arXiv subjects

Ozgur B. Akan

Publications and source records attributed to Ozgur B. Akan.

At least 55 records · Page 3Linked to original sources

arXiv2025

Environment-to-Link ISAC with Space-Weather Sensing for Ka-Band LEO Downlinks

Ka-band low-Earth-orbit (LEO) downlinks can suffer second-scale reliability collapses during flare-driven ionospheric disturbances, where fixed fade margins and reactive adaptive coding and modulation (ACM) are either overly conservative or too slow. This paper presents a GNSS-free, link-internal predictive controller that senses the same downlink via a geometry-free dual-carrier phase observable at 10~Hz: a high-pass filter and template-based onset detector, followed by a four-state nearly-constant-velocity Kalman filter, estimate $Δ$VTEC and its rate, and a short look-ahead (60~s) yields an endpoint outage probability used as a risk gate to trigger one-step discrete MCS down-switch and pilot-time update with hysteresis. Evaluation uses physics-informed log replay driven by real GOES X-ray flare morphologies under a disjoint-day frozen-calibration protocol, with uncertainty reported via paired moving-block bootstrap. Across stressed 60~s windows, the controller reduces peak BLER by 25--30\% and increases goodput by 0.10--0.15~bps/Hz versus no-adaptation baselines under a unified link-level abstraction. The loop runs in $\mathcal{O}(1)$ per 0.1~s epoch (about 0.042~ms measured), making on-board implementation feasible, and scope and deployment considerations for dispersion-dominated events are discussed.

eess.SP↗

arXiv2025

Performance Limits of Hardware-Constrained THz Inter-Satellite MIMO-ISAC Systems

Terahertz inter-satellite links (THz-ISL) offer unprecedented bandwidth for future space networks but face fundamental constraints from onboard power and thermal budgets. This paper establishes theoretical performance limits for MIMO Integrated Sensing and Communication (ISAC) systems under per-element constant-envelope (CE) transmission constraints. We demonstrate that hardware distortions -- specifically power amplifier nonlinearity, ADC quantization, and oscillator phase noise -- impose a capacity ceiling that cannot be overcome by increasing transmit power. A unified link budget framework integrates wideband beam squint, aperture pointing errors, and colored noise sources through a spectral consistency principle that ensures residual phase noise is counted exactly once across communication and sensing analyses. The sensing bounds are derived via the Whittle-Fisher Information Matrix under a Constant Acceleration kinematic model with jerk noise, yielding closed-form scaling laws: residual phase noise variance scales as $α^{-1}$ while dynamic state-estimation error (DSE) variance scales as $α^{-5}$ with pilot overhead $α$. Numerical results show divergent MIMO scaling: sensing precision improves with array size ($\mathrm{RMSE} \propto 1/\sqrt{N_t N_r}$), while the critical SNR exhibits scale invariance regarding array size, implying that the distortion-limited transition point stabilizes regardless of the array scale. The steep $α^{-5}$ DSE scaling creates an operationally infeasible region at $α< α^* \approx 0.16$, where $α^* = (C_{\mathrm{DSE}}/C_{\mathrm{PN}})^{1/4}$ -- a constraint-driven threshold under the adopted baseline for LEO operation. These findings provide design guidelines for hardware-efficient THz-ISL constellations.

eess.SP↗

arXiv2025

Federated Learning for Terahertz Wireless Communication

The convergence of Terahertz (THz) communications and Federated Learning (FL) promises ultra-fast distributed learning, yet the impact of realistic wideband impairments on optimization dynamics remains theoretically uncharacterized. This paper bridges this gap by developing a multicarrier stochastic framework that explicitly couples local gradient updates with frequency-selective THz effects, including beam squint, molecular absorption, and jitter. Our analysis uncovers a critical diversity trap: under standard unbiased aggregation, the convergence error floor is driven by the harmonic mean of subcarrier SNRs. Consequently, a single spectral hole caused by severe beam squint can render the entire bandwidth useless for reliable model updates. We further identify a fundamental bandwidth limit, revealing that expanding the spectrum beyond a critical point degrades convergence due to the integration of thermal noise and gain collapse at band edges. Finally, we demonstrate that an SNR-weighted aggregation strategy is necessary to suppress the variance singularity at these spectral holes, effectively recovering convergence in high-squint regimes where standard averaging fails. Numerical results validate the expected impact of the discussed physical layer parameters' on performance of THz-FL systems.

cs.DC↗

arXiv2025

Internet of Intelligent Reflecting Surfaces (IoIRS)

Intelligent Reflecting Surfaces (IRS) are anticipated to serve as a key cornerstone of future wireless networks, providing an unmatched capability to deterministically shape electromagnetic wave propagation. Despite this potential, most existing research still considers the IRS merely as a standalone physical-layer component, controlled by transmitters. However, as networks grow to encompass a massive number of these surfaces and a massive number of transmitters wishing to use them, this transmitter-centric design encounters substantial challenges. To overcome this challenge, we propose the Internet of IRS (IoIRS), an architecture that reconceives the IRS not just as a passive reflecting surface, but as a connected, hybrid entity functioning across both the physical layer and upper network layers. We present the conceptual framework and a preliminary protocol suite necessary to integrate these surfaces into the higher network layers. We conclude by examining how IoIRS architectures could be applied in practice, as their deployment will be essential for fully realizing the capabilities of future wireless networks.

cs.NI↗

arXiv2025

An Acoustic Communication Model in Plants

Molecular communication (MC) studies biological signals that are found in nature. Most MC literature focuses on particle properties, even though many natural phenomena exhibit wave-like behavior. One such signal is sound waves. Understanding how sound waves are used in nature can help us better utilize this signal in our interactions with our environment. To take a step in this direction, in this paper, we examine how plants process incoming sound waves and take informed actions. Indeed, plants respond to sound, yet no quantitative communication-theoretic model currently explains this behavior. This study develops the first end-to-end acoustic communication framework for plants. The model is formed following the biological steps of the incoming signal, and a mathematical description is constructed at each step following basic biological models. The resulting end-to-end communication-theoretic model is analyzed using MATLAB. Simulations show that a $200$ $Hz$, $20$ $mu Pa$ stimulus elevates cytosolic $Ca^{2+}$ from $150$ $nM$ to $230 \pm 10$ $nM$ within $50$ seconds which can cause root bending in plants in the long run. This work establishes quantitative phytoacoustics, enabling bio-inspired acoustic connections for precision agriculture and plant signaling research.

eess.SP↗

arXiv2025

A Cylindrical Nanowire Array-Based Flexure-FET Receiver for Molecular Communication

Molecular communication (MC) enables biocompatible and energy-efficient information transfer through chemical signaling, forming a foundational paradigm for emerging applications in the Internet of Nano Things (IoNT) and intrabody healthcare systems. The realization of this vision critically depends on developing advanced receiver architectures that merge nanoscale communication and networking techniques with bio-cyber interfaces, ensuring energy-efficient, reliable, and low-complexity modulation and detection while maintaining biocompatibility. To address these challenges, the Flexure-FET MC receiver was introduced as a mechanically transducing design capable of detecting both charged and neutral molecular species. In this study, we present a cylindrical nanowire array-based Flexure-FET MC receiver that enhances design versatility and scalability through distributed electromechanical coupling in a suspended-gate configuration. The proposed array architecture offers additional geometric degrees of freedom, including nanowire radius, length, spacing, and array size, providing a flexible framework that can be tailored to advanced MC scenarios. An analytical end-to-end model is developed to characterize the system's electromechanical response, noise behavior, and information-theoretic performance, including signal-to-noise ratio (SNR) and channel capacity. The results reveal the strong interdependence between geometry, electromechanical dynamics, and molecular binding processes, enabling tunable control over sensitivity, noise characteristics, and communication capacity. The enhanced structural tunability and array configuration of the proposed design provide a flexible foundation for future mixture-based and spatially modulated MC systems, paving the way toward scalable and multifunctional receiver architectures within the IoNT framework.

eess.SP↗

arXiv2025

Fundamental Limits of Cooperative Integrated Sensing and Communications over Low-Earth Orbit THz Satellite Channels

Terahertz inter-satellite links enable unprecedented sensing precision for Low Earth Orbit (LEO) constellations, yet face fundamental bounds from hardware impairments, pointing errors, and network interference. We develop a Network Cramér-Rao Lower Bound (N-CRLB) framework incorporating dynamic topology, hardware quality factor $Γ_{\text{eff}}$, phase noise $σ^2_ϕ$, and cooperative effects through recursive Fisher Information analysis. Our analysis reveals three key insights: (i) hardware and phase noise create power-independent performance ceilings ($σ_{\text{ceiling}} \propto \sqrt{Γ_{\text{eff}}}$) and floors ($σ_{\text{floor}} \propto \sqrt{σ^2_ϕ}/f_c$), with power-only scaling saturating above $\text{SNR}_{\text{crit}}=1/Γ_{\text{eff}}$; (ii) interference coefficients $α_{\ell m}$ enable opportunistic sensing with demonstrated gains of 5.5~dB under specific conditions (65~dB processing gain, 50~dBi antennas); (iii) measurement correlations from shared timing references, when properly modeled, do not degrade performance and can provide common-mode rejection benefits compared to mismodeled independent-noise baselines. Sub-millimeter ranging requires co-optimized hardware ($Γ_{\text{eff}}<0.01$), oscillators ($σ^2_ϕ<10^{-2}$), and appropriate 3D geometry configurations.

eess.SP↗

arXiv2025

Information Transmission in Quorum Sensing for Gut Microbiome

Microorganisms employ sophisticated mechanisms for intercellular communication and environmental sensing, with quorum sensing serving as a fundamental regulatory process. Dysregulation of quorum sensing has been implicated in various diseases. While most theoretical studies focus on mathematical modeling of quorum sensing dynamics, the communication-theoretic aspects remain less explored. In this study, we investigate the information processing capabilities of quorum sensing systems using a stochastic differential equation framework that links intracellular gene regulation to extracellular autoinducer dynamics. We quantify mutual information as a measure of signaling efficiency and information fidelity in two major bacterial phyla of the gut microbiota: Firmicutes and Bacteroidetes.

cs.ET↗

arXiv2025

Fundamental Limits of THz Inter-Satellite ISAC Under Hardware Impairments

This paper establishes a theoretical framework for analyzing the fundamental performance limits of terahertz (THz) Low Earth Orbit (LEO) inter-satellite link (ISL) Integrated Sensing and Communications (ISAC) systems. We develop a unified, end-to-end signal model that, jointly captures the effects of extreme orbital dynamics, cascaded non-ideal hardware impairments, and micro-radian beam pointing errors. Through Bayesian Cramér-Rao Lower Bound (BCRLB) analysis, we derive the ultimate sensing accuracy for range and range-rate, revealing a quadratic ($1/f_c^2$) improvement in estimation variance with carrier frequency, which is ultimately floored by signal-dependent hardware distortion. For communication, we show that system performance is not power-limited but hardware-limited, deriving a closed-form capacity ceiling under the joint effect of phase noise and PA nonlinearity: $C_{\text{sat}} = \log_2(1 + e^{-σ_ϕ^2}/Γ_{\text{eff}})$, where $Γ_{\text{eff}}$ is a proposed hardware quality factor. Our numerical results, based on state-of-the-art component data and the identified trade-offs, suggest that favorable operational conditions may exist in the sub-THz frequency range (200-600 GHz) where the quadratic sensing gain with frequency is balanced against hardware quality degradation. Power Amplifier (PA) nonlinearity emerges as the dominant performance bottleneck, exceeding other impairments by one to two orders of magnitude.

eess.SP↗

arXiv2025

Gut-Brain Axis as a Closed-Loop Molecular Communication Network

Molecular communication (MC) provides a quantitative framework for analyzing information transfer within biological systems. This paper introduces a novel and comprehensive MC framework for the gut-brain axis (GBA) as a system of six coupled, nonlinear delay differential equations (DDEs). The proposed model defines a bidirectional feedback loop with a gut-to-brain inflammatory channel and a brain-to-gut neuroendocrine channel. Under prolonged stress, this feedback loop becomes self-perpetuating and drives the system into a pathological state. We evaluate the end-to-end channel across varying conditions using time-domain simulations, small-signal frequency-domain characterization, and an information-theoretic capacity analysis. At homeostasis, the system maintains stable circadian dynamics with higher information throughput, whereas sustained stress drives a shift to dysregulated hypercortisolism. In this pathological state, spectral efficiency decreases due to a narrowed effective bandwidth and a lower passband gain driven by neuroendocrine delays and saturating cytokine-hormone kinetics. These results quantify the impact of these signaling mechanisms on stability and information processing, elucidating the transition from healthy circadian rhythms to a persistent pathological state of hypercortisolism.

cs.ET↗

arXiv2025

Graph Representation-based Model Poisoning on Federated Large Language Models

Federated large language models (FedLLMs) enable powerful generative capabilities within wireless networks while preserving data privacy. Nonetheless, FedLLMs remain vulnerable to model poisoning attacks. This article first reviews recent advancements in model poisoning techniques and existing defense mechanisms for FedLLMs, underscoring critical limitations, especially when dealing with non-IID textual data distributions. Current defense strategies predominantly employ distance or similarity-based outlier detection mechanisms, relying on the assumption that malicious updates markedly differ from benign statistical patterns. However, this assumption becomes inadequate against adaptive adversaries targeting billion-parameter LLMs. The article further investigates graph representation-based model poisoning (GRMP), an emerging attack paradigm that exploits higher-order correlations among benign client gradients to craft malicious updates indistinguishable from legitimate ones. GRMP can effectively circumvent advanced defense systems, causing substantial degradation in model accuracy and overall performance. Moreover, the article outlines a forward-looking research roadmap that emphasizes the necessity of graph-aware secure aggregation methods, specialized vulnerability metrics tailored for FedLLMs, and evaluation frameworks to enhance the robustness of federated language model deployments.

cs.CR↗

arXiv2025

MASC: Integrated Sensing and Communications for the Martian Internet of Space

Mars exploration missions increasingly demand reliable communication systems, yet harsh environmental conditions -- particularly frequent dust storms, extreme Doppler effects, and stringent resource constraints -- pose unprecedented challenges to conventional communication approaches. This paper presents the Martian Adaptive Sensing and Communication (MASC) system specifically designed for the Martian environment. MASC establishes a physically interpretable channel model and develops three key components: environment-aware hybrid precoding, adaptive parameter mapping, and robust communication precoding. Simulation results demonstrate that MASC maintains 45 percent sensing coverage under severe dust conditions compared to only 5 percent with conventional methods, provides up to 2.5 dB signal-to-interference-plus-noise ratio (SINR) improvement at 50 percent channel state information (CSI) uncertainty, and yields 80 percent higher capacity in moderate dust storms. Using an epsilon-constraint multi-objective optimization approach, we enable mission planners to select operational modes ranging from communication-priority (0.33 bps/Hz capacity, 28 percent sensing coverage) to sensing-priority (90 percent coverage with minimal capacity), offering a versatile framework that balances environmental awareness with hyper-reliable data transmission. This work provides a validated blueprint for integrated sensing and communication (ISAC) in non-terrestrial networks (NTN), a key enabler for achieving ubiquitous connectivity in the 6G era.

eess.SP↗

arXiv2025

Flexure-FET-Based Receiver with Competitive Binding for Interference Mitigation in Molecular Communication

Molecular communication (MC), a biologically inspired technology, enables applications in nanonetworks and the Internet of Everything (IoE), with great potential for intra-body systems such as drug delivery, health monitoring, and disease detection. This paper extends our prior work on the Flexure-FET MC receiver by integrating a competitive binding model to enhance performance in high-interference environments, where multiple molecular species coexist in the reception space. Previous studies have largely focused on ligand concentration estimation and detection, without fully addressing the effects of inter-species competition for receptor binding. Our proposed framework captures this competition, offering a more biologically accurate model for multitarget environments. By incorporating competition dynamics, the model improves understanding of MC behavior under interference. This approach enables fine-tuning of receptor responses by adjusting ligand concentrations and receptor affinities, thereby optimizing the performance of the Flexure-FET MC receiver. Comprehensive analysis shows that accounting for competitive binding is crucial for improving reliability and accuracy in complex MC systems. Factors such as signal-to-noise ratio (SNR), symbol error probability (SEP), interferer concentration, and receptor dynamics are shown to significantly affect performance. The proposed framework highlights the need to manage these factors effectively. Results demonstrate that modeling interference through competitive binding offers a realistic system perspective and allows tuning of receiver response, enabling robust detection in environments with multiple coexisting species.

eess.SP↗

arXiv2025

Zero-Trust Foundation Models: A New Paradigm for Secure and Collaborative Artificial Intelligence for Internet of Things

This paper focuses on Zero-Trust Foundation Models (ZTFMs), a novel paradigm that embeds zero-trust security principles into the lifecycle of foundation models (FMs) for Internet of Things (IoT) systems. By integrating core tenets, such as continuous verification, least privilege access (LPA), data confidentiality, and behavioral analytics into the design, training, and deployment of FMs, ZTFMs can enable secure, privacy-preserving AI across distributed, heterogeneous, and potentially adversarial IoT environments. We present the first structured synthesis of ZTFMs, identifying their potential to transform conventional trust-based IoT architectures into resilient, self-defending ecosystems. Moreover, we propose a comprehensive technical framework, incorporating federated learning (FL), blockchain-based identity management, micro-segmentation, and trusted execution environments (TEEs) to support decentralized, verifiable intelligence at the network edge. In addition, we investigate emerging security threats unique to ZTFM-enabled systems and evaluate countermeasures, such as anomaly detection, adversarial training, and secure aggregation. Through this analysis, we highlight key open research challenges in terms of scalability, secure orchestration, interpretable threat attribution, and dynamic trust calibration. This survey lays a foundational roadmap for secure, intelligent, and trustworthy IoT infrastructures powered by FMs.

cs.CR↗

arXiv2025

Semantic Learning for Molecular Communication in Internet of Bio-Nano Things

Molecular communication (MC) provides a foundational framework for information transmission in the Internet of Bio-Nano Things (IoBNT), where efficiency and reliability are crucial. However, the inherent limitations of molecular channels, such as low transmission rates, noise, and intersymbol interference (ISI), limit their ability to support complex data transmission. This paper proposes an end-to-end semantic learning framework designed to optimize task-oriented molecular communication, with a focus on biomedical diagnostic tasks under resource-constrained conditions. The proposed framework employs a deep encoder-decoder architecture to efficiently extract, quantize, and decode semantic features, prioritizing taskrelevant semantic information to enhance diagnostic classification performance. Additionally, a probabilistic channel network is introduced to approximate molecular propagation dynamics, enabling gradient-based optimization for end-to-end learning. Experimental results demonstrate that the proposed semantic framework improves diagnostic accuracy by at least 25% compared to conventional JPEG compression with LDPC coding methods under resource-constrained communication scenarios.

eess.SP↗

arXiv2025

Towards Resilient Federated Learning in CyberEdge Networks: Recent Advances and Future Trends

In this survey, we investigate the most recent techniques of resilient federated learning (ResFL) in CyberEdge networks, focusing on joint training with agglomerative deduction and feature-oriented security mechanisms. We explore adaptive hierarchical learning strategies to tackle non-IID data challenges, improving scalability and reducing communication overhead. Fault tolerance techniques and agglomerative deduction mechanisms are studied to detect unreliable devices, refine model updates, and enhance convergence stability. Unlike existing FL security research, we comprehensively analyze feature-oriented threats, such as poisoning, inference, and reconstruction attacks that exploit model features. Moreover, we examine resilient aggregation techniques, anomaly detection, and cryptographic defenses, including differential privacy and secure multi-party computation, to strengthen FL security. In addition, we discuss the integration of 6G, large language models (LLMs), and interoperable learning frameworks to enhance privacy-preserving and decentralized cross-domain training. These advancements offer ultra-low latency, artificial intelligence (AI)-driven network management, and improved resilience against adversarial attacks, fostering the deployment of secure ResFL in CyberEdge networks.

cs.CR↗

arXiv2025

Semantic Communication for the Internet of Space: New Architecture, Challenges, and Future Vision

The expansion of sixth-generation (6G) wireless networks into space introduces technical challenges that conventional bit-oriented communication approaches cannot efficiently address, including intermittent connectivity, severe latency, limited bandwidth, and constrained onboard resources. To overcome these limitations, semantic communication has emerged as a transformative paradigm, shifting the communication focus from transmitting raw data to delivering context-aware, missionrelevant information. In this article, we propose a semantic communication architecture explicitly tailored for the 6G Internet of Space (IoS), integrating multi-modal semantic processing, AIdriven semantic encoding and decoding, and adaptive transmission mechanisms optimized for space environments. The effectiveness of our proposed framework is demonstrated through a representative deep-space scenario involving semantic-based monitoring of Mars dust storms. Finally, we outline open research challenges and discuss future directions toward realizing practical semantic-enabled IoS systems.

cs.NI↗

arXiv2025

Smell of Source: Learning-Based Odor Source Localization with Molecular Communication

Odor source localization is a fundamental challenge in molecular communication, environmental monitoring, disaster response, industrial safety, and robotics. In this study, we investigate three major approaches: Bayesian filtering, machine learning (ML) models, and physics-informed neural networks (PINNs) with the aim of odor source localization in a single-source, single-molecule case. By considering the source-sensor architecture as a transmitter-receiver model we explore source localization under the scope of molecular communication. Synthetic datasets are generated using a 2D advection-diffusion PDE solver to evaluate each method under varying conditions, including sensor noise and sparse measurements. Our experiments demonstrate that \textbf{Physics-Informed Neural Networks (PINNs)} achieve the lowest localization error of $\mathbf{0.89 \times 10^{-6}}$ m, outperforming \textbf{machine learning (ML) inversion} ($\mathbf{1.48 \times 10^{-6}}$ m) and \textbf{Kalman filtering} ($\mathbf{1.62 \times 10^{-6}}$ m). The \textbf{reinforcement learning (RL)} approach, while achieving a localization error of $\mathbf{3.01 \times 10^{-6}}$ m, offers an inference time of $\mathbf{0.147}$ s, highlighting the trade-off between accuracy and computational efficiency among different methodologies.

eess.SP↗