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}
Pub Date : 2026-01-12DOI: 10.1109/LCOMM.2026.3653447
Kailin Wang;Guoyu Ma;Jingya Yang;Yiyan Ma;Mi Yang;Yunlong Lu;Guowei Shi;Bo Ai
This letter presents a code-redundancy-assisted optimization framework for tandem spreading multiple access (TSMA) systems under impulsive noise (IN). While TSMA offers low-complexity, grant-free access for massive machine-type communications (mMTC), its performance degrades in IN environments. Conventional methods optimize nonlinear clipping thresholds per segment, ignoring the global error-correction capabilities of Reed-Solomon (RS) codes. The proposed framework integrates nonlinear clipping with RS decoding constraints, leveraging code redundancy to derive the optimal clipping threshold. Simulations show significant improvements in block error rate (BLER) performance and enhanced robustness against IN with low computational complexity.
{"title":"Optimized Clipping Thresholds for Tandem Spreading Multiple Access in 6G IoT Under Impulsive Noise","authors":"Kailin Wang;Guoyu Ma;Jingya Yang;Yiyan Ma;Mi Yang;Yunlong Lu;Guowei Shi;Bo Ai","doi":"10.1109/LCOMM.2026.3653447","DOIUrl":"https://doi.org/10.1109/LCOMM.2026.3653447","url":null,"abstract":"This letter presents a code-redundancy-assisted optimization framework for tandem spreading multiple access (TSMA) systems under impulsive noise (IN). While TSMA offers low-complexity, grant-free access for massive machine-type communications (mMTC), its performance degrades in IN environments. Conventional methods optimize nonlinear clipping thresholds per segment, ignoring the global error-correction capabilities of Reed-Solomon (RS) codes. The proposed framework integrates nonlinear clipping with RS decoding constraints, leveraging code redundancy to derive the optimal clipping threshold. Simulations show significant improvements in block error rate (BLER) performance and enhanced robustness against IN with low computational complexity.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"30 ","pages":"882-886"},"PeriodicalIF":4.4,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145982202","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.2026.3653195
Jianghao Wu;Xinyang Wu;Huijun Cao;Jingwei Zhu
Hardware-efficient carrier phase recovery (CPR) is critical for coherent optical systems employing high-order quadrature amplitude modulation. This letter proposes a low-parallelism and low-complexity CPR architecture. It introduces a parallel prefix-sum engine that exploits the sparsity of the symbol distribution, enabling the hardware parallelism to be significantly reduced without information loss. Furthermore, the architecture features a multiplication-free maximum likelihood estimation to reduce intrinsic computational complexity. Implemented in 28 nm CMOS for 32 GBaud system, the proposed CPR estimator reduces area by 51% and power by 39% compared to a conventional mVV-CT-VV baseline, achieving state-of-the-art efficiencies with only 0.37 dB signal-to-noise ratio penalty.
{"title":"A Low-Complexity Carrier Phase Recovery Architecture Using Prefix-Sum and CT-MLE for Coherent Receivers","authors":"Jianghao Wu;Xinyang Wu;Huijun Cao;Jingwei Zhu","doi":"10.1109/LCOMM.2026.3653195","DOIUrl":"https://doi.org/10.1109/LCOMM.2026.3653195","url":null,"abstract":"Hardware-efficient carrier phase recovery (CPR) is critical for coherent optical systems employing high-order quadrature amplitude modulation. This letter proposes a low-parallelism and low-complexity CPR architecture. It introduces a parallel prefix-sum engine that exploits the sparsity of the symbol distribution, enabling the hardware parallelism to be significantly reduced without information loss. Furthermore, the architecture features a multiplication-free maximum likelihood estimation to reduce intrinsic computational complexity. Implemented in 28 nm CMOS for 32 GBaud system, the proposed CPR estimator reduces area by 51% and power by 39% compared to a conventional mVV-CT-VV baseline, achieving state-of-the-art efficiencies with only 0.37 dB signal-to-noise ratio penalty.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"30 ","pages":"872-876"},"PeriodicalIF":4.4,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146026456","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.2026.3651719
Yi Zhou;Desheng Wang
Semantic communication has advanced rapidly, yet most frameworks target SISO Gaussian or Rayleigh channels, limiting deployment in practical MIMO systems. We present MIST (MIMO and Modulation-aware Image Semantic Transmission), an end-to-end framework for image semantics over MIMO. MIST uses a Swin Transformer backbone, a channel-adaptive modulation module that leverages CSI and SNR to refine latent semantics, and an adaptive channel compression stage to enhance robustness to diverse channel conditions within one model. Extensive experiments under MIMO fading show consistent gains over conventional, CNN-based, and Transformer-based baselines across multiple image resolutions.
{"title":"A Swin Transformer With Channel-Adaptive Modulation for Semantic Image Transmission Over MIMO Channels","authors":"Yi Zhou;Desheng Wang","doi":"10.1109/LCOMM.2026.3651719","DOIUrl":"https://doi.org/10.1109/LCOMM.2026.3651719","url":null,"abstract":"Semantic communication has advanced rapidly, yet most frameworks target SISO Gaussian or Rayleigh channels, limiting deployment in practical MIMO systems. We present MIST (MIMO and Modulation-aware Image Semantic Transmission), an end-to-end framework for image semantics over MIMO. MIST uses a Swin Transformer backbone, a channel-adaptive modulation module that leverages CSI and SNR to refine latent semantics, and an adaptive channel compression stage to enhance robustness to diverse channel conditions within one model. Extensive experiments under MIMO fading show consistent gains over conventional, CNN-based, and Transformer-based baselines across multiple image resolutions.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"30 ","pages":"892-896"},"PeriodicalIF":4.4,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145982214","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.2026.3651820
Long Yang;Zeyu Chai;Fanggang Wang;Zhenhan Zhao;Yuchen Zhou;Jian Chen
Deep learning has shown good performance in specific emitter identification (SEI) with sufficient labeled datasets. However, in practical deployments, labeled samples are limited, while unknown open-set emitters affect identification accuracy. Hence, we propose a few-shot open-set SEI approach based on contrastive learning approach with false negative suppression. Specifically, contrastive learning is designed to pre-train the feature extractor using sufficient unlabeled auxiliary samples. This framework solves the problem of false negatives in SEI, which otherwise degrades representation learning. Subsequently, the feature extractor is fine-tuned using a small number of labeled samples on the target domain. Additionally, adaptive threshold is used for open-set recognition. ADS-B and Wi-Fi datasets are used to evaluate the proposed approach. Compared to other state-of-the-art approaches, our proposed approach improves the few-shot SEI performance under both open-set and close-set conditions.
{"title":"Few-Shot Open-Set Specific Emitter Identification: A Contrastive Learning Approach With False Negative Suppression","authors":"Long Yang;Zeyu Chai;Fanggang Wang;Zhenhan Zhao;Yuchen Zhou;Jian Chen","doi":"10.1109/LCOMM.2026.3651820","DOIUrl":"https://doi.org/10.1109/LCOMM.2026.3651820","url":null,"abstract":"Deep learning has shown good performance in specific emitter identification (SEI) with sufficient labeled datasets. However, in practical deployments, labeled samples are limited, while unknown open-set emitters affect identification accuracy. Hence, we propose a few-shot open-set SEI approach based on contrastive learning approach with false negative suppression. Specifically, contrastive learning is designed to pre-train the feature extractor using sufficient unlabeled auxiliary samples. This framework solves the problem of false negatives in SEI, which otherwise degrades representation learning. Subsequently, the feature extractor is fine-tuned using a small number of labeled samples on the target domain. Additionally, adaptive threshold is used for open-set recognition. ADS-B and Wi-Fi datasets are used to evaluate the proposed approach. Compared to other state-of-the-art approaches, our proposed approach improves the few-shot SEI performance under both open-set and close-set conditions.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"30 ","pages":"902-906"},"PeriodicalIF":4.4,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145982212","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.2026.3652310
Okzata Recy;Bhaskara Narottama;Trung Q. Duong
This letter presents a quantum graph-based solution that leverages a quantum circuit to improve learning efficiency, towards maximizing the sum-rate of wireless communication with fluid antennas in dynamic environments. The employed quantum graph neural networks (QGNN) consists of three main blocks, including 1) a quantum encoding layer, 2) a quantum graph neural network layer, and 3) an optimizer layer, which collectively comprise the end-to-end learning workflow. The QGNN adjusts parameters through a quantum graph neural network layer, utilizing basic linear gates on a parameterized quantum circuit (PQC) platform. Additionally, the QGNN circuit is designed with shallow depth and optimized gate composition to reduce quantum resource usage and accelerate convergence during training. The results demonstrate that the proposed QGNN offers competitive performance relative to the existing PQC model. Furthermore, this letter highlights the versatility of quantum graph-based solutions for addressing dynamic, topology-aware wireless network problems.
{"title":"Topology-Aware Quantum Graph Neural Networks for Sum-Rate Maximization in Fluid Antenna Systems","authors":"Okzata Recy;Bhaskara Narottama;Trung Q. Duong","doi":"10.1109/LCOMM.2026.3652310","DOIUrl":"https://doi.org/10.1109/LCOMM.2026.3652310","url":null,"abstract":"This letter presents a quantum graph-based solution that leverages a quantum circuit to improve learning efficiency, towards maximizing the sum-rate of wireless communication with fluid antennas in dynamic environments. The employed quantum graph neural networks (QGNN) consists of three main blocks, including 1) a quantum encoding layer, 2) a quantum graph neural network layer, and 3) an optimizer layer, which collectively comprise the end-to-end learning workflow. The QGNN adjusts parameters through a quantum graph neural network layer, utilizing basic linear gates on a parameterized quantum circuit (PQC) platform. Additionally, the QGNN circuit is designed with shallow depth and optimized gate composition to reduce quantum resource usage and accelerate convergence during training. The results demonstrate that the proposed QGNN offers competitive performance relative to the existing PQC model. Furthermore, this letter highlights the versatility of quantum graph-based solutions for addressing dynamic, topology-aware wireless network problems.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"30 ","pages":"912-916"},"PeriodicalIF":4.4,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146026365","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}
Traditional specific emitter identification (SEI) often suffers from performance degradation in cross-receiver scenarios due to domain shifts caused by receiver variations. To this end, we propose a source-free domain adaptation framework, termed prototype-based source-free alignment network (PSFAN), for cross-receiver SEI. Specifically, our method leverages prototypes learned from a pre-trained source model as category feature representations to guide the alignment of the target domain by minimizing feature discrepancies, quantified using multi-kernel maximum mean discrepancy (MK-MMD). Furthermore, we enforce category consistency by constraining the target classifier with prototype-distance vectors to enhance the discriminative ability of the target model. The target model is adapted through this alignment process and subsequently deployed to recognize signals from a new receiver. Experimental results demonstrate that PSFAN significantly improves SEI performance in cross-receiver scenarios.
{"title":"PSFAN: Prototype-Based Source-Free Alignment Network for Cross-Receiver Specific Emitter Identification","authors":"Zhiling Xiao;Weijie Xiong;Guomin Sun;Huaizong Shao","doi":"10.1109/LCOMM.2026.3653205","DOIUrl":"https://doi.org/10.1109/LCOMM.2026.3653205","url":null,"abstract":"Traditional specific emitter identification (SEI) often suffers from performance degradation in cross-receiver scenarios due to domain shifts caused by receiver variations. To this end, we propose a source-free domain adaptation framework, termed prototype-based source-free alignment network (PSFAN), for cross-receiver SEI. Specifically, our method leverages prototypes learned from a pre-trained source model as category feature representations to guide the alignment of the target domain by minimizing feature discrepancies, quantified using multi-kernel maximum mean discrepancy (MK-MMD). Furthermore, we enforce category consistency by constraining the target classifier with prototype-distance vectors to enhance the discriminative ability of the target model. The target model is adapted through this alignment process and subsequently deployed to recognize signals from a new receiver. Experimental results demonstrate that PSFAN significantly improves SEI performance in cross-receiver scenarios.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"30 ","pages":"907-911"},"PeriodicalIF":4.4,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146026399","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 : 2026-01-06DOI: 10.1109/LCOMM.2026.3651608
Dehao Qiu;Hongxia Zhu;Jun Luo;Qinhan Zhou;Feng Li
Orthogonal time frequency space (OTFS) is a promising multicarrier waveform, which effectively combats high Doppler effect in high mobility communications especially in Vehicle-to-Everything (V2X) scenarios. However, phase noise (PN) generated by oscillators is a key radio frequency impairment and inevitably presents in communication systems, resulting in severe performance deterioration. To this end, we design a model-driven deep learning for OTFS detection in the presence of unknown PN in V2X communication. In particular, we propose an iterative detection algorithm in the delay-Doppler domain to diminish the inter-carrier interference (ICI) caused by PN based on variational inference theory, which constitutes an approximate probabilistic inference technique associated with variational free energy minimization. Additionally, by inducing some trainable parameters, we further develop an unfolding approach to rapidly convergence and improve performance in deep learning manner. Simulation results demonstrate that the proposed detector reveals state-of-art performance comparing with other solutions in terms of bit error ratio (BER).
{"title":"Model-Driven Deep Learning for OTFS Detection With Phase Noise in V2X Communications","authors":"Dehao Qiu;Hongxia Zhu;Jun Luo;Qinhan Zhou;Feng Li","doi":"10.1109/LCOMM.2026.3651608","DOIUrl":"https://doi.org/10.1109/LCOMM.2026.3651608","url":null,"abstract":"Orthogonal time frequency space (OTFS) is a promising multicarrier waveform, which effectively combats high Doppler effect in high mobility communications especially in Vehicle-to-Everything (V2X) scenarios. However, phase noise (PN) generated by oscillators is a key radio frequency impairment and inevitably presents in communication systems, resulting in severe performance deterioration. To this end, we design a model-driven deep learning for OTFS detection in the presence of unknown PN in V2X communication. In particular, we propose an iterative detection algorithm in the delay-Doppler domain to diminish the inter-carrier interference (ICI) caused by PN based on variational inference theory, which constitutes an approximate probabilistic inference technique associated with variational free energy minimization. Additionally, by inducing some trainable parameters, we further develop an unfolding approach to rapidly convergence and improve performance in deep learning manner. Simulation results demonstrate that the proposed detector reveals state-of-art performance comparing with other solutions in terms of bit error ratio (BER).","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"30 ","pages":"952-956"},"PeriodicalIF":4.4,"publicationDate":"2026-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146082072","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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