IEEE Transactions on Molecular, Biological, and Multi-Scale Communications最新文献

{"title":"A Distributed Scheduling Algorithm for TDMA in Diffusion-Based Molecular Communications: Concept and Practical Consideration","authors":"Michael Gattringer;Stefan Angerbauer;Andreas Springer;Werner Haselmayr","doi":"10.1109/TMBMC.2026.3688087","DOIUrl":"https://doi.org/10.1109/TMBMC.2026.3688087","url":null,"abstract":"This paper proposes a distributed scheduling algorithm for time-division multiple access in diffusion-based molecular communication systems. We consider multiple transmitters sending messages to a single receiver. The proposed scheduling algorithm is executed by each transmitter and prevents inter-user interference in its final operating state. Moreover, it requires no synchronization among the transmitters and allows non-ideal time measurement and varying package lengths. The efficiency of the proposed algorithm is compared with an ideal and centralized scheduling algorithm as an upper benchmark and is validated with a particle-based simulation.","PeriodicalId":36530,"journal":{"name":"IEEE Transactions on Molecular, Biological, and Multi-Scale Communications","volume":"12 ","pages":"615-627"},"PeriodicalIF":2.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11495228","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148003599","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Stochastic Performance Analysis Framework for DNA-Based Authentication Protocols","authors":"Oriel Limor;Roy Shafir;Zohar Yakhini","doi":"10.1109/TMBMC.2026.3725619","DOIUrl":"https://doi.org/10.1109/TMBMC.2026.3725619","url":null,"abstract":"Synthetic DNA is gaining significant attention for its potential across various fields, both in biology and as an information medium. One particular emerging field of application is product tagging, driven by the need for enhanced security, precise traceability, and reliable authenticity verification. The field of DNA-based authentication protocols is still in its early stages. In this work we propose a framework for analyzing and comparing the performance of such protocols. We introduce precise definitions and present analytical results. We evaluate two representative protocols inspired by research published in recent literature. The first protocol employs the whole genome of a cell line with mutations at <inline-formula> <tex-math>$n$ </tex-math></inline-formula> specific locations and the second uses Chemical Unclonable Functions (CUFs), inducing unique patterns based on primer pair challenges. We model the interaction between three entities: the manufacturer (<inline-formula> <tex-math>$mathcal {M}$ </tex-math></inline-formula>), the customer (<inline-formula> <tex-math>$mathcal {C}$ </tex-math></inline-formula>), and an adversary (<inline-formula> <tex-math>$mathcal {A}$ </tex-math></inline-formula>). After receiving the product, <inline-formula> <tex-math>$mathcal {C} $ </tex-math></inline-formula> obtains verification challenges from <inline-formula> <tex-math>$mathcal {M}$ </tex-math></inline-formula>, applies them to the DNA embedded in the product, and sends the resulting pattern back to <inline-formula> <tex-math>$mathcal {M} $ </tex-math></inline-formula> for verification. Meanwhile, <inline-formula> <tex-math>$mathcal {A} $ </tex-math></inline-formula> attempts to counterfeit products by imitating the tagging signature based on observed results. We propose a performance indicator that quantifies <inline-formula> <tex-math>$mathcal {A}$ </tex-math></inline-formula>’s success probability as a function of the number of observed product instances and on the signature complexity. Finally, we discuss key challenges and propose optimization approaches to strengthen protocol resilience.","PeriodicalId":36530,"journal":{"name":"IEEE Transactions on Molecular, Biological, and Multi-Scale Communications","volume":"12 ","pages":"937-946"},"PeriodicalIF":2.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11660756","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148925329","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DBMC-aNOMAly: Asynchronous NOMA With Pilot-Symbol Optimization Protocol for Diffusion-Based Molecular Communication Networks","authors":"Alexander Wietfeld;Wolfgang Kellerer","doi":"10.1109/TMBMC.2026.3701552","DOIUrl":"https://doi.org/10.1109/TMBMC.2026.3701552","url":null,"abstract":"Multiple access (MA) schemes can enable cooperation between multiple nodes in future diffusion-based molecular communication (DBMC) networks. Non-orthogonal MA for DBMC networks (DBMC-NOMA) is a promising option for efficient simultaneous MA using a single molecule type. This paper studies parameter optimization and bit error probability (BEP) reduction for asynchronous DBMC-NOMA. First, we analytically derive the associated BEP and compare DBMC-NOMA with time-division and molecule-division MA. We show that asynchronous offsets can improve performance, and the upper-bound performance can be approached under almost all considered conditions by avoiding a small set of worst-case offset configurations, for which we propose and characterize a dedicated avoidance mechanism. We then propose DBMC-aNOMAly, a pilot-symbol-based optimization protocol for asynchronous DBMC-NOMA, and evaluate it using Monte Carlo simulations. DBMC-aNOMAly provides robust BEP reduction across different network sizes and noise levels, under sampling jitter, and under changing runtime conditions, outperforming protocols from previous work. An end-to-end efficiency analysis further shows that these gains translate into increased net throughput after compensating for the pilot overhead. DBMC-aNOMAly uses simple operations such as comparisons and additions that are compatible with chemical reaction networks, motivating future realistic modeling of the protocol.","PeriodicalId":36530,"journal":{"name":"IEEE Transactions on Molecular, Biological, and Multi-Scale Communications","volume":"12 ","pages":"674-692"},"PeriodicalIF":2.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11556335","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148442193","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Molecule Mixture Detection and Design for MC Systems With Non-Linear, Cross-Reactive Receiver Arrays","authors":"Bastian Heinlein;Kaikai Zhu;Sümeyye Carkit-Yilmaz;Sebastian Lotter;Helene M. Loos;Andrea Buettner;Yansha Deng;Robert Schober;Vahid Jamali","doi":"10.1109/TMBMC.2026.3721779","DOIUrl":"https://doi.org/10.1109/TMBMC.2026.3721779","url":null,"abstract":"Air-based molecular communication (MC) has the potential to be one of the first MC systems to be deployed in realworld applications, enabled by commercially available sensors. However, these sensors usually exhibit non-linear and crossreactive behavior, contrary to the idealizing assumption of linear and perfectly molecule type-specific sensing often made in the MC literature. To address this mismatch, we propose several detectors and transmission schemes for a molecule mixture communication system where the receiver (RX) employs non-linear, cross-reactive sensors. All proposed schemes are based on the first- and second-order moments of the symbol likelihoods that are fed through the non-linear RX using the Unscented Transform. In particular, we propose an approximate maximum likelihood (AML) symbol-by-symbol detector for inter-symbol interference (ISI)-free transmission scenarios and a complementary mixture alphabet design algorithm which accounts for the RX characteristics. When significant ISI is present at high data rates, the AML detector can be adapted to exploit statistical ISI knowledge. Additionally, we propose a sequence detector which combines information from multiple symbol intervals. For settings where sequence detection is not possible due to extremely limited computational power at the RX, we propose an adaptive transmission scheme which can be combined with symbol-by-symbol detection. Using computer simulations, we validate all proposed detectors and algorithms based on the responses of commercially available sensors as well as artificially generated sensor data incorporating the characteristics of metal-oxide semiconductor sensors. By employing a general system model that accounts for transmitter noise, ISI, and general non-linear, cross-reactive RX arrays, this work enables reliable communication for a large class of MC systems.","PeriodicalId":36530,"journal":{"name":"IEEE Transactions on Molecular, Biological, and Multi-Scale Communications","volume":"12 ","pages":"914-930"},"PeriodicalIF":2.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148781509","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Dynamic Cluster Formation System of Bio-Nanomachines: Emergent System Phases and Parallel Target Exploration","authors":"Tomoya Yamakawa;Tadashi Nakano","doi":"10.1109/TMBMC.2026.3713185","DOIUrl":"https://doi.org/10.1109/TMBMC.2026.3713185","url":null,"abstract":"Molecular communication enables bio-nanomachines to coordinate their behavior using signaling molecules and provides a foundation for collective behavior. This paper proposes a dynamic cluster formation system in which bio-nanomachines form, split, and merge micro-clusters through local interactions. Simulation studies show that micro-cluster formation improves the accuracy of sensing concentration gradients of signaling molecules and that the system exhibits three distinct phases: disordered, multi-cluster, and single-cluster. Furthermore, in environments with multiple targets, bio-nanomachines may form multiple spatially distributed micro-clusters, enabling parallel target exploration. These findings may contribute both to applications such as tumor-targeted drug delivery and to advancing our understanding of collective behavior of biological systems.","PeriodicalId":36530,"journal":{"name":"IEEE Transactions on Molecular, Biological, and Multi-Scale Communications","volume":"12 ","pages":"752-761"},"PeriodicalIF":2.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148626036","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Review of Confidentiality, Integrity, and Availability Challenges in Molecular Communications","authors":"Malcolm I. Anderson;Truc T. Duong;Ebuka Philip Oguchi;Anna Wisniewska;Nirnimesh Ghose","doi":"10.1109/TMBMC.2026.3730015","DOIUrl":"https://doi.org/10.1109/TMBMC.2026.3730015","url":null,"abstract":"Molecular communications (MC) are emerging paradigms that aim to transform communication at the micro- and nanoscale levels by utilizing molecules as information carriers. Unlike traditional communication, molecular communication uses chemical signals, which provide distinct advantages in biological contexts. This review examines important advances in molecular communication security customized for these unique communication channels. We highlight risks and challenges in molecular systems, focusing on the confidentiality, integrity, and availability security objectives that are critical for applications such as targeted drug delivery (TDD) and in vivo networks. Furthermore, we discuss potential attacks, interdisciplinary security measures, physical/MAC layer strategies, and hybrid networks. Our contributions provide a complete overview of existing research, identify critical challenges, and potential solutions to ensure secure and reliable molecular communication.","PeriodicalId":36530,"journal":{"name":"IEEE Transactions on Molecular, Biological, and Multi-Scale Communications","volume":"12 ","pages":"969-981"},"PeriodicalIF":2.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149033791","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interpretable Reaction–Diffusion Learning for Molecular Communication via Graph Kolmogorov–Arnold Networks","authors":"Liang Dong","doi":"10.1109/TMBMC.2026.3731398","DOIUrl":"https://doi.org/10.1109/TMBMC.2026.3731398","url":null,"abstract":"Molecular communication (MC) lets biological cells coordinate collective behaviors through diffusion-mediated signaling molecules that couple intracellular reaction kinetics across a spatial graph. Existing graph neural networks are accurate but opaque, while classical reaction–diffusion models require an explicit kinetics function unavailable in most biological settings. This paper proposes Reaction–Diffusion Graph Kolmogorov–Arnold Networks (RD-GKAN), an interpretable architecture whose forward pass implements reaction–diffusion dynamics: a learnable B-spline per species captures the reaction, and diffusion uses an explicit graph Laplacian with prescribed distance-dependent weights, MC-derived where the channel parameters are available. Each learned spline is projected onto a symbolic library via sparse regression, selecting the dominant kinetics basis (e.g., Hill or Michaelis–Menten) without assuming the form a priori. Evaluation spans five independent domains: MC testbeds, bacterial quorum sensing, ERK signaling waves, and wound-healing and two-tissue spatial transcriptomics. Under leakage-controlled evaluation, graph-structured coupling improves none of the real-data tasks. A trivial persistence baseline matches or outperforms the full model on both temporal datasets. The S. aureus device suppresses diffusive coupling, so that result is a specificity check; the ERK monolayers carry documented collective waves, and there the null reflects a task dominated by temporal autocorrelation. Static spatial reconstruction reduces to local self-feature denoising rather than inter-cellular communication, a caution for spatial graph learning. On synthetic data, B-spline parameterization outperforms an MLP by <inline-formula> <tex-math>$6.2times $ </tex-math></inline-formula> and distance-dependent edge weights improve accuracy <inline-formula> <tex-math>$2.4times $ </tex-math></inline-formula> over binary coupling.","PeriodicalId":36530,"journal":{"name":"IEEE Transactions on Molecular, Biological, and Multi-Scale Communications","volume":"12 ","pages":"1056-1076"},"PeriodicalIF":2.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149131035","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mass-Conservative Numerical Analysis of an Attraction–Repulsion Keller–Segel System for Self-Organized Biological Pattern Dynamics","authors":"Satoru Iwasaki","doi":"10.1109/TMBMC.2026.3739084","DOIUrl":"https://doi.org/10.1109/TMBMC.2026.3739084","url":null,"abstract":"Collective migration driven by attractive and repulsive chemical cues is a basic mechanism behind self-organized biological pattern formation. In this paper, we study a one-dimensional attraction–repulsion Keller–Segel (ARKS) system as a minimal mathematical model for such competing interactions. Our emphasis is not on a purely analytical characterization of the model, but on a numerical investigation of biologically relevant spatiotemporal dynamics. To this end, we construct a conservative finite-difference scheme by adapting the structure-preserving framework of Saito for the classical Keller–Segel system to the ARKS setting through a single effective potential that combines the attractive and repulsive signals into one discrete drift, to which Saito’s conservative upwind treatment can be directly applied. The proposed scheme preserves the total mass of the cell density at the discrete level, and the discrete density is shown to remain nonnegative under an adaptive time-step restriction. Numerical experiments systematically reveal, within this single conservative framework, three characteristic dynamical regimes—traveling patterns, multi-pulse interactions including bouncing behavior, and breathing pulses—and quantitatively characterize their dependence on the attraction–repulsion balance. These results illustrate how the balance between attraction and repulsion controls self-organized information processing in living systems, and demonstrate that structure-preserving computation is an effective tool for interpreting such dynamics from a biological viewpoint.","PeriodicalId":36530,"journal":{"name":"IEEE Transactions on Molecular, Biological, and Multi-Scale Communications","volume":"12 ","pages":"1116-1124"},"PeriodicalIF":2.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11732163","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149188340","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Nanopore Sequencing Channel Modeling and Adaptive Dual-Track Decoding Framework for DNA Data Storage","authors":"Ying Zeng;Rui Gong;Yi Li;Yi Fang;Guojun Han","doi":"10.1109/TMBMC.2026.3740116","DOIUrl":"https://doi.org/10.1109/TMBMC.2026.3740116","url":null,"abstract":"Nanopore sequencing (NPS) provides a promising readout mechanism for DNA data storage, but its mixed insertion, deletion, and substitution errors create severe synchronization drift and decoding ambiguity. Existing simulators can generate reads affected by nanopore sequencing errors, yet their sample-level randomness and coupled simulation parameters make controlled decoder evaluation difficult. To provide reproducible channel benchmarks, this paper constructs two NPS channel models, namely a Deletion-Dominant Model (DDM) and a Balanced-InDel Model (BIM). To improve robustness against synchronization drift, we propose an Adaptive Dual-Track Decoding (ADTD) framework. ADTD combines a Markov-Bounded Hard-Decision Decoder (MB-HDD), which uses bounded synchronization states and Hoeffding-based pruning for low-complexity hard-decision recovery, with a Turbo-Refined Soft-Decision Decoder (TR-SDD), which performs local soft-window refinement using reliability-weighted log-likelihood ratio updates and <inline-formula> <tex-math>$k$ </tex-math></inline-formula>-mer context priors. Uncertain blocks are adaptively routed from MB-HDD to TR-SDD according to hard-decision reliability indicators. Experimental results show that ADTD extends the reliable operating range of DNA retrieval. Compared with the Composite Hedges Nanopores decoder, the tolerable overall error rate increases from about 3.0% to 6.0% under DDM and from about 5.2% to 8.0% under BIM. Ablation, parameter sensitivity, routing ratio, and sequence length studies further demonstrate that ADTD improves robustness while maintaining predictable computational cost.","PeriodicalId":36530,"journal":{"name":"IEEE Transactions on Molecular, Biological, and Multi-Scale Communications","volume":"12 ","pages":"1150-1161"},"PeriodicalIF":2.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149248820","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep Learning-Based Estimation of Distance and Emission Time in Mobile Molecular Communication","authors":"Zhen Cheng;Jianlong Zheng;Yi Luo;Ming Xia;Xinwei Yao;Yongliang Zhang","doi":"10.1109/TMBMC.2026.3701830","DOIUrl":"https://doi.org/10.1109/TMBMC.2026.3701830","url":null,"abstract":"In molecular communication, estimating the distance between the transmitter and the receiver has a variety of applications at the micro-scale, such as the transmitter or the receiver localization, targeted drug delivery, etc. Existing traditional methods for distance estimation typically rely on mathematical derivations that assume prior knowledge of the emission times of molecules from the transmitters. However, when the emission time is unknown, accurately estimating the distance becomes significantly more challenging and involves higher deduction complexity. In this paper, we propose a deep learning-based approach to simultaneously estimate the distances between multiple transmitters and a receiver and the emission times of molecules. We evaluate the prediction performance of the Transformer model. Simulation results indicate that the Transformer model achieves the highest prediction accuracy for both distances and emission times estimation under given error thresholds, outperforming both the Deep Neural Networks and Informer models, which demonstrates strong prediction ability of the Transformer model.","PeriodicalId":36530,"journal":{"name":"IEEE Transactions on Molecular, Biological, and Multi-Scale Communications","volume":"12 ","pages":"693-704"},"PeriodicalIF":2.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148442207","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}