Pub Date : 2026-01-28DOI: 10.1109/TCOMM.2026.3658383
Tingting Wang;Tengfei Liu;Ye Li;Jinwei Zhao;Ruifeng Gao;Sheng Wu;Jianping Pan
Low earth orbit (LEO) satellite networks are pivotal for sixth-generation (6G) wireless systems, yet their high-speed mobility induces frequent packet loss, causing severe head-of-line blocking delays under traditional retransmission mechanisms. While streaming forward erasure correction (FEC) can mitigate retransmissions, existing packet loss models fail to capture the unique dynamics of LEO networks, causing difficulties in the design and analysis of FEC schemes. This paper addresses this problem through the following contributions. First, based on real-world Starlink measurements, we reveal the inadequacy of conventional loss models such as those based on Markov chains. Second, we propose a Markovian arrival process (MAP) to model LEO packet loss. Using an expectation-maximization (EM) algorithm to fit Starlink traces, we demonstrate its superior accuracy over existing models. Third, based on MAP modeling, we show that the decoding delay of a typical streaming FEC scheme with fixed repair insertion intervals can be analyzed by approximating it as the busy period of a MAP/D/1 queue. Using matrix-analytic methods, we provide a numerical recipe to compute this delay. Simulations validate the precision of the model in predicting delay, offering practical guidelines for FEC design in LEO networks.
{"title":"Packet Loss Modeling and Forward Erasure Correction for LEO Satellite Networks","authors":"Tingting Wang;Tengfei Liu;Ye Li;Jinwei Zhao;Ruifeng Gao;Sheng Wu;Jianping Pan","doi":"10.1109/TCOMM.2026.3658383","DOIUrl":"10.1109/TCOMM.2026.3658383","url":null,"abstract":"Low earth orbit (LEO) satellite networks are pivotal for sixth-generation (6G) wireless systems, yet their high-speed mobility induces frequent packet loss, causing severe head-of-line blocking delays under traditional retransmission mechanisms. While streaming forward erasure correction (FEC) can mitigate retransmissions, existing packet loss models fail to capture the unique dynamics of LEO networks, causing difficulties in the design and analysis of FEC schemes. This paper addresses this problem through the following contributions. First, based on real-world Starlink measurements, we reveal the inadequacy of conventional loss models such as those based on Markov chains. Second, we propose a Markovian arrival process (MAP) to model LEO packet loss. Using an expectation-maximization (EM) algorithm to fit Starlink traces, we demonstrate its superior accuracy over existing models. Third, based on MAP modeling, we show that the decoding delay of a typical streaming FEC scheme with fixed repair insertion intervals can be analyzed by approximating it as the busy period of a MAP/D/1 queue. Using matrix-analytic methods, we provide a numerical recipe to compute this delay. Simulations validate the precision of the model in predicting delay, offering practical guidelines for FEC design in LEO networks.","PeriodicalId":13041,"journal":{"name":"IEEE Transactions on Communications","volume":"74 ","pages":"3999-4013"},"PeriodicalIF":8.3,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146070317","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2026-01-28DOI: 10.1109/tcomm.2026.3658400
Zhiyang Li, Lin Mei, Mark F. Flanagan
{"title":"Enhanced Carrier Mode Shift Keying","authors":"Zhiyang Li, Lin Mei, Mark F. Flanagan","doi":"10.1109/tcomm.2026.3658400","DOIUrl":"https://doi.org/10.1109/tcomm.2026.3658400","url":null,"abstract":"","PeriodicalId":13041,"journal":{"name":"IEEE Transactions on Communications","volume":"105 1","pages":""},"PeriodicalIF":8.3,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146070308","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2026-01-28DOI: 10.1109/tcomm.2026.3658342
Zihan Zhang, Xiaoling Hu, Xiaowei Qian, Chenxi Liu
{"title":"Satellite Selection and Communication Window Prediction for Metasurface-Enabled Satellite Communication Systems","authors":"Zihan Zhang, Xiaoling Hu, Xiaowei Qian, Chenxi Liu","doi":"10.1109/tcomm.2026.3658342","DOIUrl":"https://doi.org/10.1109/tcomm.2026.3658342","url":null,"abstract":"","PeriodicalId":13041,"journal":{"name":"IEEE Transactions on Communications","volume":"7 1","pages":""},"PeriodicalIF":8.3,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146070326","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2026-01-28DOI: 10.1109/tcomm.2026.3658401
Niloofar Okati, Andre Noll Barreto, Luis Uzeda Garcia, Jeroen Wigard
{"title":"Co-existence Analysis of Terrestrial and Non-Terrestrial Networks in S-band Using Stochastic Geometry","authors":"Niloofar Okati, Andre Noll Barreto, Luis Uzeda Garcia, Jeroen Wigard","doi":"10.1109/tcomm.2026.3658401","DOIUrl":"https://doi.org/10.1109/tcomm.2026.3658401","url":null,"abstract":"","PeriodicalId":13041,"journal":{"name":"IEEE Transactions on Communications","volume":"42 1","pages":""},"PeriodicalIF":8.3,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146070708","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2026-01-28DOI: 10.1109/tcomm.2026.3658386
Antonino Favano, Luca Barletta, Marco Sforzin, Paolo Amato, Marco Ferrari
{"title":"Low-Complexity Detection for Balanced Codes in AWGN Channels with Offset","authors":"Antonino Favano, Luca Barletta, Marco Sforzin, Paolo Amato, Marco Ferrari","doi":"10.1109/tcomm.2026.3658386","DOIUrl":"https://doi.org/10.1109/tcomm.2026.3658386","url":null,"abstract":"","PeriodicalId":13041,"journal":{"name":"IEEE Transactions on Communications","volume":"30 1","pages":""},"PeriodicalIF":8.3,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146070322","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jamming in some cases observed at its transmitting side could be modeled as a constant amplitude but random phase signal: $J(t) = A_{J}cdot e^{jphi (t)}$ . Such situation may happen in single-tone or multi-tone jammed frequency hopping systems, in cellular mobile systems under co-channel interference, etc. In order to appropriately handle this kind of jamming through channel coding technologies, a jamming model based log-likelihood ratio (LLR) initialization method is proposed to replace their conventional AWGN model based LLR initialization method. Accordingly, the optimal LLR initialization formula is derived based on the analysis of the probability density function of jammed signals. Then, the approximation of optimal LLR initialization as well as its robust variant are further provided, hence the complexity is significantly reduced and only the signal-to-noise ratio (SNR) and signal-to-jamming ratio (SJR) are required as the a priori knowledge. The proposed LLR initialization method is tested in a Polar Code (PC) coded Orthogonal Frequency Division Multiplexing (OFDM) system, where both the AWGN and the Rayleigh fading channel models are considered. The obtained simulation results demonstrate that the proposed method outperforms the conventional Gaussian model based one and other existed robust initialization methods.
{"title":"A Robust LLR Initialization Method for Combating Constant Amplitude Random Phase Jamming","authors":"Yunzhi Wu;Li Li;Pingzhi Fan;Xianfu Lei;Xiaohu Tang","doi":"10.1109/TCOMM.2026.3658377","DOIUrl":"10.1109/TCOMM.2026.3658377","url":null,"abstract":"Jamming in some cases observed at its transmitting side could be modeled as a constant amplitude but random phase signal: <inline-formula> <tex-math>$J(t) = A_{J}cdot e^{jphi (t)}$ </tex-math></inline-formula>. Such situation may happen in single-tone or multi-tone jammed frequency hopping systems, in cellular mobile systems under co-channel interference, etc. In order to appropriately handle this kind of jamming through channel coding technologies, a jamming model based log-likelihood ratio (LLR) initialization method is proposed to replace their conventional AWGN model based LLR initialization method. Accordingly, the optimal LLR initialization formula is derived based on the analysis of the probability density function of jammed signals. Then, the approximation of optimal LLR initialization as well as its robust variant are further provided, hence the complexity is significantly reduced and only the signal-to-noise ratio (SNR) and signal-to-jamming ratio (SJR) are required as the a priori knowledge. The proposed LLR initialization method is tested in a Polar Code (PC) coded Orthogonal Frequency Division Multiplexing (OFDM) system, where both the AWGN and the Rayleigh fading channel models are considered. The obtained simulation results demonstrate that the proposed method outperforms the conventional Gaussian model based one and other existed robust initialization methods.","PeriodicalId":13041,"journal":{"name":"IEEE Transactions on Communications","volume":"74 ","pages":"4061-4077"},"PeriodicalIF":8.3,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146070311","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2026-01-28DOI: 10.1109/TCOMM.2026.3658933
Hongwei Hou;Yafei Wang;Xinping Yi;Wenjin Wang;Dirk T. M. Slock;Shi Jin
The upper mid-band balances coverage and capacity for the future cellular systems and also embraces extremely large-scale multiple-input multiple-output (XL-MIMO) systems, offering enhanced spectral and energy efficiency. However, these benefits are significantly degraded under mobility due to channel aging, and further exacerbated by the unique near-field (NF) and spatial non-stationarity (SnS) propagation in such systems. To address this challenge, we propose a novel channel prediction approach that incorporates dedicated channel modeling, probabilistic representations, and Bayesian inference algorithms for this emerging scenario. Specifically, we develop tensor-structured channel models in both the spatial-frequency-temporal (SFT) and beam-delay-Doppler (BDD) domains, which capture the NF and SnS propagation effects and leverage temporal correlations among multiple snapshots for channel prediction. In this model, the factor matrices of multi-linear transformations are parameterized by BDD domain grids and SnS factors, where beam domain grids are jointly determined by angles and slopes under spatial-chirp based NF representations. To enable tractable inference, we replace these environment-dependent BDD domain grids with uniformly sampled ones, and introduce perturbation parameters in each domain to mitigate grid mismatch. We further propose a hybrid beam domain strategy that integrates angle-only sampling with slope hyperparameterization to avoid the computational burden of explicit slope sampling. On this basis, we develop tensor-structured bi-layer inference (TS-BLI) algorithm under the expectation-maximization (EM) framework, which reduces the computational complexity by leveraging the inherent separation across different domains. In the E-step, we develop the bi-layer factor graph representation to isolate the bilinear mixing in the spatial domain induced by SnS propagation, thus facilitating bi-layer iterations using approximate inference techniques. In the M-step, we leverage an alternating strategy for hyperparameter learning, with closed-form rules derived by the quadratic approximation of objective functions. Numerical simulations based on a near-practical channel simulator developed upon QuaDRiGa with SnS extensions demonstrate the superior channel prediction performance of the proposed algorithm.
{"title":"Tensor-Structured Bayesian Channel Prediction for Upper Mid-Band XL-MIMO Systems","authors":"Hongwei Hou;Yafei Wang;Xinping Yi;Wenjin Wang;Dirk T. M. Slock;Shi Jin","doi":"10.1109/TCOMM.2026.3658933","DOIUrl":"10.1109/TCOMM.2026.3658933","url":null,"abstract":"The upper mid-band balances coverage and capacity for the future cellular systems and also embraces extremely large-scale multiple-input multiple-output (XL-MIMO) systems, offering enhanced spectral and energy efficiency. However, these benefits are significantly degraded under mobility due to channel aging, and further exacerbated by the unique near-field (NF) and spatial non-stationarity (SnS) propagation in such systems. To address this challenge, we propose a novel channel prediction approach that incorporates dedicated channel modeling, probabilistic representations, and Bayesian inference algorithms for this emerging scenario. Specifically, we develop tensor-structured channel models in both the spatial-frequency-temporal (SFT) and beam-delay-Doppler (BDD) domains, which capture the NF and SnS propagation effects and leverage temporal correlations among multiple snapshots for channel prediction. In this model, the factor matrices of multi-linear transformations are parameterized by BDD domain grids and SnS factors, where beam domain grids are jointly determined by angles and slopes under spatial-chirp based NF representations. To enable tractable inference, we replace these environment-dependent BDD domain grids with uniformly sampled ones, and introduce perturbation parameters in each domain to mitigate grid mismatch. We further propose a hybrid beam domain strategy that integrates angle-only sampling with slope hyperparameterization to avoid the computational burden of explicit slope sampling. On this basis, we develop tensor-structured bi-layer inference (TS-BLI) algorithm under the expectation-maximization (EM) framework, which reduces the computational complexity by leveraging the inherent separation across different domains. In the E-step, we develop the bi-layer factor graph representation to isolate the bilinear mixing in the spatial domain induced by SnS propagation, thus facilitating bi-layer iterations using approximate inference techniques. In the M-step, we leverage an alternating strategy for hyperparameter learning, with closed-form rules derived by the quadratic approximation of objective functions. Numerical simulations based on a near-practical channel simulator developed upon QuaDRiGa with SnS extensions demonstrate the superior channel prediction performance of the proposed algorithm.","PeriodicalId":13041,"journal":{"name":"IEEE Transactions on Communications","volume":"74 ","pages":"3968-3983"},"PeriodicalIF":8.3,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146070312","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：