Ruifeng Zheng, Zhihan Xu, Veronika Volkova, Pengjie Zhou, Martín Schottlender, Juan A. Cabrera, Frank H. P. Fitzek, Pit Hofmann
DNA-based molecular communication with microarray-style reception under reversible hybridization is of interest for emerging DNA-enabled sensing and Internet of Bio-Nano Things (IoBNT) systems. In such channels, the bound-state observation exhibits both inter-symbol interference (ISI) and colored counting noise. To capture these effects in a communication-oriented form, we develop a Markov state-space framework based on a voxelized reaction-diffusion model, in which a block-structured transition matrix describes molecular transport and binding/unbinding dynamics. For the microarray specialization, this representation yields the channel impulse response (CIR), the equilibrium gain, and a settling-time-based characterization of the effective channel memory. Building on the resulting symbol-rate observation model for on-off keying, we derive a grouped-binomial counting model and obtain a closed-form expression for the covariance of the counting noise. Based on these statistics, we further develop a differential-threshold detector and a finite-memory decision-feedback equalizer and benchmark them against a symbol-by-symbol maximum a posteriori detector with decision feedback (MAP-DF). Numerical results validate the theoretical correlation behavior and show that the relative performance of the proposed receivers depends strongly on the channel-memory regime.
Ruifeng Zheng, Zhihan Xu, Pit Hofmann, Frank H. P. Fitzek
This paper investigates whether low-order packet-loss statistics suffice to assess survival-time risk under bursty packet losses, i.e., the probability that no update succeeds for a prescribed number of consecutive slots. Conventional models, such as the Gilbert model, characterize packet losses using the loss rate and mean burst length, but channels sharing these statistics may exhibit substantially different failure risks. We characterize the Markov channels one order above a given low-order fit that share its statistics and analyze their risks under acknowledgment-based scheduling with transmission budgets. We show that, with dense transmission attempts, risk is primarily governed by loss persistence beyond the modeled memory, and the worst-case channel need not be the most persistent one. Under limited budgets, retry spacing makes loss relapse probabilities important and can reverse the risk ordering of compatible channels. Numerical and measurement results show that low-order models can substantially underestimate the risk, highlighting the need for scheduling-dependent channel statistics.
Shangqing Wang, Syed Irtaza Haider, Shiwei Shen, Faezeh Motazedian, Rico Radeke, Laura del Rio Carazo, Frank H. P. Fitzek
The integration of electric vehicles (EVs) into urban mobility systems offers both opportunities and challenges for cities pursuing sustainability and energy efficiency. While simulation-based approaches have demonstrated the technical feasibility of advanced charging strategies, such as smart charging, bidirectional charging, and Vehicle-to-Grid (V2G), their real-world impact is often constrained by policy, business models, and stakeholder engagement. This paper introduces a purpose-driven technology transfer (PDTT) framework that systematically connects simulation insights with the needs of urban stakeholders. Leveraging a modular simulation architecture based on Simulation of Urban Mobility (SUMO), we evaluate the operational and sustainability impacts of advanced EV charging in complex urban scenarios, assessing indicators such as energy efficiency and CO2 emissions. The PDTT framework guides the translation of simulation results into actionable pathways for deployment, policy design, and market innovation, supporting co-creation with city planners, utilities, and communities. Our findings highlight the value of simulation as a diagnostic and engagement tool, and demonstrate how a multidimensional, stakeholder-centered approach can accelerate the sustainable adoption of advanced EV charging technologies in smart city contexts.
Alexander Wietfeld, Luiz C. P. Wille, Pit Hofmann, Jonas Fuchtmann, Randy Gelfert, Frank H. P. Fitzek, Wolfgang Kellerer, Jens Kirchner
Future molecular communication (MC) networks are envisioned to use the human cardiovascular system as a channel. Disease-related changes affect particle transport, allowing an MC receiver to also act as a sensor. In this work, we investigate plaque formation in a carotid bifurcation through a systematic fluid-dynamics simulation study with non-Newtonian blood rheology. We analyze how plaque geometry changes branch-specific particle reception and timing, and use these observations to estimate plaque-induced channel obstruction. At an illustrative reference of only 128 emitted particles, a 250 ms observation window and 50 ms temporal resolution, a Ridge detector estimates maximum local channel-width reduction with a mean absolute error of 8.30 percentage points. We evaluate robustness across system constraints like particle budgets, observation windows, branch access, detection efficiency, count resolution, and synchronization. Coarse counting has little effect when timing features remain available, whereas limited temporal resolution and clock uncertainty substantially affect short-window estimates. Extending the observation window to 1 s reduces these timing-related penalties. The results establish a baseline for plaque sensing from constrained MC observations and identify receiver capabilities relevant to the Internet of Bio-Nano Things.