Ozgur Baris Akan

Hardware impairments affect sensing and communication asymmetrically in monostatic integrated sensing and communication (ISAC): the transmitter can monitor its own distorted waveform for sensing, whereas the remote communication receiver cannot. The aggregate k distortion model treats impairments as unknown noise, which is appropriate for communication but pessimistic for monostatic sensing. We derive power-amplifier-aware sensing Cramér–Rao bounds (CRBs) and a phase-noise-aware CRB that reveals an irreducible velocity-error floor, and quantify when k-based bounds overestimate sensing degradation. Simulations validate the analysis and show robustness to practical digital pre-distortion (DPD) template errors (less than 1 dB overhead at −25 dB normalized mean square error (NMSE)).

Data-driven channel models often require extensive simulation data and capture only limited statistics of the underlying physical process. To address these limitations in diffusion-based particle signaling, we build upon physics-informed machine learning to develop a framework that fuses sparse channel measurements with governing diffusion–reaction laws. This work explores physics-informed operator learning for particle-based communication channels, aiming to bridge mechanistic PDE modeling and data-driven surrogates in this domain. Our method employs a Physics-Informed Neural Operator (PINO) to predict the spatiotemporal particle concentration field and generalizes across channel configurations with reduced dependence on explicit geometric parameterization. We further extend the framework to model quorum sensing between bacterial colonies and capture autoinducer dynamics. Compared with Physics-Informed Neural Networks (PINNs) and Deep Operator Networks (DeepONets), PINO achieves high accuracy and significant computational efficiency: on the nanomachine channel, PINO reduces the relative ℓ₂ error from 99.3% to 9.2%; on the quorum sensing model, PINO improves R² from 0.808 (DeepONet) to 0.999, while multi-resolution inference yields 4–5× speed-ups on coarse grids. These results highlight physics-informed operator learning as a promising method for particle-based communication networks.

Molecular communication (MC), a biologically-inspired technology, enables applications in nanonetworks and the Internet of Everything (IoE), offering unprecedented potential in intra-body systems for applications such as drug delivery, health monitoring, and disease detection. In this paper, we extend our previous research on the Flexure-FET MC receiver by integrating a competitive binding model to enhance its performance in high-interference environments, where multiple species coexist in the reception space. While previous studies have primarily focused on ligand concentration estimation and detection methods, they have not fully addressed the competition between molecular species for receptor binding sites. The competitive binding framework introduced here plays a pivotal role in multitarget environments, closely reflecting real-world biological scenarios. By accounting for the competition between species for receptor binding sites, this framework enhances the understanding of MC dynamics in complex environments. Leveraging this framework enables the fine-tuning of receptor responses, optimizing Flexure-FET MC receiver performance through adjustments in ligand concentrations and receptor affinities. Through comprehensive performance analysis, we demonstrate that integrating competitive binding underscores the importance of accounting for interference in MC systems to improve reliability and accuracy. While the signal-to-noise ratio (SNR) and symbol error probability (SEP) are impacted by factors such as interferer concentration and receptor dynamics, the framework emphasizes the need to manage these interference factors to optimize the system’s overall performance. The results show that accounting for interference—through competitive binding—provides a more realistic understanding of system behavior and enables the fine-tuning of the receiver’s response, ultimately ensuring more reliable and accurate detection in environments where multiple species are present.

The Internet of Bio-Nano Things (IoBNT) requires mobile nanomachines that navigate fluidic environments while exchanging molecular signals under external control. We propose a chemo-hydrodynamic transceiver for catalytic Janus particles in which a single optical input jointly controls molecular emission and self-propulsion, unlike standard models that decouple communication from mobility. The resulting stochastic channel contains an actuation-dependent variance term scaling as I⁴, which creates a non-monotonic reliability trade-off. Stronger actuation increases emission but also amplifies motion-induced fading. In a reduced single-link setting, exact-BEP analysis reveals a finite optimal actuation level, and comparison with Brownian-mobility baselines shows that neglecting active motility can markedly underestimate BEP, especially at high actuation and short link distances. These results provide physical-layer guidance for mobility-aware actuation and link design in IoBNT.

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 an achievable-rate 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 uncompensated 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: uncompensated 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 (RMSE ∝ 1/ √ N_tN_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^∗ ≈ 0.10, where α^∗ = (C_DSE/C_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.

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.

Molecular communication (MC) is an emerging paradigm that takes inspiration from biological processes, enabling communication at the nanoscale and facilitating the development of the Internet of Bio-Nano Things (IoBNT). Traditional models of MC often rely on idealized assumptions that overlook practical challenges related to noise and signal behavior. This paper proposes and evaluates the first physical MC ion transmitter (ITX) using an ion exchange membrane. The circuit network model is used to simulate ion transport and analyze both transient and steady-state behavior. This analysis includes the effects of noise sources such as thermal and shot noise on signal integrity and SNR. The main contributions of this paper are to demonstrate how a practical MC ITX can produce a realistic waveform and to highlight future research challenges associated with a physical membrane-based ITX.

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.