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Hans D. Schotten

Publications and source records attributed to Hans D. Schotten.

At least 19 recordsLinked to original sources

Coded Fourier-Curve Constellations with Tangent Artificial Noise: Covariance-Aware Demapping, Complexity, and Key Sensitivity

We realize and evaluate the covariance-aware soft demapper for a coded link over phase-keyed Fourier-curve constellations with tangent artificial noise (AN). A phase key shared by transmitter and legitimate receiver instantiates a codebook of $M$ points on a closed curve through $k$ complex slots; AN along the curve's tangent gives every symbol a Gaussian observation with a symbol-dependent rank-one covariance. The maximum-likelihood symbol metric then differs from the Euclidean rule by one rank-one correction per candidate, realized as a max-log demapper beside a Euclidean matched-filter bank at $2kM$ extra multiply--accumulate operations per symbol and a $10$\,KB lookup table. On a regular $(3,6)$ LDPC-coded link at $(k,M){=}(20,64)$ it reaches BLER${=}10^{-1}$ about $5$\,dB earlier than Euclidean demapping under natural labeling and $1.0$\,dB earlier under Gray labeling, the better labeling for both; whitening only the average AN covariance recovers $0.7$\,dB of the natural-labeling gap, the rest being due to the candidate-specific covariance label. A bit-interleaved coded-modulation achievable-rate computation corroborates the ordering, a Woodbury extension keeps the rank-one structure under per-tone Ricean fading, and $6$-bit lookup-table quantization costs no measurable coded-BLER degradation. Finally, the key is a modulation parameter rather than a secret: decoding needs a per-component key accuracy of about $0.5$\,rad, and a design-aware receiver recovers it to within $0.1$\,rad from the third-order moments of a single LDPC block, decoding at the legitimate receiver's level, so we make no secrecy claim.

cs.IT

Confusion-Erasure Bounds of Error-Bounded Decoders under QAM

6G is expected to push ultra-reliable low-latency communication (URLLC) toward stringent residual-error targets for mission-critical services, where undetected errors and erasures carry fundamentally different costs. Block error rate (BLER) conflates block confusions (undetected errors) and block erasures, which have fundamentally different impacts on system reliability. This paper extends the confusion and erasure analysis of error-bounded decoders to square quadrature amplitude modulation (QAM) constellations in the finite blocklength (FBL) regime. To handle QAM's heterogeneous symbol energies - which make the per-pair Euclidean distance a distribution rather than a single value - we derive analytical lower and upper bounds on the block confusion rate by, respectively, collapsing this distribution to its root-mean-square (RMS) distance and averaging the pairwise confusion over it. These bounds are proven to be monotonically decreasing in both the average symbol energy and the blocklength, with the decrease rate governed by the constellation order. Numerical results confirm that as the signal-to-noise ratio (SNR) or redundancy increases, the confusion rate falls many orders of magnitude below the reliability target, leaving detectable erasures as the dominant residual error.

cs.IT

Symbol-Domain Chase Combining on Fourier-Curve Constellations: Exact Penalties of Per-Round Bit Reduction

A Fourier-curve constellation places $M$ symbols on a closed curve in $\R^{2k}$ and injects artificial noise along the tangent at the transmitted symbol, so every symbol candidate carries its own rank-one noise covariance; a Chase retransmission repeats one such $M$-ary symbol. How should the covariance-aware receiver combine the repeated observations? It can accumulate the $M$ candidate metrics and form bit log-likelihood ratios (LLRs) once, or it can form bit LLRs in every round and add them. The rounds are independent given the symbol but not given a single label bit, so even exact per-round bit LLRs do not add up to the joint-round LLR. We derive exact identities for the gap under log-sum-exp and max-log reduction, with their equality conditions; they hold for any repeated $M$-ary symbol, grow with the number of rounds, and vanish for binary signaling. On the Fourier channel with a rate-$1/2$ LDPC code after $L=4$ rounds, per-round max-log reduction needs $1.95$ dB more per-slot SNR at block error rate $10^{-1}$ than even covariance-ignorant Euclidean accumulation. Optimized bit-metric generalized mutual information puts the SNR penalty of per-round exact reduction at the code-rate threshold at $2.7$ dB on the Fourier channel and $1.5$ dB on Gray 64-QAM, joint max-log costs less than $0.1$ dB, and a 5G NR LDPC code on Gray 64-QAM loses $1.6$ dB at $L=4$. Controls with Gray labeling, isotropic noise, and $β=0$ show that the loss does not depend on the symbol-dependent covariance, whose own effect is the separate matched-versus-Euclidean correction. The Fourier receiver should therefore accumulate matched candidate metrics across rounds and reduce to bits once.

eess.SP

Orchra: Stateful-aware Cross-slice Workload Migrations in the 6G Control Plane

Network slicing is a foundational capability of Fifth Generation (5G)-Advanced and emerging Sixth Generation (6G) networks, yet practical support for seamless runtime slice transitions remains limited. Standard cloud-native 5G architectures lack native support for stateful inter/intra-slice session migration, relying instead on high-overhead Non-Access Stratum (NAS) re-registrations, container redeployment etc., which disrupt userplane traffic for up to 245.50 ms. To address this limitation, we present Orchra, an intelligent orchestrator for stateful, low-latency context transfer. By externalizing critical user equipment state-including NAS context, security keys, and Protocol Data Unit (PDU) session information-into a transient staging layer, Orchra preserves session continuity across slice boundaries without requiring full re-registration. Experimental evaluation shows that Orchra reduces this userplane interruption by more than twice in comparison to conventional Third Generation Partnership Project (3GPP)-based approaches while incurring negligible security overhead. These results demonstrate a practical and reproducible approach for enabling seamless, state-preserving slice transitions in cloud-native 5G-Advanced networks.

cs.NI

Integrated Sensing and Communications over Hierarchical Cellular and Cell-Free MIMO Systems

This paper studies integrated sensing and communications (ISAC) over a hybrid system that seamlessly combines legacy cellular base stations with distributed cell-free (CF) access points (APs). We propose a hierarchical ISAC architecture where a central base station (CBS) serves its near users and simultaneously operates as a monostatic radar for aerial target detection, while distributed APs---many idle under user-centric clustering---act as cost-free bistatic receivers. The CBS jointly handles communication processing and multi-static sensing fusion, reducing fronthaul overhead compared to conventional cell-free ISAC. To achieve this, a five-phase time-division duplexing workflow with precise ISAC role assignment is specified. Closed-form expressions for spectral efficiency and multi-static sensing signal-to-noise ratio analytically characterize the communications--sensing Pareto frontier. Numerical results confirm that the proposed hierarchical design simultaneously achieves higher sum throughput and superior sensing accuracy than conventional cell-free ISAC.

cs.IT

A Communication-Efficient Digital Twin Framework for PSO-Based Swarm Navigation and Obstacle Avoidance

Swarm-based target localization in industrial environments faces two major challenges: navigating obstacle-rich spaces and managing intensive communication among agents. This paper proposes a communication-efficient digital twin (DT) framework for Particle Swarm Optimization (PSO)-based swarm navigation and obstacle avoidance. The DT, deployed on a Multi-Access Edge Computing (MEC) server, maintains a virtual replica of the environment to provide global guidance and obstacle bypassing when agents become trapped or experience poor connectivity. By reducing unnecessary peer-to-peer communication and centralizing environmental information, the proposed framework improves both navigation efficiency and communication resource utilization. Simulation results demonstrate that the DT-assisted PSO with obstacle avoidance achieves faster convergence and significantly lower communication load compared with decentralized P2P and random-walk PSO approaches. These findings highlight the potential of integrating DT with swarm intelligence to enhance cooperative exploration in complex industrial scenarios such as chemical leakage localization.

cs.RO

Self-Healing 6G Networks-in-Network for Resilient Wireless Communication

Future 6G networks must manage increasingly dynamic radio environments in which multiple autonomous sub-networks (SNs) share frequency resources and adapt to changing operating conditions. In such scenarios, interference from faulty devices or intentional jamming can disrupt ongoing communications, making rapid and autonomous network adaptation essential. This demonstration presents a self-healing networks-in-network (NiN) architecture that closely integrates the detection of spectrum anomalies with dynamic spectrum management. A spectrum scanner continuously monitors the frequency spectrum and forwards detected anomalies to the DSM, which automatically identifies suitable frequency resources and reconfigures the affected SN. During the live demonstration, participants can initiate controlled disruptions and observe the entire adaptation process in real time, from anomaly detection to autonomous frequency reallocation and network recovery. The demonstrator illustrates how integrating spectrum monitoring and resource management into a single control loop can improve the resilience of future NiN implementations and demonstrates a practical approach to autonomous, spectrum-aware networking.

cs.NI

Network Verified NTN Positioning for 6G A Standards Oriented Survey of Hybrid TN NTN Localization

Wireless positioning is evolving from legacy cellular and standalone satellite navigation toward hybrid, high-accuracy, three-dimensional (3D), and network-verifiable frameworks for 5G Advanced, 6G, and non terrestrial network (NTN) systems. This shift is driven by intelligent transportation, unmanned aerial vehicle (UAV) operation, low-altitude economy services, public warning, and regulated NTN use cases that require not only accurate location estimates, but also vertical awareness and verification confidence. This survey reviews the evolution from Long Term Evolution (LTE) and Global Navigation Satellite System (GNSS) positioning to New Radio (NR), Release-18/19 positioning enhancements, and NTN-enabled 3D positioning. It synthesizes observable families, 3rd Generation Partnership Project (3GPP) standard evolution, method-level tradeoffs, and open challenges for hybrid terrestrial network (TN) NTN designs, with emphasis on standardization impact, vertical observability, reliability-aware measurement selection, and network-side verification.

eess.SY

Switched-Feed Pinching-Antenna Systems for Wideband Terahertz Communications

The pinching-antenna system (PASS) uses dielectric particles along a low-loss waveguide as reconfigurable passive radiators. Existing analyses conclude that the in-waveguide attenuation is negligible at low frequencies and millimeter wave bands; we show this fails at terahertz (THz), where realizable waveguide losses are dramatically larger. We develop a unified wideband THz-PASS propagation model integrating in-waveguide attenuation, atmospheric absorption, molecular re-radiation noise, and beam squint. Closed-form results follow: a band-averaged coherence factor; a cluster-center placement satisfying a band-edge SINR equalization condition; an associated placement-inversion threshold; and a proposed \emph{Switched-Feed PASS} (SF-PASS) architecture in which a centrally located radio-frequency switch routes the signal among multiple waveguide segments, with a closed-form insertion-loss payoff threshold. Numerical evaluation at the best PASS-compatible THz operating point shows that SF-PASS substantially outperforms single-feed PASS in spectral efficiency and is competitive with a large-scale antenna array at much lower hardware costs.

cs.IT

Pinching-Antenna-Assisted Terahertz Communications: Modeling and Benchmarking

Pinching antenna systems (PASS) employing dielectric waveguides have recently emerged as a promising flexible antenna architecture for high-frequency wireless communications. While prior work has focused primarily on millimeter-wave regimes, extending PASS to the terahertz (THz) band introduces distinct electromagnetic phenomena that invalidate conventional modeling assumptions. This paper develops the first analytical framework for THz-PASS that integrates in-waveguide propagation attenuation, evanescent coupling via coupled-mode theory, and THz-specific free-space effects including molecular absorption and its re-radiation noise. Using this model, we benchmark THz-PASS against conventional phased arrays under identical propagation scenarios. Our comparative evaluation reveals that THz-PASS achieves effective gains in spectral efficiency through proximity exploitation, making it particularly well-suited for confined and linear deployment topologies.

cs.IT

Superimposed Transmission for Cooperative Cellular and Cell-Free Massive MIMO Systems

This paper proposes a superimposed transmission strategy for cooperative cellular and cell-free massive MIMO systems. By classifying users into near and far, the base station transmits an additional data symbol for each near user, superimposed on the signals from distributed access points. Successive interference cancellation is employed at near-user receivers to decode both symbols. The proposed strategy achieves the highest peak spectral efficiency while maintaining fairness at the cell edge, thereby outperforming all the existing network configurations in system capacity.

cs.IT

Balancing Functionality and GDPR-Driven Privacy in ISAC Trajectory Sharing

Integrated Sensing and Communications (ISAC) enables trajectory sharing that enhances beamforming, resource allocation, and cooperative perception, yet raises fundamental privacy concerns under the General Data Protection Regulation (GDPR) data minimisation principle. This paper proposes a Fisher Information Density (FID)-constrained trajectory sharing framework that enforces a local lower bound on estimation uncertainty, providing hard, quantifiable privacy guarantees by construction. Unlike fixed-noise approaches, the proposed method bounds the Privacy Leak Ratio (PLR) regardless of sensing power or adversarial post-processing, ensuring that no trajectory segment can be reconstructed beyond a prescribed accuracy threshold. Simulations on the OpenTraj dataset demonstrate that the framework keeps the average PLR below 20-25% and the maximum leakage segment duration under 2-2.5 s, while preserving data utility for downstream tasks such as movement prediction. The resulting criterion is interpretable, model-agnostic, and compatible with GDPR-compliant ISAC system design.

eess.SP

GDPR-Aware Trajectory Sharing for ISAC-Assisted Robot Navigation: A Case Study on FID-Constrained Collision Prediction

Integrated sensing and communication (ISAC) enables intelligent wireless infrastructure but raises growing regulatory concern as fine-grained personal trajectory histories become a byproduct of sensing. General Data Protection Regulation (GDPR) Articles 5(1)(c) and 5(1)(f) require that personal data be limited to what is necessary and protected through appropriate technical measures against unauthorised reconstruction. This paper addresses both requirements through a Fisher information density (FID)-constrained trajectory sharing scheme for robot collision avoidance, where sensing estimates are perturbed according to local information content before sharing. Experiments on real pedestrian traces show that FID-controlled sharing achieves a strictly better privacy-utility tradeoff than fixed-error perturbation: at matched missed-conflict rates, reconstruction leakage and sustained exposure lengths are consistently lower, establishing information-aware perturbation as a principled technical measure aligned with GDPR data minimisation and integrity requirements.

cs.RO

Atomic Handover for 6G Nomadic Non-Public Networks Using Edge-Based Spectrum Brokering

Nomadic Non-Public Networks (NNPN) are expected to play an important role in future 6G systems by enabling mobile and rapidly deployable network infrastructures for scenarios such as emergency response or temporary events. In such environments, maintaining seamless connectivity is challenging, as both network attachment and spectrum access may need to be adapted simultaneously when moving across heterogeneous infrastructures. In this paper, we investigate handover mechanisms for NNPN and propose a zero-touch approach that jointly considers mobility management and dynamic spectrum coordination. The proposed architecture introduces an edge-based Spectrum Broker in combination with a Cognitive Spectrum Manager to support an atomic handover procedure, where network selection and spectrum allocation are performed in a single step. The concept is evaluated using a MATLAB-based simulation of a mobile healthcare scenario, where an ambulance with its NNPN transitions between Public Land Mobile Networks (PLMN) and Non-Public Networks (NPN).

cs.NI

The Economics of Autonomy: Real-Time Risk Indexing for Insurable AI-Driven 6G Systems

The transition to sixth-generation (6G) networks transforms wireless infrastructure into a cognitive substrate supporting Vehicle-to-Everything (V2X), Industrial IoT (IIoT), and Integrated Sensing and Communication (ISAC). In this paradigm, autonomous agentic AI performs orchestration at millisecond scales, rendering traditional static governance frameworks fundamentally inadequate for risk management. This paper introduces GIRAF(Governance-Integrated Risk and Assurance Framework), a Governance-as-Code (GaC) framework for real-time risk quantification and trust modulation in agentic 6G systems. GIRAF derives a continuous Aggregate Risk Index ($R_{t}$) from machine-readable runtime signals, including epistemic confidence, network jitter, and verification latency. A core contribution is the formalization of the verification staleness trade-off, where safety mechanisms induce risk if computational latency exceeds 6G deadlines. We demonstrate that GIRAF identifies 'Confidence Gaps' discrepancies between agent reported certainty and environmental ground truth, triggering automated safety envelopes when conditions deteriorate. Crucially, GIRAF serves as the foundational governance groundwork and conceptual 'glue' that externalizes these technical risks into machine-readable telemetry. Through simulations with fine-tuned Large Language Models (LLMs), we validate that the framework preserves operational integrity while providing the essential actuarial baseline required for multi-stakeholder liability attribution and dynamic premium quantification in the 6G ecosystem.

cs.NI

Steins;Gate Drive: Semantic Safety Arbitration over Structured Futures for Latency-Decoupled LLM Planning

Cloud-hosted LLM driver agents provide useful semantic judgments, but their inference latency exceeds stepwise vehicle-control windows. Learned world models predict futures, but they usually keep future generation and action selection inside large coupled loops. We present SteinsGateDrive, a latency-decoupled planner-runtime architecture in which the worldline metaphor from the eponymous story names one plausible consequence of an intervention: the LLM selects counterfactual driving futures before the final control instant, and a runtime reuses the selected forecast only while safety contracts remain valid. The generator builds three world-line roles: alpha nominal ego-conditioned futures, beta interaction counterfactuals around nearby vehicles, and gamma hazard-stress futures such as braking, cut-ins, or blocked corridors. The selected branch becomes a typed StrategicForecast with horizon, validity/abort conditions, fallback, and authority. On a within-subject, matched-seed normal-highway protocol with 10 seeds and 20 steps, GPT-5.4 mini reduces effective lag from +3.07 s at 1-second horizon to -0.01 s at 4-second horizon while preserving the measured no-collision safety boundary. The architecture's safety contribution comes from the atom-predicate runtime check, not from the drift score, which functions as a refresh-frequency knob.

cs.RO

A Multi-Layer Cloud-IDS Pipeline with LLM and Adaptive Q-Learning Calibration

Security in cloud computing has become a major concern due to several factors such as layered cloud architectures, dynamic environments, and exposure to unseen or zero-day attacks. Moreover, intrusion detection systems (IDS) typically operate at specific layers and rely heavily on machine learning models, which often perform well in experimental settings but fail to sustain performance in real cloud deployments. In this work, we implement a confidence-aware multilevel intrusion detection system using reinforcement learning tailored for cloud environments. The system secures three distinct layers: network, host, and hypervisor. Machine learning models at each layer detect known attack patterns, while prediction confidence distinguishes reliable decisions from uncertain outcomes. Within the multi-gate flow, low-confidence events pass through a learned-threshold confidence gate (Gate-1), followed by a Chroma memory-matching gate (Gate-2), with unresolved events escalated to a large language model (LLM) for semantic analysis and explanation. Final attack promotion at Gate-3 uses calibrated LLM confidence or weighted-fusion fallback, while uncertain events are retained in a review bucket to avoid forced classification. Generated explanations and confirmed knowledge are stored in ChromaDB to support future analysis and retraining. The approach is first evaluated using static thresholds, establishing a baseline for comparison. Results show that the proposed system learns adaptive thresholds and reduces LLM escalation by 58.78%, lowering cost while maintaining strong performance (88.68% accuracy, 85.29% precision, 84.72% recall, 85.00% F1). The network and hypervisor layers achieve 98.02% and 97.08% accuracy, demonstrating a balanced and efficient detection system.

cs.CR

Comparative Analysis of Direct-to-Cell (D2C) and 3GPP Non-Terrestrial Networks (NTN) for Global Connectivity

The quest for ubiquitous mobile coverage has catalyzed two fundamentally distinct architectural paradigms: Direct-to-Cell (D2C) and standardized 3GPP Non-Terrestrial Networks (NTN). D2C, pioneered by SpaceX Starlink and AST SpaceMobile, leverages existing terrestrial spectrum and unmodified consumer handsets to provide emergency connectivity as a market-driven overlay. In contrast, 3GPP NTN, standardized across Releases 17-19, offers a systematic satellite-native framework designed for long-term scalability, high-throughput broadband, and deep integration with terrestrial 5G/6G networks. This paper presents a comprehensive technical comparison of these approaches, analyzing their standardization trajectories, network architectures, physical-layer innovations, security postures, and operational trade-offs. We further examine their implications for emerging 6G use cases, particularly autonomous driving, where safety-critical redundancy motivates a hybrid tri-link architecture combining terrestrial 5G, NTN broadband, and D2C emergency fallback. Our analysis shows that, although D2C enables rapid market entry through legacy-device compatibility, NTN provides superior performance, security, and scalability, positioning it as the foundational framework for 6G satellite-terrestrial convergence. A hybrid model that combines the strengths of both paradigms is identified as the most practical path toward truly global connectivity.

cs.NI