Pub Date : 2025-12-18DOI: 10.1109/LCOMM.2025.3645792
Jiawen Li;Yu Jin;Yonghua Wang
Spectrum sensing in 3D environments is critical for reliable autonomous aerial vehicle (AAV) communications. However, in realistic spectrum availability-heterogeneous environments, the complex spatiotemporal coupling characteristic challenges extracting both temporal and spatial features simultaneously. Therefore, this letter proposes a temporal-spatial decoupled local cooperative framework, decomposing the complex sensing task into two relatively simpler subtasks. Specifically, a composite feature representation integrating auto-correlation and cross-correlation matrices is introduced to enrich sample information. Furthermore, a multi-residual convolutional neural network (CNN) with a channel attention mechanism is designed as a universal classifier, maintaining superior nonlinear fitting capability while controlling the network scale. Experiments demonstrate that the proposed strategy achieves superior sensing performance compared to existing methods.
{"title":"Local Cooperative Sensing With Temporal–Spatial Decoupling","authors":"Jiawen Li;Yu Jin;Yonghua Wang","doi":"10.1109/LCOMM.2025.3645792","DOIUrl":"https://doi.org/10.1109/LCOMM.2025.3645792","url":null,"abstract":"Spectrum sensing in 3D environments is critical for reliable autonomous aerial vehicle (AAV) communications. However, in realistic spectrum availability-heterogeneous environments, the complex spatiotemporal coupling characteristic challenges extracting both temporal and spatial features simultaneously. Therefore, this letter proposes a temporal-spatial decoupled local cooperative framework, decomposing the complex sensing task into two relatively simpler subtasks. Specifically, a composite feature representation integrating auto-correlation and cross-correlation matrices is introduced to enrich sample information. Furthermore, a multi-residual convolutional neural network (CNN) with a channel attention mechanism is designed as a universal classifier, maintaining superior nonlinear fitting capability while controlling the network scale. Experiments demonstrate that the proposed strategy achieves superior sensing performance compared to existing methods.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"30 ","pages":"632-636"},"PeriodicalIF":4.4,"publicationDate":"2025-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145886534","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}
Physical Layer Authentication (PLA) is a promising strategy for wireless security. Most existing PLA schemes have relied on real-valued neural networks, where complex-valued channel impulse response (CIR) is processed by separating the real and imaginary components into dual-channel inputs. This conversion disrupts the inherent coupling between magnitude and phase, thereby constraining authentication accuracy. Importantly, the spatial position of each user inherently serves as a reliable identity fingerprint. In this letter, a complex-valued network-based multi-task learning (CVN-MTL) model is proposed for multi-user authentication. By leveraging the spatiotemporal characteristics of both CIR and position, the CVN-MTL model simultaneously performs user authentication and fine-grained localization. Experiment results show that the CVN-MTL model performs superiority on authentication performance and is robust to different communication scenarios.
{"title":"Deep Complex Network Architecture for Multi-User Physical Layer Authentication in Wireless Communication","authors":"Xiaoying Qiu;Xiaoyu Ma;Jinwei Yu;Wenbao Jiang;Zhaozhong Guo;Maozhi Xu","doi":"10.1109/LCOMM.2025.3645190","DOIUrl":"https://doi.org/10.1109/LCOMM.2025.3645190","url":null,"abstract":"Physical Layer Authentication (PLA) is a promising strategy for wireless security. Most existing PLA schemes have relied on real-valued neural networks, where complex-valued channel impulse response (CIR) is processed by separating the real and imaginary components into dual-channel inputs. This conversion disrupts the inherent coupling between magnitude and phase, thereby constraining authentication accuracy. Importantly, the spatial position of each user inherently serves as a reliable identity fingerprint. In this letter, a complex-valued network-based multi-task learning (CVN-MTL) model is proposed for multi-user authentication. By leveraging the spatiotemporal characteristics of both CIR and position, the CVN-MTL model simultaneously performs user authentication and fine-grained localization. Experiment results show that the CVN-MTL model performs superiority on authentication performance and is robust to different communication scenarios.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"30 ","pages":"637-641"},"PeriodicalIF":4.4,"publicationDate":"2025-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145886679","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 : 2025-12-17DOI: 10.1109/LCOMM.2025.3645348
Heng Fu;Weijian Si;Ruizhi Liu
We propose a novel deep learning (DL)-based neural network that ingeniously merges a tailored attention mechanism and wavelet transform to jointly optimize non-orthogonal pilot design and channel estimation in multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) systems. To this end, we develop a pilot designer that leverages customized attention-based layers dedicated to identifying and selecting the optimal time slots and pilot subcarrier positions within a subframe and a channel estimator incorporating specialized wavelet blocks to perform denoising on the raw channel estimates. Simulation results demonstrate that our proposed scheme significantly outperforms traditional linear estimation methods and several state-of-the-art DL-based techniques.
{"title":"A Novel Deep Learning-Based Wavelet-Assisted Joint Pilot Design and Channel Estimation for MIMO-OFDM Systems","authors":"Heng Fu;Weijian Si;Ruizhi Liu","doi":"10.1109/LCOMM.2025.3645348","DOIUrl":"https://doi.org/10.1109/LCOMM.2025.3645348","url":null,"abstract":"We propose a novel deep learning (DL)-based neural network that ingeniously merges a tailored attention mechanism and wavelet transform to jointly optimize non-orthogonal pilot design and channel estimation in multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) systems. To this end, we develop a pilot designer that leverages customized attention-based layers dedicated to identifying and selecting the optimal time slots and pilot subcarrier positions within a subframe and a channel estimator incorporating specialized wavelet blocks to perform denoising on the raw channel estimates. Simulation results demonstrate that our proposed scheme significantly outperforms traditional linear estimation methods and several state-of-the-art DL-based techniques.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"30 ","pages":"607-611"},"PeriodicalIF":4.4,"publicationDate":"2025-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145830895","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}
Free-space optical (FSO) communications technology has been widely applied in uncrewed aerial vehicle (UAV) networks to offer the ambitious large-capacity, high-security, and interference-immuned links. However, due to atmospheric disturbances at low-altitude airspace as well as flexible-mobility and jitter of the UAV platform, the FSO link between UAVs often suffers from frequent beam misalignment, leading to undesired interruption of communications. Therefore, in this letter, we conceive a UAV-to-UAV (U2U) FSO beam alignment system, where an adaptive exploration driven deep deterministic policy gradient (AED-DDPG) algorithm is proposed to enhance the FSO link quality. By jointly optimizing transmit power and divergence angle at the transmitter site, associated to the field-of-view (FoV) angle at the receiver site, the minimized outage probability can be consequently attained. Our simulation results demonstrate that the proposed method effectively improves the FSO beam alignment of the U2U link under dynamic conditions, which further enhances the robustness of the UAV-FSO system.
{"title":"Adaptive Beam Alignment for UAV Free-Space Optical Communications With Low-Altitude Dynamics Consideration","authors":"Wanting Wang;Simeng Feng;Chenyan Gao;Jinchao Qin;Baolong Li;Chao Dong;Qihui Wu","doi":"10.1109/LCOMM.2025.3644867","DOIUrl":"https://doi.org/10.1109/LCOMM.2025.3644867","url":null,"abstract":"Free-space optical (FSO) communications technology has been widely applied in uncrewed aerial vehicle (UAV) networks to offer the ambitious large-capacity, high-security, and interference-immuned links. However, due to atmospheric disturbances at low-altitude airspace as well as flexible-mobility and jitter of the UAV platform, the FSO link between UAVs often suffers from frequent beam misalignment, leading to undesired interruption of communications. Therefore, in this letter, we conceive a UAV-to-UAV (U2U) FSO beam alignment system, where an adaptive exploration driven deep deterministic policy gradient (AED-DDPG) algorithm is proposed to enhance the FSO link quality. By jointly optimizing transmit power and divergence angle at the transmitter site, associated to the field-of-view (FoV) angle at the receiver site, the minimized outage probability can be consequently attained. Our simulation results demonstrate that the proposed method effectively improves the FSO beam alignment of the U2U link under dynamic conditions, which further enhances the robustness of the UAV-FSO system.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"30 ","pages":"662-666"},"PeriodicalIF":4.4,"publicationDate":"2025-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145886680","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 : 2025-12-16DOI: 10.1109/LCOMM.2025.3645078
Yang Wang;Yin Xu;Cixiao Zhang;Zhiyong Chen;Mingzeng Dai;Haiming Wang;Bingchao Liu;Dazhi He;Meixia Tao
Reconfigurable intelligent surface (RIS) has been recognized as a promising technology for next-generation wireless communications. However, the performance of RIS-assisted systems critically depends on accurate channel state information (CSI). To address this challenge, this letter proposes a novel channel estimation method for RIS-aided millimeter-wave (mmWave) systems based on diffusion models (DMs). Specifically, the forward diffusion process of the original signal is formulated to model the received signal as a noisy observation within the framework of DMs. Subsequently, the channel estimation task is formulated as the reverse diffusion process, and a sampling algorithm based on denoising diffusion implicit models (DDIMs) is developed to enable effective inference. Furthermore, a lightweight neural network, termed BRCNet, is introduced to replace the conventional U-Net, significantly reducing the number of parameters and computational complexity. Extensive experiments conducted under various scenarios demonstrate that the proposed method consistently outperforms existing baselines.
{"title":"Channel Estimation for RIS-Assisted mmWave Systems via Diffusion Models","authors":"Yang Wang;Yin Xu;Cixiao Zhang;Zhiyong Chen;Mingzeng Dai;Haiming Wang;Bingchao Liu;Dazhi He;Meixia Tao","doi":"10.1109/LCOMM.2025.3645078","DOIUrl":"https://doi.org/10.1109/LCOMM.2025.3645078","url":null,"abstract":"Reconfigurable intelligent surface (RIS) has been recognized as a promising technology for next-generation wireless communications. However, the performance of RIS-assisted systems critically depends on accurate channel state information (CSI). To address this challenge, this letter proposes a novel channel estimation method for RIS-aided millimeter-wave (mmWave) systems based on diffusion models (DMs). Specifically, the forward diffusion process of the original signal is formulated to model the received signal as a noisy observation within the framework of DMs. Subsequently, the channel estimation task is formulated as the reverse diffusion process, and a sampling algorithm based on denoising diffusion implicit models (DDIMs) is developed to enable effective inference. Furthermore, a lightweight neural network, termed BRCNet, is introduced to replace the conventional U-Net, significantly reducing the number of parameters and computational complexity. Extensive experiments conducted under various scenarios demonstrate that the proposed method consistently outperforms existing baselines.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"30 ","pages":"597-601"},"PeriodicalIF":4.4,"publicationDate":"2025-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145830815","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 : 2025-12-16DOI: 10.1109/LCOMM.2025.3644886
Wang Liu;Qingtao Zeng;Yuanmeng Zhang;Junfei Li;Erqing Zhang;Likun Lu
In recent years, with the explosive growth of terminal-side data, semantic communication (SemCom) has emerged as a promising solution to reduce the volume of transmitted data. However, the performance of deep learning(DL)-based semantic communication systems heavily relies on the computational capabilities of intelligent devices. Motivated by this, this letter proposes a lightweight Prompt-based Deep Separable Convolution Semantic Communication model (PDSC-SemCom). Specifically, PDSC-SemCom constructs a semantic decoder based on prompt learning with deep separable convolution (DS-Conv1D) and introduces a degradation-aware clustering routing mechanism. By integrating image degradation information with semantic information, it reorders the feature sequences accordingly. Subsequently, prompts guide the lightweight DS-Conv1D to focus on processing sequence segments that are both heavily degraded and semantically rich. Experimental results demonstrate that, for both image and text transmission tasks, PDSC-SemCom achieves competitive recovery performance while maintaining low computational overhead.
{"title":"PDSC-SemCom: A Lightweight Prompt-Guided Deep Separable Convolution Semantic Communication System","authors":"Wang Liu;Qingtao Zeng;Yuanmeng Zhang;Junfei Li;Erqing Zhang;Likun Lu","doi":"10.1109/LCOMM.2025.3644886","DOIUrl":"https://doi.org/10.1109/LCOMM.2025.3644886","url":null,"abstract":"In recent years, with the explosive growth of terminal-side data, semantic communication (SemCom) has emerged as a promising solution to reduce the volume of transmitted data. However, the performance of deep learning(DL)-based semantic communication systems heavily relies on the computational capabilities of intelligent devices. Motivated by this, this letter proposes a lightweight Prompt-based Deep Separable Convolution Semantic Communication model (PDSC-SemCom). Specifically, PDSC-SemCom constructs a semantic decoder based on prompt learning with deep separable convolution (DS-Conv1D) and introduces a degradation-aware clustering routing mechanism. By integrating image degradation information with semantic information, it reorders the feature sequences accordingly. Subsequently, prompts guide the lightweight DS-Conv1D to focus on processing sequence segments that are both heavily degraded and semantically rich. Experimental results demonstrate that, for both image and text transmission tasks, PDSC-SemCom achieves competitive recovery performance while maintaining low computational overhead.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"30 ","pages":"567-571"},"PeriodicalIF":4.4,"publicationDate":"2025-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145830794","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 : 2025-12-16DOI: 10.1109/LCOMM.2025.3645100
Zhefu Wu;Tao Zhang;Yuxuan Wan;Agyemang Paul
Conventional OFDM receivers suffer performance degradation under dynamic 5G channels with high mobility and large delay spreads. Although deep learning-based receivers show promise, most existing designs emphasize frequency-domain modeling, limiting robustness in time-varying scenarios. To address this, we propose DARNet, an end-to-end receiver that directly processes time-domain signals. DARNet integrates complex-valued convolutional layers with a native sparse attention mechanism to extract and fuse time–frequency features for accurate bit recovery. Using datasets generated from 3GPP CDL channel models via the Sionna platform, evaluations show that DARNet surpasses traditional methods, achieving notable BER gains under complex channel conditions.
{"title":"DARNet: Deep Attention Receiver Network for 5G","authors":"Zhefu Wu;Tao Zhang;Yuxuan Wan;Agyemang Paul","doi":"10.1109/LCOMM.2025.3645100","DOIUrl":"https://doi.org/10.1109/LCOMM.2025.3645100","url":null,"abstract":"Conventional OFDM receivers suffer performance degradation under dynamic 5G channels with high mobility and large delay spreads. Although deep learning-based receivers show promise, most existing designs emphasize frequency-domain modeling, limiting robustness in time-varying scenarios. To address this, we propose DARNet, an end-to-end receiver that directly processes time-domain signals. DARNet integrates complex-valued convolutional layers with a native sparse attention mechanism to extract and fuse time–frequency features for accurate bit recovery. Using datasets generated from 3GPP CDL channel models via the Sionna platform, evaluations show that DARNet surpasses traditional methods, achieving notable BER gains under complex channel conditions.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"30 ","pages":"572-576"},"PeriodicalIF":4.4,"publicationDate":"2025-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145830796","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 : 2025-12-16DOI: 10.1109/LCOMM.2025.3645065
Nishant Kumar;Aditya Prakash;Sudhan Majhi;Subhabrata Paul
In this letter, channel estimation for massive multiple-input multiple-output (mMIMO) is performed by using binary zero correlation zone (ZCZ) sequences having a length in the form of a non-power of two $(2^{n+k+1}+2^{n+k-1})$ . The sequences are constructed using generalized Boolean functions (GBFs) that do not depend upon pre-existing sequences such as Hadamard sequences, complementary sequences, and complementary sets and optimally satisfy the Tang-Fan-Matsufuji bound on ZCZ sequences. The performance of MIMO channel estimation indicates that the proposed ZCZ sequences outperform those of using the existing sequences.
{"title":"New Construction of Binary ZCZ Sequences of Non-Power-of-Two Lengths for Massive MIMO Channel Estimation","authors":"Nishant Kumar;Aditya Prakash;Sudhan Majhi;Subhabrata Paul","doi":"10.1109/LCOMM.2025.3645065","DOIUrl":"https://doi.org/10.1109/LCOMM.2025.3645065","url":null,"abstract":"In this letter, channel estimation for massive multiple-input multiple-output (mMIMO) is performed by using binary zero correlation zone (ZCZ) sequences having a length in the form of a non-power of two <inline-formula> <tex-math>$(2^{n+k+1}+2^{n+k-1})$ </tex-math></inline-formula>. The sequences are constructed using generalized Boolean functions (GBFs) that do not depend upon pre-existing sequences such as Hadamard sequences, complementary sequences, and complementary sets and optimally satisfy the Tang-Fan-Matsufuji bound on ZCZ sequences. The performance of MIMO channel estimation indicates that the proposed ZCZ sequences outperform those of using the existing sequences.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"30 ","pages":"547-551"},"PeriodicalIF":4.4,"publicationDate":"2025-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145830890","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 : 2025-12-15DOI: 10.1109/LCOMM.2025.3644693
Pialy Biswas;Meik Dörpinghaus;Gerhard Fettweis
We study spike-based sensor node communication using runlength-limited (RLL) coding to encode information in the temporal distances of the spikes. For such systems integrate-and-fire time encoding machines (IF-TEMs) are considered as an energy-efficient alternative to uniform sampling analog-to-digital converters (ADCs) at the receiver. In this regard, we present a spike detector that employs an IF-TEM with periodic reset followed by a demapper calculating log-likelihood ratios of the transmitted RLL symbols. We assess the communication performance based on the achievable rate between the RLL encoder input and the RLL decoder output. A comparison to the use of 1-bit ADCs shows that the proposed spike detection enables communication at significantly lower energy per bit to noise power spectral density ratio $E_{b}/N_{0}$ .
{"title":"IF-TEM-Based Detection for Spike Communications With RLL Encoding","authors":"Pialy Biswas;Meik Dörpinghaus;Gerhard Fettweis","doi":"10.1109/LCOMM.2025.3644693","DOIUrl":"https://doi.org/10.1109/LCOMM.2025.3644693","url":null,"abstract":"We study spike-based sensor node communication using runlength-limited (RLL) coding to encode information in the temporal distances of the spikes. For such systems integrate-and-fire time encoding machines (IF-TEMs) are considered as an energy-efficient alternative to uniform sampling analog-to-digital converters (ADCs) at the receiver. In this regard, we present a spike detector that employs an IF-TEM with periodic reset followed by a demapper calculating log-likelihood ratios of the transmitted RLL symbols. We assess the communication performance based on the achievable rate between the RLL encoder input and the RLL decoder output. A comparison to the use of 1-bit ADCs shows that the proposed spike detection enables communication at significantly lower energy per bit to noise power spectral density ratio <inline-formula> <tex-math>$E_{b}/N_{0}$ </tex-math></inline-formula>.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"30 ","pages":"647-651"},"PeriodicalIF":4.4,"publicationDate":"2025-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11300874","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145886681","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}
Pub Date : 2025-12-15DOI: 10.1109/LCOMM.2025.3644397
Aolin Liu;Bowen Feng;Ke Zhang;Ye Wang;Qinyu Zhang
Error detection is a critical function of channel coding in practical communication systems. Through a combination of theoretical analysis and experimental validation, it is concluded that the undetected error rate (UER) in the high signal-to-noise ratio (SNR) regime is predominantly determined by the code weight distribution. Existing polar code constructions based on extended BCH (EBCH) codes exhibit outstanding weight distribution properties. Building on this, a conversion strategy is proposed to transform dynamic frozen bits into parity-check (PC) bits, thereby incorporating error detection capability into the designed decoder. Simulation results demonstrate that the proposed schemes outperform cyclic redundancy check (CRC) concatenated polar codes in both block error rate (BLER) and UER under high-SNR conditions.
{"title":"Design of Parity-Check Concatenated Polar Codes From EBCH Codes","authors":"Aolin Liu;Bowen Feng;Ke Zhang;Ye Wang;Qinyu Zhang","doi":"10.1109/LCOMM.2025.3644397","DOIUrl":"https://doi.org/10.1109/LCOMM.2025.3644397","url":null,"abstract":"Error detection is a critical function of channel coding in practical communication systems. Through a combination of theoretical analysis and experimental validation, it is concluded that the undetected error rate (UER) in the high signal-to-noise ratio (SNR) regime is predominantly determined by the code weight distribution. Existing polar code constructions based on extended BCH (EBCH) codes exhibit outstanding weight distribution properties. Building on this, a conversion strategy is proposed to transform dynamic frozen bits into parity-check (PC) bits, thereby incorporating error detection capability into the designed decoder. Simulation results demonstrate that the proposed schemes outperform cyclic redundancy check (CRC) concatenated polar codes in both block error rate (BLER) and UER under high-SNR conditions.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"30 ","pages":"552-556"},"PeriodicalIF":4.4,"publicationDate":"2025-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145830868","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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