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Achievable Rate Optimization for Large Stacked Intelligent Metasurfaces Based on Statistical CSI

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arXiv 2024年

作者： Papazafeiropoulos, Anastasios Kourtessis, Pandelis Chatzinotas, Symeon Kaklamani, Dimitra I. Venieris, Iakovos S. The Communications and Intelligent Systems Research Group University of Hertfordshire HatfieldAL10 9AB United Kingdom SnT The University of Luxembourg Luxembourg The Microwave and Fiber Optics Laboratory School of Electrical and Computer Engineering National Technical University of Athens Zografou Athens15780 Greece The Intelligent Communications and Broadband Networks Laboratory School of Electrical and Computer Engineering National Technical University of Athens Zografou Athens15780 Greece

Stacked intelligent metasurface (SIM) is an emerging design that consists of multiple layers of metasurfaces. A SIM enables holographic multiple-input multiple-output (HMIMO) precoding in the wave domain, which results in the reduction of energy consumption and hardware cost. On the ground of multiuser beamforming, this letter focuses on the downlink achievable rate and its maximization. Contrary to previous works on multiuser SIM, we consider statistical channel state information (CSI) as opposed to instantaneous CSI to overcome challenges such as large overhead. Also, we examine the performance of large surfaces. We apply an alternating optimization (AO) algorithm regarding the phases of the SIM and the allocated transmit power. Simulations illustrate the performance of the considered large SIM-assisted design as well as the comparison between different CSI considerations. © 2024, CC BY.

关键词： Energy utilization

Uplink Performance Analysis of Heterogeneous Non-Terrestrial networks in Harsh Environments: A Novel Stochastic Geometry Model

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IEEE Transactions on Communications 2025年

作者： Dong, Wen-Yu Yang, Shaoshi Chen, Sheng Beijing University of Posts and Telecommunications School of Information and Communication Engineering Beijing100876 China Ministry of Education Key Laboratory of Universal Wireless Communications Beijing100876 China Ministry of Education Key Laboratory of Mathematics and Information Networks Beijing100876 China University of Southampton School of Electronics and Computer Science SouthamptonSO17 1BJ United Kingdom

In harsh environments, such as mountainous terrain, dense vegetation and urban landscapes, a single type of unmanned aerial vehicles (UAVs) may encounter challenges like flight restrictions, difficulty in task execution or increased risk. Therefore, employing multiple types of UAVs to collaborate along with satellite assistance, becomes essential in such scenarios. In this context, we present a stochastic geometry based approach for modeling the heterogeneous non-terrestrial networks (NTNs) by using the classical binomial point process and introducing a novel point process, called Matérn hard-core cluster process (MHCCP) which possesses both properties of exclusivity and clustering. Through simulations, MHCCP has been validated as a more suitable model for UAV groups composed of multiple clusters, compared with traditional point processes such as Poisson point process, binomial point process, and Poisson cluster process. This is because MHCCP ensures inter-cluster repulsion while effectively capturing the clustered distribution observed in practical scenarios. Then, taking into account the influence of terrain shadows on the aerial-satellite links in low-altitude harsh environments, we derive closed-form expressions of the outage probability and average ergodic rate for the aerial-to-satellite uplink of heterogeneous NTNs. Unlike existing studies, our analysis adopts an advanced system configuration that combines beamforming with frequency division multiple access and incorporates a shadowed-Rician fading model to accurately capture signal fading under complex environmental conditions. Furthermore, we investigate link performance in the presence of co-channel interference. Monte Carlo simulations validate that the derived closed-form solutions of the outage probability and the average ergodic rate provide a precise quantitative tool for evaluating the reliability and transmission efficiency of the aerial-satellite links, offering deeper insights into system performa

关键词： Satellite links

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

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arXiv 2024年

作者： Yang, Zonghui Gao, Shijian Cheng, Xiang Yang, Liuqing State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Peking University Beijing100871 China Guangzhou511400 China Guangzhou China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology 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 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 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. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep reinforcement learning

Hybrid Human-Artificial Intelligence Enabled Edge Caching Based on Interest Evolution 14th

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Hybrid Human-Artificial Intelligence Enabled Edge Caching Ba...

14th EAI International Conference on Mobile Multimedia Communications, Mobimedia 2021

作者： Li, Zhidu Li, Fuxiang Wu, Dapeng Wang, Honggang Wang, Ruyan School of Communication and Information Engineering Chongqing University of Posts and Telecommunications Chongqing China Key Laboratory of Optical Communication and Networks Chongqing China Key Laboratory of Ubiquitous Sensing and Networking Chongqing China Electrical and Computer Engineering Department University of Massachusetts Dartmouth Dartmouth United States

ISBN: (纸本)9783030898137

How to cache appropriable contents for users from huge amount of candidates is a challenge in edge caching network. To address this challenge, this paper studies an edge caching scheme based on user interest, where an interest extraction and evolution network is developed. Specifically, the input features are first classified and embedding. The user interest is then mined and modeled according to the user historical behaviors with the gated recurrent unit network. Thereafter, the user interest evolution process is studied by analyzing the impact of the previous interests on the current interest through an attention mechanism. The group interest model is further studied by merging user interest evolution and social relationships among contents, based on which edge caching scheme is obtained. The effectiveness of the proposed scheme is finally validated by extensive experiments with a real-world dataset. The analysis in this paper sheds new light on edge content caching from user interest evolution perspective. © 2021, ICST Institute for computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Behavioral research

Efficient and Privacy-Preserving Weighted Nearby-Fit Spatial Keyword Query in Cloud

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IEEE Internet of Things Journal 2025年

作者： Sun, Lili Lu, Rongxing Zheng, Yandong Zhang, Yonggang Tao, Yi Jilin University College of Computer Science and Technology Changchun130012 China Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Changchun130012 China University of New Brunswick Faculty of Computer Science FrederictonNBE3B 5A3 Canada Xidian University State Key Laboratory of Integrated Services Networks Xi'an710071 China Jilin University College of Software Changchun130012 China

In the modern digital landscape, integrating geographic locations and textual descriptions within a geo-textual dataset enhances location-based services (LBS) via spatial keyword queries, as these queries combine spatial and textual information to deliver more precise and personalized results. Additionally, the advent of cloud computing allows data owners to outsource data management and services to the cloud, boosting scalability but introducing efficiency challenges due to complex encryption. Although many schemes have been proposed for spatial keyword queries on encrypted geo-textual data, none supports matching a query keyword set with the keyword sets of multiple objects, a common query type in LBS. Imagine a user seeking to rent a house close to his/her workplace, with easy access to conveniences like supermarkets. By using nearby-fit spatial keyword queries, we can match the desired house with a house-type target object and its nearby amenities, offering more practical and flexible recommendations than traditional spatial keyword queries. Hence, in this paper, we introduce an efficient and privacy-preserving scheme called the Privacy-preserving Weighted Nearby-fit Spatial Keyword (PWNSK) query scheme. First, we design a target-oriented spatial keyword (TOSK) tree for data organization and a TOSK tree based WNSK query algorithm for efficient pruning by simultaneously utilizing locations, keywords, and distances from nearby objects to target objects. For privacy, we develop several protocols, including one for polynomial coefficient re-encoding, based on polynomial coefficient encoding and fully homomorphic encryption. Building on these protocols, we introduce our PWNSK scheme. A thorough security analysis confirms its robustness, while extensive experiments also showcase its effectiveness. © 2014 IEEE.

关键词： Differential privacy

Denoising Diffusion Model-Based Channel Estimation in IRS-Assisted ISAC Systems

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Denoising Diffusion Model-Based Channel Estimation in IRS-As...

International Conference on Wireless Communications and Signal Processing (WCSP)

作者： Lan Yi Jiansong Miao Tao Zhang Zhenhui Yuan Jiawen Kang Dusit Niyato Beijing Laboratory of Advanced Information Networks Beijing University of Posts and Telecommunications Beijing China School of Software Engineering Beijing Jiaotong University Beijing China School of Engineering University of Warwick UK School of Automation Guangdong University of Technology Guangzhou China School of Computer Science and Engineering Nanyang Technological University Singapore Singapore

ISBN: (数字)9798350390643

ISBN: (纸本)9798350390650

In Integrated Sensing and Communications (ISAC) systems assisted by Intelligent Reflecting Surfaces (IRS), precise channel estimation is crucial for optimal system performance and maximizing the potential benefits. The estimation problem, however, becomes intricate due to complex channel coupling and specific environmental conditions. This paper addresses these challenges by formulating channel estimation as a denoising problem and proposing a novel diffusion-based framework. This approach mitigates the impact of ill-posedness, which is typically problematic for general neural network (NN)-based techniques. By modeling the joint distribution and sampling from the learned posterior distribution, we accurately recover the channel coefficients from noisy pilot-based observations. Simulation results demonstrate significant performance improvements of the proposed method compared to both least-squares and NN-based benchmarks. Moreover, thanks to the carefully designed architecture and residual prediction strategy, the number of sampling steps in the diffusion reverse process is greatly reduced, enabling real-time CSI acquisition.

关键词： Wireless communication System performance Noise reduction Channel estimation Signal processing algorithms Estimation Artificial neural networks Integrated sensing and communication Prediction algorithms Diffusion models

SAFE: Semantic Adaptive Feature Extraction with Rate Control for 6G Wireless Communications

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arXiv 2024年

作者： Yan, Yuna Li, Lixin Zhang, Xin Lin, Wensheng Cheng, Wenchi Han, Zhu School of Electronics and Information Northwestern Polytechnical University Xi’an710129 China State Key Laboratory of Integrated Services Networks Xidian University Xi’an710071 China Department of Electrical and Computer Engineering University of Houston HoustonTX77004 United States

Most current Deep Learning-based Semantic Communication (DeepSC) systems are designed and trained exclusively for particular single-channel conditions, which restricts their adaptability and overall bandwidth utilization. To address this, we propose an innovative Semantic Adaptive Feature Extraction (SAFE) framework, which significantly improves bandwidth efficiency by allowing users to select different sub-semantic combinations based on their channel conditions. This paper also introduces three advanced learning algorithms to optimize the performance of SAFE framework as a whole. Through a series of simulation experiments, we demonstrate that the SAFE framework can effectively and adaptively extract and transmit semantics under different channel bandwidth conditions, of which effectiveness is verified through objective and subjective quality evaluations. Copyright © 2024, The Authors. All rights reserved.

关键词： Bandwidth

Multi-level Personalized Federated Learning on Heterogeneous and Long-Tailed Data

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arXiv 2024年

作者： Zhang, Rongyu Chen, Yun Wu, Chenrui Wang, Fangxin Li, Bo The Chinese University of Hong Kong Shenzhen China The Chinese University of Hong Kong Shenzhen China The Guangdong Provincial Key Laboratory of Future Networks of Intelligence China The Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong

Federated learning (FL) offers a privacy-centric distributed learning framework, enabling model training on individual clients and central aggregation without necessitating data exchange. Nonetheless, FL implementations often suffer from non-i.i.d. and long-tailed class distributions across mobile applications, e.g., autonomous vehicles, which leads models to overfitting as local training may converge to sub-optimal. In our study, we explore the impact of data heterogeneity on model bias and introduce an innovative personalized FL framework, Multi-level Personalized Federated Learning (MuPFL), which leverages the hierarchical architecture of FL to fully harness computational resources at various levels. This framework integrates three pivotal modules: Biased Activation Value Dropout (BAVD) to mitigate overfitting and accelerate training;Adaptive Cluster-based Model Update (ACMU) to refine local models ensuring coherent global aggregation;and Prior Knowledge-assisted Classifier Fine-tuning (PKCF) to bolster classification and personalize models in accord with skewed local data with shared knowledge. Extensive experiments on diverse real-world datasets for image classification and semantic segmentation validate that MuPFL consistently outperforms state-of-the-art baselines, even under extreme non-i.i.d. and long-tail conditions, which enhances accuracy by as much as 7.39% and accelerates training by up to 80% at most, marking significant advancements in both efficiency and effectiveness. Copyright © 2024, The Authors. All rights reserved.

关键词： Electronic data interchange

SC-CDM: Enhancing Quality of Image Semantic Communication with a Compact Diffusion Model

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arXiv 2024年

作者： Zhang, Kexin Li, Lixin Lin, Wensheng Yan, Yuna Cheng, Wenchi Han, Zhu School of Electronics and Information Northwestern Polytechnical University Xi’an710129 China State Key Laboratory of Integrated Services Networks Xidian University Xi’an710071 China Department of Electrical and Computer Engineering University of Houston HoustonTX77004 United States

Semantic Communication (SC) is an emerging technology that has attracted much attention in the sixth-generation (6G) mobile communication systems. However, few literature has fully considered the perceptual quality of the reconstructed image. To solve this problem, we propose a generative SC for wireless image transmission (denoted as SC-CDM). This approach leverages compact diffusion models to improve the fidelity and semantic accuracy of the images reconstructed after transmission, ensuring that the essential content is preserved even in bandwidth-constrained environments. Specifically, we aim to redesign the swin Transformer as a new backbone for efficient semantic feature extraction and compression. Next, the receiver integrates the slim prior and image reconstruction networks. Compared to traditional Diffusion Models (DMs), it leverages DMs’ robust distribution mapping capability to generate a compact condition vector, guiding image recovery, thus enhancing the perceptual details of the reconstructed images. Finally, a series of evaluation and ablation studies are conducted to validate the effectiveness and robustness of the proposed algorithm and further increase the Peak Signal-to-Noise Ratio (PSNR) by over 17% on top of CNN-based DeepJSCC. Copyright © 2024, The Authors. All rights reserved.

关键词： Image reconstruction