{"title":"Real-Time Tracking System With Partially Coupled Sources","authors":"Saeid Sadeghi Vilni;Risto Wichman","doi":"10.1109/LCOMM.2025.3588270","DOIUrl":"https://doi.org/10.1109/LCOMM.2025.3588270","url":null,"abstract":"We consider a pull-based real-time tracking system consisting of multiple partially coupled sources and a sink. The sink monitors the sources in real-time and can request an update from one source at each time instant. The sources send updates over an unreliable wireless channel. The sources are partially coupled, and updates about one source can provide partial knowledge about other sources. We study the problem of minimizing the sum of an average distortion function and a transmission cost. Since the controller is at the sink side, the controller (sink) has only partial knowledge about the source states, and thus, we model the problem as a partially observable Markov decision process (POMDP) and then cast it as a belief-MDP problem. Using the relative value iteration algorithm, we solve the problem and propose a control policy. Simulation results demonstrate the effectiveness and superiority of the proposed policy compared to two baseline policies.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"29 9","pages":"2158-2162"},"PeriodicalIF":4.4,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11078388","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145073380","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}
{"title":"A Class Incremental Learning Method With Forward-Compatible and Covariance-Aware for Specific Emitter Identification","authors":"Xiaoyu Shen;Jiang Zhang;Xiaoqiang Qiao;Zhihui Shang;Min Wang;Tao Zhang","doi":"10.1109/LCOMM.2025.3588238","DOIUrl":"https://doi.org/10.1109/LCOMM.2025.3588238","url":null,"abstract":"Specific Emitter Identification (SEI) is essential for IoT security. Due to the continuous emergence of new communication devices in the real world, SEI needs to cope with an increasing number of transmitter categories. A trained recognition model needs to possess the capability to continuously learn new devices. This letter proposes a novel class incremental learning method based on forward compatibility and covariance awareness, named FCCA. Specifically, this letter devises a virtual signal class generation approach and an integrated loss function to expand the feature space for incremental categories while preserving valid feature representations. During the incremental phase, FCCA uses a frozen feature extractor to obtain category feature embeddings and models feature covariance relationships, helping the classifier better differentiate between categories. Experimental results on benchmark datasets demonstrate that FCCA outperforms other methods. It also demonstrates excellent performance on few-shot class incremental problems.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"29 9","pages":"2153-2157"},"PeriodicalIF":4.4,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145073311","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}
{"title":"Channel Estimation for OFDM Systems Over Doubly Selective Channels Based on CEHNet","authors":"Ruochen Wang;Biyun Ma;Jiaojiao Liu;Yuehua Ding;Zhiheng Zhou","doi":"10.1109/LCOMM.2025.3588114","DOIUrl":"https://doi.org/10.1109/LCOMM.2025.3588114","url":null,"abstract":"In dynamic scenarios, time-frequency doubly selective channels challenge accurate estimation. Deep learning-based method emerges as a promising way by leveraging temporal correlation and local time-frequency features characterized by wireless channels. To enhance adaptability in dynamic channels with fewer pilots, this letter proposes a novel channel estimation algorithm based on a channel-enhanced deep Horblock network (CEHNet), where the Horblock structure is integrated into the super-resolution convolutional neural network (SRCNN) to capture long-range dependencies effectively. Additionally, the autocorrelation of the channel state information (CSI) matrix, derived from pilot signals, is fed into CEHNet in parallel, thereby emphasizing multipath delay and Doppler frequency shift information therein. Furthermore, the incorporation of Lasso regression accelerates network convergence. Experimental results demonstrate that the proposed algorithm outperforms baseline methods in various scenarios, achieving superior performance with fewer epochs, particularly when pilots are sparse or missing.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"29 9","pages":"2148-2152"},"PeriodicalIF":4.4,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145073188","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}
David R. Nickel;Anindya Bijoy Das;David J. Love;Christopher G. Brinton
{"title":"Learning-Based Two-Way Communications: Algorithmic Framework and Comparative Analysis","authors":"David R. Nickel;Anindya Bijoy Das;David J. Love;Christopher G. Brinton","doi":"10.1109/LCOMM.2025.3588133","DOIUrl":"https://doi.org/10.1109/LCOMM.2025.3588133","url":null,"abstract":"Machine learning (ML)-based feedback channel coding has garnered significant research interest in the past few years. However, there has been limited research exploring ML approaches in the so-called “two-way” setting where two users jointly encode messages and feedback over a shared channel. In this work, we present a general architecture for ML-based two-way feedback coding, and show how several popular one-way schemes can be converted to the two-way setting through our algorithmic framework. We compare such schemes against one-way counterparts, revealing error-rate benefits of ML-based two-way coding in certain signal-to-noise ratio (SNR) regimes. We then analyze the tradeoffs between error performance and computational overhead for three state-of-the-art neural network coding models instantiated in the two-way paradigm.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"29 9","pages":"2133-2137"},"PeriodicalIF":4.4,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145073285","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}
Mu Niu;Pinchang Zhang;Ji He;Yuanyu Zhang;Zhiquan Liu
{"title":"PHY-Layer Authentication Exploiting Spatial Channel and Radiometric Signatures for mmWave MIMO Systems","authors":"Mu Niu;Pinchang Zhang;Ji He;Yuanyu Zhang;Zhiquan Liu","doi":"10.1109/LCOMM.2025.3587066","DOIUrl":"https://doi.org/10.1109/LCOMM.2025.3587066","url":null,"abstract":"This letter presents a robust physical-layer (PHY-layer) authentication framework for millimeter-wave (mmWave) multiple-input multiple-output (MIMO) systems by jointly exploiting spatial channel features and hardware-induced impairments. A CANDECOMP/PARAFAC (CP) tensor decomposition is employed to extract path, angle, and array error features, which are individually classified via binary hypothesis testing. The final decision is obtained through weighted fusion. Closed-form expressions for false alarm and detection probabilities are derived, and simulations confirm the method’s high accuracy and robustness under various spoofing attacks.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"29 9","pages":"2108-2112"},"PeriodicalIF":4.4,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145100361","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}
{"title":"An Efficient Method to Estimate the Embedded Element Efficiency: A Key Parameter in Large-Scale Array Communications","authors":"Yongxi Liu;Ming Zhang;Xiaoming Chen;Anxue Zhang","doi":"10.1109/LCOMM.2025.3588004","DOIUrl":"https://doi.org/10.1109/LCOMM.2025.3588004","url":null,"abstract":"The embedded element efficiency (EEE), which quantifies mutual coupling in antenna arrays, critically limits the signal-to-noise ratio in large-scale array communications. However, calculating the EEE for finite arrays remains challenging. Existing methods are either computationally expensive or fail to account for edge effects in finite arrays. In this letter, an efficient method to estimate the EEE for finite arrays is proposed. We first derive the impedance matrix of an array from the pattern overlap matrix, then compute the generalized scattering parameters for a given source network. Consequently, the EEE of each element can be determined. Compared with traditional approaches, this method captures the EEE variations across different elements while maintaining high computational efficiency, and is applicable to arrays with arbitrary geometries. Channel capacity is evaluated to illustrate the impact of EEE on multiple-input multiple-output (MIMO) systems.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"29 9","pages":"2123-2127"},"PeriodicalIF":4.4,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145073284","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}
{"title":"A Novel Framework for Few-Shot RF Fingerprint Identification Using Signal Recurrence Plot and Convolutional Broad Learning Network","authors":"Hui Liu;Dongxing Zhao;Yupeng Chen","doi":"10.1109/LCOMM.2025.3588073","DOIUrl":"https://doi.org/10.1109/LCOMM.2025.3588073","url":null,"abstract":"Radio frequency fingerprint identification (RFFI) is critical for securing Internet of Things (IoT) devices and wireless communication systems. However, existing deep learning approaches often suffer a sharp degradation in accuracy when labeled data is limited. To address this issue, this letter introduces a novel RFFI method, SRP-CBL, which combines signal recurrence plots and convolutional broad learning. It converts RF time series into recurrence plots and applies convolution operations for feature extraction within the broad learning framework. By leveraging sparse connectivity and weight sharing, the model reduces complexity and improves generalization in low-label regimes. Experiments on a public dataset demonstrate that SRP-CBL consistently outperforms state-of-the-art methods in accuracy under limited training data. The dataset can be downloaded from <uri>https://cores.ee.ucla.edu/downloads/datasets/wisig/</uri>","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"29 9","pages":"2128-2132"},"PeriodicalIF":4.4,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145073143","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}
Wenyu Zhang;Zhiwei Xie;Sherali Zeadally;Kaiyuan Bai;Hua Shao;Haijun Zhang;Victor C. M. Leung
{"title":"PSC: A Packet Semantic Communication Framework for Lossy Transmission With Packet Loss Channel","authors":"Wenyu Zhang;Zhiwei Xie;Sherali Zeadally;Kaiyuan Bai;Hua Shao;Haijun Zhang;Victor C. M. Leung","doi":"10.1109/LCOMM.2025.3587319","DOIUrl":"https://doi.org/10.1109/LCOMM.2025.3587319","url":null,"abstract":"Currently, semantic communication is predominantly confined to wireless communication scenarios, but not the general scenarios that transmit data packets. We propose packet semantic communication (PSC), a new semantic communication framework which achieves reliable transmissions in networks with packet loss. PSC works at the application layer, and it encodes the source data as binary data bits by using deep joint source-channel coding (DeepJSCC) and entropy coding, then transmits data as packets, and the decoder at the receiver performs the decoding process even when the packets are partially lost. We implement the proposed PSC for image transmission with a packet loss channel and compare its performance with the conventional better portable graphics (BPG) codec and learned image compression (LIC) model. The results obtained show that BPG and LIC are highly vulnerable to packet loss, but the proposed PSC can achieve strong robustness against packet loss, even when the packet loss rate (PLR) is as high as 80%.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"29 9","pages":"2113-2117"},"PeriodicalIF":4.4,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145100369","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}
{"title":"Near-Field Beamforming Design for Multi-IRS Wireless Beam Routing","authors":"Tao Wang;Changsheng You;Changchuan Yin","doi":"10.1109/LCOMM.2025.3586435","DOIUrl":"https://doi.org/10.1109/LCOMM.2025.3586435","url":null,"abstract":"This letter investigates efficient near-field beamforming designs for wireless communication systems aided by multiple extremely large-scale intelligent reflecting surfaces (Multi-XL-IRSs). Previous studies have mostly assumed far-field channel conditions, based on which angle-based beamforming is adopted for the base station (BS) and IRSs. However, this approach may lead to degraded performance in practical near-field scenarios. To address this issue, we consider near-field channel modeling and formulate an optimization problem aimed at maximizing the received power at the user equipment (UE) by jointly optimizing the beamforming at the BS and IRSs. Given the intractability under the beam training (BT)-based communication framework, the problem is revisited and reformulated. Subsequently, an efficient algorithm based on alternating optimization (AO) is proposed to solve the reformulated problem. Numerical results demonstrate that, in typical setups, our AO-based beamforming designs provide over 170% and 20% received power gains over conventional angle-based beamforming and beam focusing, respectively. Furthermore, the power scaling law derived under far-field conditions no longer holds in near-field scenarios.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"29 9","pages":"2088-2092"},"PeriodicalIF":4.4,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145100444","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}
{"title":"Enhanced Adaptive Second-Order Grid Refinement Channel Estimation for OTFS Systems","authors":"Peng Liu;Meng Tang;Hao Wang","doi":"10.1109/LCOMM.2025.3586688","DOIUrl":"https://doi.org/10.1109/LCOMM.2025.3586688","url":null,"abstract":"In orthogonal time frequency space (OTFS) modulation, the virtual grid in the delay-Doppler (DD) domain is critical for estimating fractional channel. However, the resolution of the DD grid influences both algorithmic complexity and channel estimation (CE) performance. This letter proposes an adaptive second-order grid refinement (ASGR) scheme, which effectively balances computational efficiency and CE precision. To accurately estimate the doubly fractional channel in the OTFS system, a second-order Taylor expansion is used to approximate the measurement matrix. A fast Bayesian compressive sensing (FBCS) is then employed for preliminary hyperparameter estimation to reduce computational load. Subsequently, the pivotal parameters on the grid are adaptively adjusted, and the evolved points are inserted into the original grid to achieve local refinement. Simulation results show that the proposed ASGR achieves a performance gain of 4-6 dB over the off-grid FBCS.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"29 9","pages":"2093-2097"},"PeriodicalIF":4.4,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145100360","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}