Pub Date : 2025-12-11DOI: 10.1109/LCOMM.2025.3639746
Lei Qian;Ang Li;Chenyan Zhang;Wenwen Jiang;Chenguang Zhang;Nuo Huang
The stochastic terminal rotations and random link blockages in visible light communication (VLC) systems can significantly degrade statistical delay performance. To address this, this letter aims to mathematically characterize the statistical delay for VLC systems. First, we propose a Markov-modulated Laplace process (MMLP) service model capturing burstiness from orientation dynamics and blockages. Then, we analytically derive the probability density function of the achievable transmission rate under dynamic channel conditions. Finally, we establish closed-form delay bounds using a unified exponential supermartingale construction. Monte Carlo simulations validate our bounds’ superior accuracy over traditional effective bandwidth/effective capacity methods, particularly in high-burstiness scenarios. Specifically, the proposed bound achieves a delay violation probability from 10-2 to 10-3 at a delay threshold of 1 ms, which meets the stringent requirements of delay-sensitive applications.
{"title":"Statistical Delay Characterization for Visible Light Communication Under Realistic Dynamics","authors":"Lei Qian;Ang Li;Chenyan Zhang;Wenwen Jiang;Chenguang Zhang;Nuo Huang","doi":"10.1109/LCOMM.2025.3639746","DOIUrl":"https://doi.org/10.1109/LCOMM.2025.3639746","url":null,"abstract":"The stochastic terminal rotations and random link blockages in visible light communication (VLC) systems can significantly degrade statistical delay performance. To address this, this letter aims to mathematically characterize the statistical delay for VLC systems. First, we propose a Markov-modulated Laplace process (MMLP) service model capturing burstiness from orientation dynamics and blockages. Then, we analytically derive the probability density function of the achievable transmission rate under dynamic channel conditions. Finally, we establish closed-form delay bounds using a unified exponential supermartingale construction. Monte Carlo simulations validate our bounds’ superior accuracy over traditional effective bandwidth/effective capacity methods, particularly in high-burstiness scenarios. Specifically, the proposed bound achieves a delay violation probability from 10-2 to 10-3 at a delay threshold of 1 ms, which meets the stringent requirements of delay-sensitive applications.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"30 ","pages":"502-506"},"PeriodicalIF":4.4,"publicationDate":"2025-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145778266","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Current semantic communication methods encode all features uniformly, however, downstream models are far more sensitive to distortions in high-frequency semantic features than in low-frequency ones. To address this issue, we propose an Ordered Hierarchical Encoding (OHE) framework for semantic image transmission. OHE employs a hierarchical encoding scheme that combine cascaded pooling with cross-attention to construct multi-scale semantic representations, effectively decoupling and extracting low-frequency and high-frequency features. Furthermore, an ordered representation mechanism with random prefix masking enforces a natural prioritization of semantic information, enabling progressive reconstruction and supporting simple, flexible rate control. Extensive experiments conducted on various datasets demonstrate that our method outperforms the baselines.
{"title":"Ordered Hierarchical Encoding for Robust Image Semantic Communication","authors":"Huogen Yang;Zhehao Zhou;Zhongmin Yang;Xianchao Zhang;Shuxiao Ye;Changheng Wang;Guangxue Yue","doi":"10.1109/LCOMM.2025.3642888","DOIUrl":"https://doi.org/10.1109/LCOMM.2025.3642888","url":null,"abstract":"Current semantic communication methods encode all features uniformly, however, downstream models are far more sensitive to distortions in high-frequency semantic features than in low-frequency ones. To address this issue, we propose an Ordered Hierarchical Encoding (OHE) framework for semantic image transmission. OHE employs a hierarchical encoding scheme that combine cascaded pooling with cross-attention to construct multi-scale semantic representations, effectively decoupling and extracting low-frequency and high-frequency features. Furthermore, an ordered representation mechanism with random prefix masking enforces a natural prioritization of semantic information, enabling progressive reconstruction and supporting simple, flexible rate control. Extensive experiments conducted on various datasets demonstrate that our method outperforms the baselines.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"30 ","pages":"532-536"},"PeriodicalIF":4.4,"publicationDate":"2025-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145830814","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-10DOI: 10.1109/LCOMM.2025.3642793
Rui Xu;Yuanhang He;Gaolei Li;Chaofeng Zhang;Jianhua Li
Remote embodied intelligence relies on the seamless transmission of rich multi-modal sensory information, particularly vision and touch, to enable intelligent agents to collaboratively perceive, interact with, and manipulate physical environments. However, transmitting such high-dimensional and heterogeneous data over wireless channels in real time poses substantial challenges in terms of bandwidth, latency, and semantic integrity. In this letter, we propose a novel vision-tactile fusion semantic communication (VT-FSC) framework tailored for remote embodied intelligence applications. By leveraging cross-modal feature fusion and attention-guided semantic compression, the proposed system transforms raw visual and tactile data into a unified low-dimensional semantic representation. This compact representation is then robustly transmitted through noisy wireless channels and decoded at the receiver to reconstruct both the visual scene and the tactile signal accurately. To ensure perceptual alignment, encoder and decoder are jointly optimized via human-in-the-loop feedback mechanisms. Experimental results in the multi-model dataset show that our method achieves up to 15% higher ST-SIM and 47% lower RMSE than baselines, validating the effectiveness of our framework in achieving high semantic compression rates and accurate perceptual reconstruction for remote embodied intelligence applications.
{"title":"VT-FSC: Vision-Tactile Fusion Semantic Communication for Remote Embodied Intelligence","authors":"Rui Xu;Yuanhang He;Gaolei Li;Chaofeng Zhang;Jianhua Li","doi":"10.1109/LCOMM.2025.3642793","DOIUrl":"https://doi.org/10.1109/LCOMM.2025.3642793","url":null,"abstract":"Remote embodied intelligence relies on the seamless transmission of rich multi-modal sensory information, particularly vision and touch, to enable intelligent agents to collaboratively perceive, interact with, and manipulate physical environments. However, transmitting such high-dimensional and heterogeneous data over wireless channels in real time poses substantial challenges in terms of bandwidth, latency, and semantic integrity. In this letter, we propose a novel vision-tactile fusion semantic communication (VT-FSC) framework tailored for remote embodied intelligence applications. By leveraging cross-modal feature fusion and attention-guided semantic compression, the proposed system transforms raw visual and tactile data into a unified low-dimensional semantic representation. This compact representation is then robustly transmitted through noisy wireless channels and decoded at the receiver to reconstruct both the visual scene and the tactile signal accurately. To ensure perceptual alignment, encoder and decoder are jointly optimized via human-in-the-loop feedback mechanisms. Experimental results in the multi-model dataset show that our method achieves up to 15% higher ST-SIM and 47% lower RMSE than baselines, validating the effectiveness of our framework in achieving high semantic compression rates and accurate perceptual reconstruction for remote embodied intelligence applications.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"30 ","pages":"582-586"},"PeriodicalIF":4.4,"publicationDate":"2025-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145830898","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Indoor WIFI localization often suffers from severe signal fluctuations and interference, limiting accuracy and stability in dynamic environments. This letter introduces a dual-model fusion framework that combines Gaussian Process Regression (GPR) and the Log-Distance Path Loss (LDPL) model to construct high-fidelity WiFi fingerprint maps and predict signals in unobserved regions. Building on these maps, we further propose a Bayesian multi-fingerprint localization algorithm that fuses three complementary fingerprints—Received Signal Strength (RSS), Signal Strength Difference (SSD), and Hyperbolic Location Fingerprint (HLF)—to enhance robustness to temporal and spatial variations. Comprehensive experiments in a large multi-floor environment show that our method reduces mean RSS prediction error to 3.09 dBm and achieves up to 20 % lower localization RMSE than state-of-the-art methods, demonstrating superior resilience to environmental dynamics.
{"title":"A Robust WIFI Localization Method for Indoor Dynamic Scenes","authors":"Jian Sun;Wei Sun;Genwei Zhang;Chenjun Tang;Song Li;Xing Zhang","doi":"10.1109/LCOMM.2025.3641829","DOIUrl":"https://doi.org/10.1109/LCOMM.2025.3641829","url":null,"abstract":"Indoor WIFI localization often suffers from severe signal fluctuations and interference, limiting accuracy and stability in dynamic environments. This letter introduces a dual-model fusion framework that combines Gaussian Process Regression (GPR) and the Log-Distance Path Loss (LDPL) model to construct high-fidelity WiFi fingerprint maps and predict signals in unobserved regions. Building on these maps, we further propose a Bayesian multi-fingerprint localization algorithm that fuses three complementary fingerprints—Received Signal Strength (RSS), Signal Strength Difference (SSD), and Hyperbolic Location Fingerprint (HLF)—to enhance robustness to temporal and spatial variations. Comprehensive experiments in a large multi-floor environment show that our method reduces mean RSS prediction error to 3.09 dBm and achieves up to 20 % lower localization RMSE than state-of-the-art methods, demonstrating superior resilience to environmental dynamics.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"30 ","pages":"537-541"},"PeriodicalIF":4.4,"publicationDate":"2025-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145830877","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-04DOI: 10.1109/LCOMM.2025.3640237
Yunfei Chen
This letter studies the effect of near field (NF) propagation on the detection of space shift keying signals when the mobile user either operates in the NF all the time or moves from the far field (FF) to the NF during operation. The performances of the maximum likelihood detectors in the NF, FF and mismatched cases are compared and also analyzed using the union bounds. Numerical results show that the NF case slightly outperforms the FF case due to the extra difference in the large-scale fading and that the mismatched case suffers from significant performance loss due to the change of propagation environment. This performance loss increases when the number of transmitting antennas, the number of receiving antennas or the signal-to-noise ratio increase. More importantly, the user location has a large impact in the mismatched case.
{"title":"Detection and Performance Analysis of Near-Field Space Shift Keying","authors":"Yunfei Chen","doi":"10.1109/LCOMM.2025.3640237","DOIUrl":"https://doi.org/10.1109/LCOMM.2025.3640237","url":null,"abstract":"This letter studies the effect of near field (NF) propagation on the detection of space shift keying signals when the mobile user either operates in the NF all the time or moves from the far field (FF) to the NF during operation. The performances of the maximum likelihood detectors in the NF, FF and mismatched cases are compared and also analyzed using the union bounds. Numerical results show that the NF case slightly outperforms the FF case due to the extra difference in the large-scale fading and that the mismatched case suffers from significant performance loss due to the change of propagation environment. This performance loss increases when the number of transmitting antennas, the number of receiving antennas or the signal-to-noise ratio increase. More importantly, the user location has a large impact in the mismatched case.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"30 ","pages":"522-526"},"PeriodicalIF":4.4,"publicationDate":"2025-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145778228","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-04DOI: 10.1109/LCOMM.2025.3640402
Kaijing Yang;Qiao Xiao;Chaofeng Wang
This study addresses transmission scheduling in underwater acoustic (UWA) networks by optimizing transmission schedule and power allocation according to time availability and channel variations. The objective is to maximize network energy efficiency (EE) while minimizing transmission latency (TL). To capture transmission and interference relationships among links, a spatial-temporal (ST) graph is designed to represent the system state. Transmission scheduling is then formulated as a sequential decision-making process (SDP), where the optimal strategy is adaptively determined based on the graph-based system representation. Network performance is optimized using graph reinforcement learning (GRL), specifically through a graph policy gradient (GPG) approach. Simulation results demonstrate that the proposed method achieves higher EE and lower TL compared to benchmark methods.
{"title":"Graph Reinforcement Learning-Based Transmission Scheduling for Underwater Acoustic Networks","authors":"Kaijing Yang;Qiao Xiao;Chaofeng Wang","doi":"10.1109/LCOMM.2025.3640402","DOIUrl":"https://doi.org/10.1109/LCOMM.2025.3640402","url":null,"abstract":"This study addresses transmission scheduling in underwater acoustic (UWA) networks by optimizing transmission schedule and power allocation according to time availability and channel variations. The objective is to maximize network energy efficiency (EE) while minimizing transmission latency (TL). To capture transmission and interference relationships among links, a spatial-temporal (ST) graph is designed to represent the system state. Transmission scheduling is then formulated as a sequential decision-making process (SDP), where the optimal strategy is adaptively determined based on the graph-based system representation. Network performance is optimized using graph reinforcement learning (GRL), specifically through a graph policy gradient (GPG) approach. Simulation results demonstrate that the proposed method achieves higher EE and lower TL compared to benchmark methods.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"30 ","pages":"497-501"},"PeriodicalIF":4.4,"publicationDate":"2025-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145778229","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-03DOI: 10.1109/LCOMM.2025.3639928
Leandro R. Ximenes;Igor S. C. Rodrigues;Miguel S. Pinheiro
This letter presents a unified framework to achieve space-time-color coding for screen-to-camera (S2C) communication. The novel approach integrates color-shift keying (CSK) modulation, tensor-based S2C modeling, and the visible light communications (VLC) scheme called color-hopping space-time (CHST) scheme to enable high-order, flicker-free video transmission. We further propose the alternating optimization for Khatri-Rao factorization (AO-KRF) algorithm for efficient symbol detection, achieving fast convergence and low complexity. Simulation and experimental results, including real video and real screen–smartphone setups under ambient light, confirm that AO-KRF attains good performance while reducing computational cost, making the framework suitable for real-time and realistic S2C communication scenarios.
{"title":"Space-Time-Color Scheme for Screen-to-Camera Communications","authors":"Leandro R. Ximenes;Igor S. C. Rodrigues;Miguel S. Pinheiro","doi":"10.1109/LCOMM.2025.3639928","DOIUrl":"https://doi.org/10.1109/LCOMM.2025.3639928","url":null,"abstract":"This letter presents a unified framework to achieve space-time-color coding for screen-to-camera (S2C) communication. The novel approach integrates color-shift keying (CSK) modulation, tensor-based S2C modeling, and the visible light communications (VLC) scheme called color-hopping space-time (CHST) scheme to enable high-order, flicker-free video transmission. We further propose the alternating optimization for Khatri-Rao factorization (AO-KRF) algorithm for efficient symbol detection, achieving fast convergence and low complexity. Simulation and experimental results, including real video and real screen–smartphone setups under ambient light, confirm that AO-KRF attains good performance while reducing computational cost, making the framework suitable for real-time and realistic S2C communication scenarios.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"30 ","pages":"507-511"},"PeriodicalIF":4.4,"publicationDate":"2025-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11275689","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145778259","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In pulse orthogonal frequency division multiplexing (OFDM)-based integrated sensing and communication (ISAC) systems, the embedding of communication signals typically leads to elevated autocorrelation sidelobe levels (SLLs) and increased peak-to-average power ratio (PAPR). This letter proposes a sequence design framework that jointly optimizes sensing and communication performance where a PAPR constraint is incorporated to regulate signal power fluctuations. To address the resulting non-convex problem, an efficient algorithm is developed by integrating the alternating direction method of multipliers (ADMM) with coordinate descent (CD). Simulations show the method achieves low SLLs below –30 dB while ensuring robust communication reliability.
{"title":"Pulse OFDM-Based ISAC Sequence Design With Low Sidelobe Levels and PAPR Property","authors":"Jizhou Chen;Kainan Cheng;Jun Li;Jinyang He;Huiyong Li;Dezhi Wang;Ziyang Cheng","doi":"10.1109/LCOMM.2025.3639622","DOIUrl":"https://doi.org/10.1109/LCOMM.2025.3639622","url":null,"abstract":"In pulse orthogonal frequency division multiplexing (OFDM)-based integrated sensing and communication (ISAC) systems, the embedding of communication signals typically leads to elevated autocorrelation sidelobe levels (SLLs) and increased peak-to-average power ratio (PAPR). This letter proposes a sequence design framework that jointly optimizes sensing and communication performance where a PAPR constraint is incorporated to regulate signal power fluctuations. To address the resulting non-convex problem, an efficient algorithm is developed by integrating the alternating direction method of multipliers (ADMM) with coordinate descent (CD). Simulations show the method achieves low SLLs below –30 dB while ensuring robust communication reliability.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"30 ","pages":"527-531"},"PeriodicalIF":4.4,"publicationDate":"2025-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145778226","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-28DOI: 10.1109/LCOMM.2025.3638736
Yuanbo Liu;Weiyang Xu;Yucheng Wu
Existing methods for impulsive noise (IN) suppression in power line communication (PLC) suffer from error floors, particularly in frequency-selective channels. We introduce the multi-scale wavelet attention network (MSWANet), a two-stage deep learning framework to address this issue. The proposed architecture first leverages the frame preamble for initial channel estimation. The resulting channel state information (CSI) then guides the MSWANet in denoising the data symbols. MSWANet synergistically integrates wavelet transforms for efficient multiscale analysis with gated attention for precise feature fusion, allowing it to accurately separate sporadic IN from the desired signal. Validated on a public real-world noise dataset, this decoupled approach demonstrates at least 6.3 dB SINR gain over competing methods at a target bit error rate of $10^{-5}$ .
现有的电力线通信（PLC）脉冲噪声抑制方法存在误差层，特别是在频率选择信道中。我们引入了多尺度小波注意网络（MSWANet），一个两阶段深度学习框架来解决这个问题。所提出的架构首先利用帧前导进行初始信道估计。由此产生的信道状态信息（CSI）然后指导MSWANet去噪数据符号。MSWANet协同集成小波变换，用于有效的多尺度分析，并具有精确特征融合的门控注意，使其能够准确地从所需信号中分离零星的IN。在公开的真实世界噪声数据集上验证，这种解耦方法在目标误码率为10^{-5}$的情况下，比竞争方法至少获得6.3 dB SINR增益。
{"title":"A Two-Stage Network for PLC Impulsive Noise Suppression and Channel Estimation","authors":"Yuanbo Liu;Weiyang Xu;Yucheng Wu","doi":"10.1109/LCOMM.2025.3638736","DOIUrl":"https://doi.org/10.1109/LCOMM.2025.3638736","url":null,"abstract":"Existing methods for impulsive noise (IN) suppression in power line communication (PLC) suffer from error floors, particularly in frequency-selective channels. We introduce the multi-scale wavelet attention network (MSWANet), a two-stage deep learning framework to address this issue. The proposed architecture first leverages the frame preamble for initial channel estimation. The resulting channel state information (CSI) then guides the MSWANet in denoising the data symbols. MSWANet synergistically integrates wavelet transforms for efficient multiscale analysis with gated attention for precise feature fusion, allowing it to accurately separate sporadic IN from the desired signal. Validated on a public real-world noise dataset, this decoupled approach demonstrates at least 6.3 dB SINR gain over competing methods at a target bit error rate of <inline-formula> <tex-math>$10^{-5}$ </tex-math></inline-formula>.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"30 ","pages":"297-301"},"PeriodicalIF":4.4,"publicationDate":"2025-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145760927","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-27DOI: 10.1109/LCOMM.2025.3637904
Zhenhui Yang;Kechao Cai;Zhuoyue Chen;Jinbei Zhang
Quantum satellite networks enable entanglement distribution between ground stations, but existing single-satellite solutions relying solely on satellite-to-ground downlinks cannot effectively support long-distance entanglement distribution. To overcome this limitation, we propose a hybrid approach, Entanglement Distribution using Hybrid Links (ED-HL), which integrates both entanglement distribution using a single satellite (ED-SS) and entanglement distribution assisted by inter-satellite links (ED-IS), where satellites are equipped with quantum memories. The key challenge in ED-HL is to effectively coordinate satellites to distribute entanglement to ground stations via inter-satellite links and downlinks. We formulate this entanglement distribution problem as an integer programming problem, establish its submodularity, monotonicity and non-negativity properties, and design a greedy algorithm for its solution. Simulations based on real-world constellation data show that ED-HL significantly extends the entanglement distribution distance compared to single-satellite approaches.
{"title":"Optimizing Satellite-to-Ground Station Pair Assignments Using Hybrid Links for Long-Distance Quantum Entanglement Distribution","authors":"Zhenhui Yang;Kechao Cai;Zhuoyue Chen;Jinbei Zhang","doi":"10.1109/LCOMM.2025.3637904","DOIUrl":"https://doi.org/10.1109/LCOMM.2025.3637904","url":null,"abstract":"Quantum satellite networks enable entanglement distribution between ground stations, but existing single-satellite solutions relying solely on satellite-to-ground downlinks cannot effectively support long-distance entanglement distribution. To overcome this limitation, we propose a hybrid approach, Entanglement Distribution using Hybrid Links (ED-HL), which integrates both entanglement distribution using a single satellite (ED-SS) and entanglement distribution assisted by inter-satellite links (ED-IS), where satellites are equipped with quantum memories. The key challenge in ED-HL is to effectively coordinate satellites to distribute entanglement to ground stations via inter-satellite links and downlinks. We formulate this entanglement distribution problem as an integer programming problem, establish its submodularity, monotonicity and non-negativity properties, and design a greedy algorithm for its solution. Simulations based on real-world constellation data show that ED-HL significantly extends the entanglement distribution distance compared to single-satellite approaches.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"30 ","pages":"322-326"},"PeriodicalIF":4.4,"publicationDate":"2025-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145760929","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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