange between devices and the edge server, we propose a data compression scheme by jointly leveraging sparsification, stochastic quantization, and lossless encoding methods. Furthermore, we formulate a fine-tuning delay minimization problem under accuracy and memory constraints, taking device heterogeneity and channel dynamics into account. To solve the problem, the nonlinear mixed-integer problem is decoupled into two subproblems in different timescales. The two-timescale resource management algorithm is proposed to jointly optimize the compression rate and transformer block allocation in the large timescale using the augmented Lagrangian method, and determine spectrum resource allocation in the small timescale via sequential quadratic programming. Extensive simulation results demonstrate that the proposed scheme can reduce the fine-tuning delay by up to 80.2% and communication overhead by 93.6% compared to state-of-the-art benchmarks, while satisfying device-side memory and model accuracy constraints. Copyright © 2025, The Authors. All rights reserved.

关键词： Quadratic programming

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