Pub Date : 2026-01-19DOI: 10.1109/LCOMM.2026.3654956
Mingkun Li;Pengyu Wang;Yuhan Dong;Jinshu Chen;Zhaocheng Wang
With the development of wireless communication devices, due to limited spectrum resources and rising interference, the importance of effective spectrum sensing techniques has drawn much attention. Hereby, modulation recognition serves as a cornerstone for non-cooperative communications and anti-jamming operations. While deep learning becomes popular through autonomous feature extraction, its vulnerability to adversarial attacks poses critical security risks. To address this challenge, FlowSlicer is proposed based on diffusion models for the modulation recognition domain. Furthermore, we explore a segmented recognition strategy for communication signals and propose an aggregation algorithm to enhance the modulation recognition. Simulation results validate the robustness of FlowSlicer under various adversarial attack strategies.
{"title":"Adversarial Defense in Modulation Recognition via Diffusion and Segment-Wise Classification","authors":"Mingkun Li;Pengyu Wang;Yuhan Dong;Jinshu Chen;Zhaocheng Wang","doi":"10.1109/LCOMM.2026.3654956","DOIUrl":"https://doi.org/10.1109/LCOMM.2026.3654956","url":null,"abstract":"With the development of wireless communication devices, due to limited spectrum resources and rising interference, the importance of effective spectrum sensing techniques has drawn much attention. Hereby, modulation recognition serves as a cornerstone for non-cooperative communications and anti-jamming operations. While deep learning becomes popular through autonomous feature extraction, its vulnerability to adversarial attacks poses critical security risks. To address this challenge, FlowSlicer is proposed based on diffusion models for the modulation recognition domain. Furthermore, we explore a segmented recognition strategy for communication signals and propose an aggregation algorithm to enhance the modulation recognition. Simulation results validate the robustness of FlowSlicer under various adversarial attack strategies.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"30 ","pages":"987-991"},"PeriodicalIF":4.4,"publicationDate":"2026-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175772","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 : 2026-01-19DOI: 10.1109/LCOMM.2026.3655084
Jingjing Zhao;Qingyi Huang;Kaiquan Cai;Quan Zhou;Xidong Mu;Yuanwei Liu
A point-to-point movable element (ME) enabled reconfigurable intelligent surface (ME-RIS) communication system is investigated, where each element position can be flexibly adjusted to create favorable channel conditions. For maximizing the communication rate, an efficient ME position optimization approach is proposed. Specifically, by characterizing the cascaded channel power gain in an element-wise manner, the position of each ME is iteratively updated by invoking the successive convex approximation method. Numerical results unveil that: 1) proposed element-wise ME position optimization algorithm outperforms the standard gradient ascent algorithm (GAA) which is easily trapped in local optima and 2) ME-RIS significantly improves the communication rate compared to the conventional RIS with fixed-position elements.
{"title":"Movable-Element RIS-Aided Wireless Communications: An Element-Wise Position Optimization Approach","authors":"Jingjing Zhao;Qingyi Huang;Kaiquan Cai;Quan Zhou;Xidong Mu;Yuanwei Liu","doi":"10.1109/LCOMM.2026.3655084","DOIUrl":"https://doi.org/10.1109/LCOMM.2026.3655084","url":null,"abstract":"A point-to-point movable element (ME) enabled reconfigurable intelligent surface (ME-RIS) communication system is investigated, where each element position can be flexibly adjusted to create favorable channel conditions. For maximizing the communication rate, an efficient ME position optimization approach is proposed. Specifically, by characterizing the cascaded channel power gain in an element-wise manner, the position of each ME is iteratively updated by invoking the successive convex approximation method. Numerical results unveil that: 1) proposed element-wise ME position optimization algorithm outperforms the standard gradient ascent algorithm (GAA) which is easily trapped in local optima and 2) ME-RIS significantly improves the communication rate compared to the conventional RIS with fixed-position elements.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"30 ","pages":"967-971"},"PeriodicalIF":4.4,"publicationDate":"2026-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146081996","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 : 2026-01-16DOI: 10.1109/LCOMM.2026.3651317
Qiyuan Li;Qin Huang
This letter introduces integrated interleaved (II) codes into product codes. In II codes, a shared redundancy check relationship protects the first-layer code, which helps correct errors that individual component codes cannot correct. Thus, this letter introduces integrated interleaving coding scheme to the component codes of the product code, enabling it to correct the minimum error patterns of the original product code. The simulation results show that this code outperforms existing schemes on both the binary erasure channel and the additive white Gaussian noise channel.
{"title":"Product Code With Integrated Interleaved Component Codes","authors":"Qiyuan Li;Qin Huang","doi":"10.1109/LCOMM.2026.3651317","DOIUrl":"https://doi.org/10.1109/LCOMM.2026.3651317","url":null,"abstract":"This letter introduces integrated interleaved (II) codes into product codes. In II codes, a shared redundancy check relationship protects the first-layer code, which helps correct errors that individual component codes cannot correct. Thus, this letter introduces integrated interleaving coding scheme to the component codes of the product code, enabling it to correct the minimum error patterns of the original product code. The simulation results show that this code outperforms existing schemes on both the binary erasure channel and the additive white Gaussian noise channel.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"30 ","pages":"897-901"},"PeriodicalIF":4.4,"publicationDate":"2026-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145982207","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 : 2026-01-15DOI: 10.1109/LCOMM.2025.3650674
Gehan Sathsara Vithanage;Dushantha Nalin K. Jayakody;Ioannis Krikidis
In this work a receiver-centric waveform design technique for simultaneous wireless information and power transfer (SWIPT) is proposed, eliminating the traditional trade-off between energy harvesting (EH) efficiency and information transfer (IT) integrity. By injecting pulses into the receiver, the peak-to-average power ratio (PAPR) of the received signal is increased, using diode nonlinearity to enhance EH without affecting IT. Particle swarm optimization (PSO) is used to tune the pulse parameters to obtain the maximum harvest power under practical constraints. The Monte Carlo simulation results demonstrate superior EH performance compared to existing waveform optimization schemes. The method remains robust under common IT optimizations, such as selective mapping (SLM) and partial transmit sequence (PTS), confirming its compatibility and scalability for real-world SWIPT systems.
{"title":"Receiver-Centric Waveform Design: A New Frontier in SWIPT","authors":"Gehan Sathsara Vithanage;Dushantha Nalin K. Jayakody;Ioannis Krikidis","doi":"10.1109/LCOMM.2025.3650674","DOIUrl":"https://doi.org/10.1109/LCOMM.2025.3650674","url":null,"abstract":"In this work a receiver-centric waveform design technique for simultaneous wireless information and power transfer (SWIPT) is proposed, eliminating the traditional trade-off between energy harvesting (EH) efficiency and information transfer (IT) integrity. By injecting pulses into the receiver, the peak-to-average power ratio (PAPR) of the received signal is increased, using diode nonlinearity to enhance EH without affecting IT. Particle swarm optimization (PSO) is used to tune the pulse parameters to obtain the maximum harvest power under practical constraints. The Monte Carlo simulation results demonstrate superior EH performance compared to existing waveform optimization schemes. The method remains robust under common IT optimizations, such as selective mapping (SLM) and partial transmit sequence (PTS), confirming its compatibility and scalability for real-world SWIPT systems.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"30 ","pages":"932-936"},"PeriodicalIF":4.4,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146026371","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 : 2026-01-14DOI: 10.1109/LCOMM.2026.3654375
Shaoshuai Jiang;Shufang Li;Liang Yin
Modulation recognition of radar signals is a core challenge in electronic countermeasures under complex electromagnetic environments. Deep learning demonstrates significant promise for signal analysis. Yet real-world applications often involve a broad Signal-to-Noise Ratio (SNR) range, spanning from low to high levels. Such scenarios present dual challenges: key feature loss and inflexible feature capture. To tackle these, we propose a progressive solution. It integrates a channel-level fusion mechanism for multiple time-frequency images (TFIs) and multi-scale dynamic cascaded attention (MDCA) module. First, the channel-level fusion mechanism reconstructs TFIs into combined feature maps for enhanced representation. Meanwhile, the MDCA uses variable-sized window groups across layers to capture fine-grained local and global features. Experiments on eight typical radar signals (SNR range from -21 dB to 3 dB) show the proposed method’s average accuracy outperforms traditional models. This verifies the engineering practicality of our approach in radar signal modulation recognition scenarios.
雷达信号的调制识别是复杂电磁环境下电子对抗的核心问题。深度学习在信号分析方面展示了巨大的前景。然而，现实世界的应用通常涉及广泛的信噪比（SNR）范围，从低到高。这样的场景提出了双重挑战：关键特征丢失和不灵活的特征捕获。为了解决这些问题，我们提出了一个渐进的解决方案。它集成了信道级多时频图像（tfi）融合机制和多尺度动态级联注意（MDCA）模块。首先，通道级融合机制将tfi重构为组合特征映射以增强表征。同时，MDCA跨层使用可变大小的窗口组来捕获细粒度的局部和全局特征。在8个典型雷达信号（信噪比范围为-21 dB ~ 3 dB）上的实验表明，该方法的平均精度优于传统模型。验证了该方法在雷达信号调制识别场景下的工程实用性。
{"title":"Feature Fusion Based on Multi-Scale Cascaded Attention for Radar Signal Modulation Recognition","authors":"Shaoshuai Jiang;Shufang Li;Liang Yin","doi":"10.1109/LCOMM.2026.3654375","DOIUrl":"https://doi.org/10.1109/LCOMM.2026.3654375","url":null,"abstract":"Modulation recognition of radar signals is a core challenge in electronic countermeasures under complex electromagnetic environments. Deep learning demonstrates significant promise for signal analysis. Yet real-world applications often involve a broad Signal-to-Noise Ratio (SNR) range, spanning from low to high levels. Such scenarios present dual challenges: key feature loss and inflexible feature capture. To tackle these, we propose a progressive solution. It integrates a channel-level fusion mechanism for multiple time-frequency images (TFIs) and multi-scale dynamic cascaded attention (MDCA) module. First, the channel-level fusion mechanism reconstructs TFIs into combined feature maps for enhanced representation. Meanwhile, the MDCA uses variable-sized window groups across layers to capture fine-grained local and global features. Experiments on eight typical radar signals (SNR range from -21 dB to 3 dB) show the proposed method’s average accuracy outperforms traditional models. This verifies the engineering practicality of our approach in radar signal modulation recognition scenarios.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"30 ","pages":"997-1001"},"PeriodicalIF":4.4,"publicationDate":"2026-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175689","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}
To effectively capture the inherent near-field effects and spatial non-stationarity across extremely large antenna arrays (ELAAs), this letter develops a novel analytical channel model tailored for extremely large-scale multiple-input multiple-output (XL-MIMO) systems. In the proposed framework, spatial non-stationarity is first characterized using a 0-1 diagonal matrix, after which the composite XL-MIMO channel matrix is formulated as a linear combination of structured matrix components. Leveraging this representation, we perform a comprehensive performance analysis, evaluating key metrics including the downlink ergodic capacity, an efficiently computable upper bound derived from eigenvalue matrix, and the symbol error probability (SEP). The analysis demonstrates that the proposed scheme not only achieves performance comparable to benchmark methods but also substantially reduces computational complexity. Furthermore, the analysis reveals a pronounced performance degradation in the presence of increasing channel estimation errors.
{"title":"Performance Analysis for Extremely Large-Scale MIMO Communication Systems","authors":"Yingchen Le;Zhuxian Lian;Yajun Wang;Lin Ling;Chuanjin Zu;Bibo Zhang;Xiaopei Hua","doi":"10.1109/LCOMM.2026.3654136","DOIUrl":"https://doi.org/10.1109/LCOMM.2026.3654136","url":null,"abstract":"To effectively capture the inherent near-field effects and spatial non-stationarity across extremely large antenna arrays (ELAAs), this letter develops a novel analytical channel model tailored for extremely large-scale multiple-input multiple-output (XL-MIMO) systems. In the proposed framework, spatial non-stationarity is first characterized using a 0-1 diagonal matrix, after which the composite XL-MIMO channel matrix is formulated as a linear combination of structured matrix components. Leveraging this representation, we perform a comprehensive performance analysis, evaluating key metrics including the downlink ergodic capacity, an efficiently computable upper bound derived from eigenvalue matrix, and the symbol error probability (SEP). The analysis demonstrates that the proposed scheme not only achieves performance comparable to benchmark methods but also substantially reduces computational complexity. Furthermore, the analysis reveals a pronounced performance degradation in the presence of increasing channel estimation errors.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"30 ","pages":"917-921"},"PeriodicalIF":4.4,"publicationDate":"2026-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146026346","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 : 2026-01-14DOI: 10.1109/LCOMM.2026.3654545
Wenqiang Shi;Hu Jin;Yingke Lei;Fei Teng;Jin Wang
The performance of specific emitter identification (SEI) techniques is often significantly degraded due to changes in the signal distribution of targets to be identified and the lack of labels in the data. To address the aforementioned issue, this letter proposes a SEI method based on adaptive wavelet decomposition and domain adversarial regularization (AWD2AR) for multiple cross-domain scenarios. Firstly, AWD2AR preprocesses all the received signals to obtain more separable feature representations. Subsequently, AWD2AR compels the target domain feature extractor to learn domain-invariant features. Meanwhile, a metric-based regularization term is utilized to ensure the correct matching of various classes within the domain, thereby enhancing the model’s performance on the target domain. Experimental results on different datasets demonstrate that AWD2AR outperforms the state-of-the-art algorithms in various cross-domain conditions. Furthermore, the rationality of AWD2AR has been validated through ablation experiment.
{"title":"AWD2AR: An Unsupervised Identification Framework for Specific Emitters in Diverse Cross-Domain Scenarios","authors":"Wenqiang Shi;Hu Jin;Yingke Lei;Fei Teng;Jin Wang","doi":"10.1109/LCOMM.2026.3654545","DOIUrl":"https://doi.org/10.1109/LCOMM.2026.3654545","url":null,"abstract":"The performance of specific emitter identification (SEI) techniques is often significantly degraded due to changes in the signal distribution of targets to be identified and the lack of labels in the data. To address the aforementioned issue, this letter proposes a SEI method based on adaptive wavelet decomposition and domain adversarial regularization (AWD2AR) for multiple cross-domain scenarios. Firstly, AWD2AR preprocesses all the received signals to obtain more separable feature representations. Subsequently, AWD2AR compels the target domain feature extractor to learn domain-invariant features. Meanwhile, a metric-based regularization term is utilized to ensure the correct matching of various classes within the domain, thereby enhancing the model’s performance on the target domain. Experimental results on different datasets demonstrate that AWD2AR outperforms the state-of-the-art algorithms in various cross-domain conditions. Furthermore, the rationality of AWD2AR has been validated through ablation experiment.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"30 ","pages":"957-961"},"PeriodicalIF":4.4,"publicationDate":"2026-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146082154","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}
Modulation recognition is critical in intelligent wireless communication, yet deep learning-based automatic modulation classification (AMC) models are vulnerable to adversarial attacks, posing severe risks. While adversarial detection and training offer partial mitigation, they suffer from evasion risks, signal distortion, or high latency—making them unfit for real-time systems like Unmanned Aerial Vehicle (UAV) swarms. Although generative models can purify adversarial inputs, their slow inference limits practicality. Conversely, reconstruction-based methods enable low-latency recovery but often compromise waveform fidelity. We propose a reconstruction-driven adversarial purification approach that directly restores clean signals at the input level, preserving both semantic features and physical consistency without classifier modification, ensuring high accuracy and real-time robustness. Experimental results on the RML2016.10b dataset show that our reconstruction-based method SigReconstruction achieves an average classification accuracy of 77.04% under adversarial attacks(clean accuracy of 86.68%). Reconstruction quality is corroborated by low mean squared erro (MSE) (0.0286/0.0031/0.0272) and low Fréchet Inception Distance (FID) (62.33/128.82/157.48), indicating faithful waveform recovery and feature alignment. These results demonstrate that targeted reconstruction with physical constraints offers practical, low-latency robustness for adversarially challenged wireless communications.
{"title":"A Reconstruction-Based Defense Framework for Automatic Modulation Recognition","authors":"Zhen Hong;Chenyang Song;Jinhao Wan;Chengdong Jin;Haojie Zheng;Taotao Li;Zhenyu Wen","doi":"10.1109/LCOMM.2026.3653978","DOIUrl":"https://doi.org/10.1109/LCOMM.2026.3653978","url":null,"abstract":"Modulation recognition is critical in intelligent wireless communication, yet deep learning-based automatic modulation classification (AMC) models are vulnerable to adversarial attacks, posing severe risks. While adversarial detection and training offer partial mitigation, they suffer from evasion risks, signal distortion, or high latency—making them unfit for real-time systems like Unmanned Aerial Vehicle (UAV) swarms. Although generative models can purify adversarial inputs, their slow inference limits practicality. Conversely, reconstruction-based methods enable low-latency recovery but often compromise waveform fidelity. We propose a reconstruction-driven adversarial purification approach that directly restores clean signals at the input level, preserving both semantic features and physical consistency without classifier modification, ensuring high accuracy and real-time robustness. Experimental results on the RML2016.10b dataset show that our reconstruction-based method SigReconstruction achieves an average classification accuracy of 77.04% under adversarial attacks(clean accuracy of 86.68%). Reconstruction quality is corroborated by low mean squared erro (MSE) (0.0286/0.0031/0.0272) and low Fréchet Inception Distance (FID) (62.33/128.82/157.48), indicating faithful waveform recovery and feature alignment. These results demonstrate that targeted reconstruction with physical constraints offers practical, low-latency robustness for adversarially challenged wireless communications.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"30 ","pages":"937-941"},"PeriodicalIF":4.4,"publicationDate":"2026-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146026323","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 : 2026-01-13DOI: 10.1109/LCOMM.2026.3653871
Nuwan J. G. Kankanamge;Sajjad Emdadi Mahdimahalleh;Nghi H. Tran;Khanh Pham
Channel coding represents a promising application area for neural network (NN)-based techniques. However, because the existing theoretical encoding and decoding algorithms are already highly optimized, developing NN-based methods that surpass traditional designs remains a difficult task. To address the challenge, this letter studies the recently proposed NN-based channel coding framework known as DeepPolar, with the goal of enhancing its frame error rate (FER) performance beyond that of the conventional successive cancellation (SC) decoder. Toward this goal, we introduce reliability-deweighted (RDW) top-$k$ max loss, RDW $p$ -norm loss, and RDW focal loss functions to prioritize critical bit positions during an extended training curriculum specifically designed to target FER rather than bit error rate (BER). Numerical results indicate that judicious design of these loss functions leads to a significant FER improvement of approximately 0.9 to 1 dB over the original DeepPolar code and 0.3 to 0.4 dB over the traditional polar code with SC decoding at FER level of $10^{-4}$ , depending on encoding configurations, without sacrificing BER performances. Furthermore, the proposed code designs exhibit performance close to the normal approximation of the finite blocklength capacity, operating merely 1.7 dB away. This demonstrates their considerable potential to advance NN-based polar codes.
{"title":"Improving DeepPolar Neural Codes via Reliability-Weighted FER-Centric Loss Functions","authors":"Nuwan J. G. Kankanamge;Sajjad Emdadi Mahdimahalleh;Nghi H. Tran;Khanh Pham","doi":"10.1109/LCOMM.2026.3653871","DOIUrl":"https://doi.org/10.1109/LCOMM.2026.3653871","url":null,"abstract":"Channel coding represents a promising application area for neural network (NN)-based techniques. However, because the existing theoretical encoding and decoding algorithms are already highly optimized, developing NN-based methods that surpass traditional designs remains a difficult task. To address the challenge, this letter studies the recently proposed NN-based channel coding framework known as DeepPolar, with the goal of enhancing its frame error rate (FER) performance beyond that of the conventional successive cancellation (SC) decoder. Toward this goal, we introduce reliability-deweighted (RDW) top-<inline-formula> <tex-math>$k$ </tex-math></inline-formula> max loss, RDW <inline-formula> <tex-math>$p$ </tex-math></inline-formula>-norm loss, and RDW focal loss functions to prioritize critical bit positions during an extended training curriculum specifically designed to target FER rather than bit error rate (BER). Numerical results indicate that judicious design of these loss functions leads to a significant FER improvement of approximately 0.9 to 1 dB over the original DeepPolar code and 0.3 to 0.4 dB over the traditional polar code with SC decoding at FER level of <inline-formula> <tex-math>$10^{-4}$ </tex-math></inline-formula>, depending on encoding configurations, without sacrificing BER performances. Furthermore, the proposed code designs exhibit performance close to the normal approximation of the finite blocklength capacity, operating merely 1.7 dB away. This demonstrates their considerable potential to advance NN-based polar codes.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"30 ","pages":"867-871"},"PeriodicalIF":4.4,"publicationDate":"2026-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146026342","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 : 2026-01-12DOI: 10.1109/LCOMM.2026.3651562
Peng Xu;Jiaxin Liu;An Wang;Chen Yi;Qi Li
Blind recognition of polar codes in non-cooperative scenarios remains insufficiently addressed. Although existing methods have achieved reliable performance in code length recognition, reliably identifying information bits remains challenging under moderate-to-high bit error rate (BER). In this letter, assuming the code length is known, we propose a two-stage blind information bits recognition scheme. In the first stage, multi-threshold voting judgment is performed to obtain the initial frozen set. In the second stage, we perform partial orders (POs) correction to enforce structural consistency of the frozen set. When BER ranges from 0 to 0.2, simulations show that the proposed method, at the cost of modest computational complexity, significantly improves the information bits identification accuracy compared with existing estimation and derivation-based approaches, achieving relative gains of approximately 40.0%, 20.7% and 13.8% for polar codes (32,15), (64,30), and (128,64), respectively.
{"title":"Blind Recognition of Polar Code Information Bits Based on Multi-Threshold Voting and Partial Orders","authors":"Peng Xu;Jiaxin Liu;An Wang;Chen Yi;Qi Li","doi":"10.1109/LCOMM.2026.3651562","DOIUrl":"https://doi.org/10.1109/LCOMM.2026.3651562","url":null,"abstract":"Blind recognition of polar codes in non-cooperative scenarios remains insufficiently addressed. Although existing methods have achieved reliable performance in code length recognition, reliably identifying information bits remains challenging under moderate-to-high bit error rate (BER). In this letter, assuming the code length is known, we propose a two-stage blind information bits recognition scheme. In the first stage, multi-threshold voting judgment is performed to obtain the initial frozen set. In the second stage, we perform partial orders (POs) correction to enforce structural consistency of the frozen set. When BER ranges from 0 to 0.2, simulations show that the proposed method, at the cost of modest computational complexity, significantly improves the information bits identification accuracy compared with existing estimation and derivation-based approaches, achieving relative gains of approximately 40.0%, 20.7% and 13.8% for polar codes (32,15), (64,30), and (128,64), respectively.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"30 ","pages":"887-891"},"PeriodicalIF":4.4,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145982281","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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