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}
Pub Date : 2026-01-05DOI: 10.1109/LCOMM.2025.3650446
Dexia Jiang;Pingzhi Fan;Jingqiu Gao
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.
{"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}
Pub Date : 2026-01-01DOI: 10.1109/LCOMM.2025.3650170
Hongmei Kang;Yidi Zhang;Yimeng Liu;Ming Jiang
In this letter, we propose a multilevel coded modulation (MLCM) scheme based on single parity check (SPC)-aided spatially coupled low-density parity-check (SC-LDPC) codes to enhance spectral efficiency. A two-level MLCM framework is designed, in which the SC-LDPC component codes are compatible with the fifth-generation new radio (5G-NR) LDPC codes, and the message length allocations are optimized through the protograph-based extrinsic information transfer (PEXIT) analysis. Besides, an SPC constraint of message bits is introduced between different SC-LDPC codes to further lower the error floor. Simulation results demonstrate that the proposed MLCM scheme achieves significant gains over the conventional 5G-NR LDPC codes using bit-interleaved coded modulation, while also notably reducing decoding complexity.
{"title":"PEXIT Analysis and Design of Multilevel Coded Modulation With SPC-Aided SC-LDPC Codes","authors":"Hongmei Kang;Yidi Zhang;Yimeng Liu;Ming Jiang","doi":"10.1109/LCOMM.2025.3650170","DOIUrl":"https://doi.org/10.1109/LCOMM.2025.3650170","url":null,"abstract":"In this letter, we propose a multilevel coded modulation (MLCM) scheme based on single parity check (SPC)-aided spatially coupled low-density parity-check (SC-LDPC) codes to enhance spectral efficiency. A two-level MLCM framework is designed, in which the SC-LDPC component codes are compatible with the fifth-generation new radio (5G-NR) LDPC codes, and the message length allocations are optimized through the protograph-based extrinsic information transfer (PEXIT) analysis. Besides, an SPC constraint of message bits is introduced between different SC-LDPC codes to further lower the error floor. Simulation results demonstrate that the proposed MLCM scheme achieves significant gains over the conventional 5G-NR LDPC codes using bit-interleaved coded modulation, while also notably reducing decoding complexity.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"30 ","pages":"772-776"},"PeriodicalIF":4.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145929389","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 : 2026-01-01DOI: 10.1109/LCOMM.2025.3650380
Ruifeng Zheng;Pengjie Zhou;Pit Hofmann;Juan A. Cabrera;Frank H. P. Fitzek
This letter presents a molecular communication receiver model grounded in Langmuir adsorption kinetics, offering a physically consistent alternative to passive and fully absorbing models. The receiver detects information molecules through reversible binding to a finite number of surface-anchored receptors (probes), thereby capturing the saturation and competition effects in realistic biosensing environments. We derive closed-form solutions for finite-duration pulse inputs under reaction-limited conditions and propose simplified asymptotic approximations for short- and long-pulse regimes, which accurately characterize the binding dynamics under limited receptor availability. An equivalent resistor–capacitor circuit analogy is introduced, mapping molecular binding and unbinding to time-varying and fixed resistances. Particle-based Monte Carlo simulations verify that the proposed model accurately captures the channel behavior and temporal memory of realistic biochemical receivers with finite receptor capacity.
{"title":"Molecular Communication With Langmuir Adsorption Kinetics: Channel Characteristics and Temporal Memory","authors":"Ruifeng Zheng;Pengjie Zhou;Pit Hofmann;Juan A. Cabrera;Frank H. P. Fitzek","doi":"10.1109/LCOMM.2025.3650380","DOIUrl":"https://doi.org/10.1109/LCOMM.2025.3650380","url":null,"abstract":"This letter presents a molecular communication receiver model grounded in Langmuir adsorption kinetics, offering a physically consistent alternative to passive and fully absorbing models. The receiver detects information molecules through reversible binding to a finite number of surface-anchored receptors (probes), thereby capturing the saturation and competition effects in realistic biosensing environments. We derive closed-form solutions for finite-duration pulse inputs under reaction-limited conditions and propose simplified asymptotic approximations for short- and long-pulse regimes, which accurately characterize the binding dynamics under limited receptor availability. An equivalent resistor–capacitor circuit analogy is introduced, mapping molecular binding and unbinding to time-varying and fixed resistances. Particle-based Monte Carlo simulations verify that the proposed model accurately captures the channel behavior and temporal memory of realistic biochemical receivers with finite receptor capacity.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"30 ","pages":"787-791"},"PeriodicalIF":4.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145929397","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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