发布求助
文献互助 智能选刊 最新文献

IEEE Communications Letters最新文献

筛选
英文 中文
A Low-Complexity Carrier Phase Recovery Architecture Using Prefix-Sum and CT-MLE for Coherent Receivers 基于前缀和和CT-MLE的低复杂度载波相位恢复体系
IF 4.4 3区 计算机科学
IEEE Communications Letters Pub Date : 2026-01-12 DOI: 10.1109/LCOMM.2026.3653195
Jianghao Wu;Xinyang Wu;Huijun Cao;Jingwei Zhu
{"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}
引用次数: 0
A Swin Transformer With Channel-Adaptive Modulation for Semantic Image Transmission Over MIMO Channels 基于信道自适应调制的Swin变压器在MIMO信道上的语义图像传输
IF 4.4 3区 计算机科学
IEEE Communications Letters Pub Date : 2026-01-12 DOI: 10.1109/LCOMM.2026.3651719
Yi Zhou;Desheng Wang
{"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}
引用次数: 0
Few-Shot Open-Set Specific Emitter Identification: A Contrastive Learning Approach With False Negative Suppression 少射开集特定发射器识别：一种假负抑制的对比学习方法
IF 4.4 3区 计算机科学
IEEE Communications Letters Pub Date : 2026-01-12 DOI: 10.1109/LCOMM.2026.3651820
Long Yang;Zeyu Chai;Fanggang Wang;Zhenhan Zhao;Yuchen Zhou;Jian Chen
{"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}
引用次数: 0
Topology-Aware Quantum Graph Neural Networks for Sum-Rate Maximization in Fluid Antenna Systems 流体天线系统和速率最大化的拓扑感知量子图神经网络
IF 4.4 3区 计算机科学
IEEE Communications Letters Pub Date : 2026-01-12 DOI: 10.1109/LCOMM.2026.3652310
Okzata Recy;Bhaskara Narottama;Trung Q. Duong
{"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}
引用次数: 0
PSFAN: Prototype-Based Source-Free Alignment Network for Cross-Receiver Specific Emitter Identification 基于原型的无源对准网络，用于跨接收机特定的发射器识别
IF 4.4 3区 计算机科学
IEEE Communications Letters Pub Date : 2026-01-12 DOI: 10.1109/LCOMM.2026.3653205
Zhiling Xiao;Weijie Xiong;Guomin Sun;Huaizong Shao
{"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}
引用次数: 0
Wireless Interference Recognition With Non-Exemplar Class Incremental Learning 基于非范例类增量学习的无线干扰识别
IF 4.4 3区 计算机科学
IEEE Communications Letters Pub Date : 2026-01-12 DOI: 10.1109/LCOMM.2026.3651758
Xin Huang;Mingkun Li;Pengyu Wang;Jinshu Chen;Zhaocheng Wang
{"title":"Wireless Interference Recognition With Non-Exemplar Class Incremental Learning","authors":"Xin Huang;Mingkun Li;Pengyu Wang;Jinshu Chen;Zhaocheng Wang","doi":"10.1109/LCOMM.2026.3651758","DOIUrl":"https://doi.org/10.1109/LCOMM.2026.3651758","url":null,"abstract":"When considering the challenge introduced by dynamic wireless environment, there is an urgent demand for robust wireless interference recognition (WIR) techniques that can continuously adapt to emerging interference types. While deep learning (DL) based WIR methods perform well in static environment, they suffer from catastrophic forgetting when incrementally learning new classes without access to old data. To tackle this challenge of class incremental learning (CIL), this letter proposes a non-exemplar method that employs a Gaussian mixture model (GMM) based prototype augmentation in combination with a composite time-frequency self-supervised learning (CTFSSL), termed GPA-TFSS. Specifically, the GMM based prototype augmentation could represent old class distributions and generate diverse prototypes through probabilistic sampling, while the CTFSSL strategy incorporates rotation, segment-wise shifting, and cyclic overlay augmentations to improve the feature generalization. Simulations on a 28-class interference dataset under Rician fading show that GPA-TFSS consistently outperforms existing non-exemplar methods and even surpasses a typical exemplar-based baseline, achieving a strong balance between the model’s plasticity and stability across different incremental conditions.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"30 ","pages":"1041-1045"},"PeriodicalIF":4.4,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146223716","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}
引用次数: 0
Model-Driven Deep Learning for OTFS Detection With Phase Noise in V2X Communications V2X通信中带相位噪声的OTFS检测模型驱动深度学习
IF 4.4 3区 计算机科学
IEEE Communications Letters Pub Date : 2026-01-06 DOI: 10.1109/LCOMM.2026.3651608
Dehao Qiu;Hongxia Zhu;Jun Luo;Qinhan Zhou;Feng Li
{"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}
引用次数: 0
Energy-Efficient Multi-Stream Sparse Regression RaptorQ Codes for Unsourced Random Access 无源随机接入的高效多流稀疏回归RaptorQ码
IF 4.4 3区 计算机科学
IEEE Communications Letters Pub Date : 2026-01-05 DOI: 10.1109/LCOMM.2025.3650446
Dexia Jiang;Pingzhi Fan;Jingqiu Gao
{"title":"Energy-Efficient Multi-Stream Sparse Regression RaptorQ Codes for Unsourced Random Access","authors":"Dexia Jiang;Pingzhi Fan;Jingqiu Gao","doi":"10.1109/LCOMM.2025.3650446","DOIUrl":"https://doi.org/10.1109/LCOMM.2025.3650446","url":null,"abstract":"In the letter, we propose a novel RaptorQ-based unsourced random access (URA) scheme that integrates RaptorQ codes and sparse regression codes (SPARCs) to design access schemes tailored for massive machine-type communication scenarios. The proposed scheme eliminates the need for conventional outer tree codes by employing a dynamic preamble sequence, which serves as a temporary user identifier and facilitates the stitching of data segments across sub-slots. Furthermore, a dynamic approximate message passing (AMP) algorithm is utilized to jointly recover multiple data substreams transmitted concurrently by each user within a given sub-slot. In addition, the RaptorQ-based outer code, functioning as an erasure code, is capable of simultaneously correcting both missed detection and false alarm errors in the decoded sub-segments. Simulation results demonstrate that the proposed scheme achieves improved energy efficiency when operating under moderate and large load conditions. Moreover, it exhibits a significant performance gain over existing URA schemes in additive white Gaussian noise channels.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"30 ","pages":"877-881"},"PeriodicalIF":4.4,"publicationDate":"2026-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145982209","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}
引用次数: 0
Semantic-Aware Resource Allocation for Uplink RSMA via Deep Reinforcement Learning 基于深度强化学习的上行RSMA语义感知资源分配
IF 4.4 3区 计算机科学
IEEE Communications Letters Pub Date : 2026-01-01 Epub Date: 2026-03-04 DOI: 10.1109/LCOMM.2026.3689898
Chujun Zhang;Linyu Huang;Qian Ning
{"title":"Semantic-Aware Resource Allocation for Uplink RSMA via Deep Reinforcement Learning","authors":"Chujun Zhang;Linyu Huang;Qian Ning","doi":"10.1109/LCOMM.2026.3689898","DOIUrl":"https://doi.org/10.1109/LCOMM.2026.3689898","url":null,"abstract":"Semantic communications (SemCom) and uplink rate-splitting multiple access (RSMA) are promising enablers for efficient 6G systems, yet their integration poses new resource allocation challenges due to the intrinsic coupling between semantic compression and multi-user interference management. This letter investigates semantic-aware resource allocation for an uplink RSMA system. Joint optimization of semantic compression, bandwidth allocation, and transmit power is formulated to maximize long-term task transmission efficiency. The problem is modeled as a Markov decision process and solved using a Soft Actor-Critic framework. The simulation results demonstrate robust performance gains over baseline schemes under various resource constraints.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"30 ","pages":"1910-1914"},"PeriodicalIF":4.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147937400","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}
引用次数: 0
Uniform Circular Arrays in Near-Field: Omnidirectional Coverage With Limited Capacity 近场均匀圆形阵列：有限容量的全向覆盖
IF 4.4 3区 计算机科学
IEEE Communications Letters Pub Date : 2026-01-01 Epub Date: 2026-04-29 DOI: 10.1109/LCOMM.2026.3688469
Ahmed Hussain;Asmaa Abdallah;Abdulkadir Celik;Ahmed M. Eltawil
{"title":"Uniform Circular Arrays in Near-Field: Omnidirectional Coverage With Limited Capacity","authors":"Ahmed Hussain;Asmaa Abdallah;Abdulkadir Celik;Ahmed M. Eltawil","doi":"10.1109/LCOMM.2026.3688469","DOIUrl":"https://doi.org/10.1109/LCOMM.2026.3688469","url":null,"abstract":"Recent studies suggest that uniform circular arrays (UCAs) can extend the angular coverage of the radiative near-field region. This work investigates whether such enhanced angular coverage translates into improved spatial multiplexing performance when compared to uniform linear arrays (ULAs). To more accurately delineate the effective near-field region, we introduce the effective beamfocusing Rayleigh distance (EBRD)—an angle-dependent metric that bounds the spatial region where beamfocusing remains effective. Closed-form expressions for both beamdepth and EBRD are derived for UCAs. Our analysis shows that, under a fixed antenna element count, ULAs achieve narrower beamdepth and a longer EBRD than UCAs. Conversely, under a fixed aperture length, UCAs provide slightly narrower beamdepth and a marginally longer EBRD. Simulation results further confirm that ULAs achieve a higher sum-rate under the fixed element constraint, while UCAs offer marginal performance gain under the fixed aperture constraint.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"30 ","pages":"1900-1904"},"PeriodicalIF":4.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147937576","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}
引用次数: 0
首页 上一页 下一页 尾页
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整，为了账户安全，请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们：info@booksci.cn Book学术提供免费学术资源搜索服务，方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号：604180095
Book学术官方微信
小红书