Signal Processing · Cross-listed from cs.IT
When to Stop? Dynamic Early Termination of Sequential Ensembles
Paul Bezner, Felix Krieg and Stephan ten Brink
Ordered-statistics decoding (OSD) post-processing substantially improves the performance of belief propagation (BP) ensembles, but it also removes their natural syndrome-based stopping criterion. The high computational complexity of ensemble decoding and OSD can be reduced by sequential activation of ensemble members paired with an early termination. Instead of a syndrome-based termination we therefore apply dynamic ensemble termination (DET) to sequential ensembles: members are evaluated one at a time and decoding stops when the estimated risk that an unseen member would correct the current decision falls below an offline-fitted threshold. With hardware-oriented design in mind, we instantiate the framework with layered normalized min-sum component decoders, a row-boosted ensemble, conditional low-order OSD on the dual code, and a conventional low-cost decoder gate. A decision tree estimates the residual risk from statistics of the current candidate list. Member-dependent thresholds then target a prescribed frame error rate (FER) loss. We validate this framework on mutliple IEEE 802.11 low-density parity-check (LDPC) codes with blocklengths \(648,1296\), and \(1944\) at rates \(1/2\) and \(5/6\). At each code's design point, where the full \(64\)-member list reaches an FER of \(10^{-3}\), the DET decoder processes \(1.04\) to \(1.15\) members on average. On the \((648,540)\) code, it reduces BP work by \(39\times\) and OSD activations by \(18\times\) against the full list while retaining an signal-to-noise ratio (SNR) gain of about \(0.4\,\mathrm{dB}\).
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Signal Processing · Cross-listed from cs.LG
Bridging the Data Gap: Digital Twin as a New Paradigm for AI-based Radio Sensing
Éloi Sainte-Beuve, Guillaume Larue, Louis-Adrien Dufrène et al. (+2 more)
We present a methodology that places a 3D digital twin (DT) of the environment as the main enabler behind the development of
radio sensing at scale. The DT acts as a world model, providing geometry, materials, and transmitter/receiver placements to a ray-tracing engine that generates time-indexed channel impulse responses (CIRs) for large numbers of plausible scenes (moving people and objects, layout variants, seasonal/weather conditions, etc). From these synthetic sequences, we train a sequential neural network that maps CIR time series to spatial occupancy estimates, enabling device-free localization (DFL) without instrumented targets. We posit that sensing is best approached as an environment-conditioned learning problem: rather than seeking a single global model, we advocate training or fine-tuning local models specialized to a site-specific DT. As a first experiment, we introduce a novel State Space Model architecture, trained and evaluated across multiple room geometries. The localization performances obtained demonstrate the potential of the approach.
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All authors Éloi Sainte-Beuve, Guillaume Larue, Louis-Adrien Dufrène, Quentin Lampin, Ali Al Khansa
Signal Processing
Hardware-in-the-Loop Phase-Aware CNN for Real-Time 5G Channel Estimation
Javad Zolfaghari-Bengar, Rakibul Rony, Elisa Gomez-de-Lope et al. (+3 more)
This demo presents real-time AI-based uplink channel-estimation inference using data collected from a hardware-in-the-loop 5G platform. The data-collection setup integrates commercial RF signal generation, programmable channel emulation, an O-RAN
Radio Unit, DU emulation, and a lightweight phase-aware convolutional neural network (CNN) that estimates the channel response directly from received DMRS signals. Unlike simulation-only evaluations, the hardware-derived
dataset exposes the
estimator to practical RF and system-level impairments, including calibration mismatches, synchronization imperfections, quantization effects, phase noise, and implementation-specific nonlinearities. During the demo, attendees will observe real-time CNN inference and channel reconstruction using captured hardware-generated DMRS observations and compare the proposed CNN against Least Squares (LS) and frequency-domain LMMSE baselines. The objective is to showcase a practical AI-native physical-layer inference pipeline that combines hardware-derived 5G data with real-time neural channel estimation for future 5G-Advanced and 6G systems.
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All authors Javad Zolfaghari-Bengar, Rakibul Rony, Elisa Gomez-de-Lope, Alejandro Villena-Rodriguez, Abhinav Mahadevan, Nicolas Kourtellis
Signal Processing
Extremely Large Beyond-Diagonal RIS: Low-Rank Modal Optimization for Near-Field Communications
Giovanni Iacovelli, Chandan Kumar Sheemar, Eva Lagunas et al. (+1 more)
Beyond-diagonal reconfigurable intelligent surfaces (BD-RIS) achieve their best performance when fully connected, at the price of an optimization and hardware burden that grows quadratically, and per iteration cubically, with the number of elements. Extremely large surfaces make this burden prohibitive, while their sheer aperture places both the base station and the users in the radiative near field, where far-field design tools break down. This paper introduces the extremely large BD-RIS (XL-BD-RIS) concept and shows that near-field geometry is precisely what makes fully connected performance affordable at scale. Modeling the cascade with the free-space Green function, we prove that the aperture fields live in a low-dimensional subspace spanned by the spherical-wave responses of the terminal positions, and we design a compact
unitary modal
matrix on this subspace, built from localization information alone, that provably attains the fully connected optimum with a number of reconfigurable entries set by the geometry and independent of the panel size. A weighted-MMSE Riemannian
algorithm optimizes the beamformers and the modal
matrix with monotone convergence at panel-size-independent cost. Numerical results show that a $24\times24$-element panel reaches the fully connected optimum with about two hundred entries instead of three hundred thousand. A mismatched DFT beamspace pays a sixty-fold entry penalty rooted in the beam spread of spherical wavefronts, while the classical block-wise architecture delivers strictly lower rates at any matched entry budget.
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All authors Giovanni Iacovelli, Chandan Kumar Sheemar, Eva Lagunas, Symeon Chatzinotas
Signal Processing
Laser-Diode LiFi With Diffused-Beam Optics: System-Level Modeling and a Cross-Validated ns-3 Simulation Framework
Hussain Ahmad, Syed Muhammad Talha Gillani, Toheed Omer et al. (+1 more)
Laser diodes (LDs) promise an order-of-magnitude bandwidth advantage over light-emitting diodes for indoor optical wireless access, but reported prototype studies frequently leave the gap between hardware demonstrations and system-level performance unquantified. This paper develops a complete, reproducible system model of a diffused-beam LD LiFi transceiver - a 500-mW
laser source beam-shaped by a holographic diffuser, an intensity-modulation/ direct-detection (IM/DD) receiver, and adaptive M-QAM signaling - and embeds it in two cross validated simulators: an open ns-3 module providing full-stack network simulation (channel, PHY, ARQ MAC, Net Device, IP/UDP/TCP) and a Python link-level engine used for Monte Carlo validation of all analytical error models. Starting from a hardware prototype that transferred data, real-time voice, and images over a 14-m line-of-sight link, we identify and close the technical gaps typical of prototype-class reports: serial-interface throughput ceilings misread as optical-link capacity, absent noise modeling, unmeasurable error floors, and unexamined beamwidth/coverage trade-offs. The framework shows that the same optical front end, freed of its 2-Mbaud UART bottleneck and driven at its 250-MHz electrical bandwidth, supports 930 Mb/s net at 14 m under a $3.8 \times 10^{-3}$ HD-FEC threshold with 16-QAM, scales to 1.86 Gb/s at 5 m with 256-QAM, and sustains on-off keying to 23.3 m; a $20^\circ$ diffuser covers a 4.2-m-radius
cell of a standard room at desk height. Network simulations over the ns-3 stack yield saturation goodput within 7% of the PHY line rate and sub-0.11-ms 99th-percentile latency at 70% load. All models, code, and figures are released for reproduction.
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All authors Hussain Ahmad, Syed Muhammad Talha Gillani, Toheed Omer, Saleem Aslam
Signal Processing
CII: Novel CSI-RS Metric for Joint Precoder and RIS Reporting in Multi-User NextG Networks
Ali Fuat Sahin, Sefa Kayraklik, Ali Gorcin et al. (+3 more)
While reconfigurable intelligent surfaces (RISs) are among the key enablers for next-generation (NextG) wireless networks, efficient feedback reporting for joint base station (BS) precoding and passive RIS configuration remains a major challenge due to the associated signaling overhead. By extending the standard-compliant channel state information reference signal framework, this paper introduces a novel channel information indicator (CII) that jointly represents the active BS precoding
matrix and passive RIS configuration within a single feedback metric for multi-user multiple-input single-output systems. Simulation results demonstrate that the proposed unified feedback framework significantly reduces uplink signaling overhead compared with conventional disjoint reporting schemes. Furthermore, despite only a modest increase in the feedback payload, the proposed CII-based scheme outperforms conventional precoding
matrix indicator approaches in terms of system performance, offering a practical and standards-compatible solution for RIS integration in NextG wireless networks.
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All authors Ali Fuat Sahin, Sefa Kayraklik, Ali Gorcin, Ibrahim Hokelek, Ertugrul Basar, Halim Yanikomeroglu
Signal Processing
Adaptive Source-Channel Coding for Bi-static Integrated Sensing and Semantic Communications
Haotian Wang, Dan Wang, Xiaodong Xu et al. (+4 more)
Semantic communication (SemCom) has emerged as a new paradigm to facilitate the performance of integrated sensing and communication systems in 6G, due to its potential to enhance transmission efficiency by transmitting task-relevant semantic features rather than raw bits. However, most of the existing works mainly focus on sensing data compression to reduce the subsequent communication overheads, without considering the integrated transmission framework for both the SemCom and sensing tasks. This paper proposes a sensing-aware adaptive source-channel coding (SA-ASCC) and beamforming design framework for bi-static integrated sensing and SemCom (ISSC) systems by jointly optimizing the coding rate for SemCom task and the transmit beamforming for both the SemCom and sensing tasks. Specifically, an end-to-end semantic distortion function is approximated by deriving an upper bound composing of source and channel coding induced components, and then a hybrid Cramér-Rao bound (HCRB) is derived for target position under imperfect time synchronization due to the transceiver deployed at different places in our considered bi-static ISSC system. To characterize the achievable region between SemCom and sensing performance, a distortion minimization problem is formulated by considering the HCRB threshold, channel uses, and power budget, which is non-convex due to the coupled design variables and the mixed-integer program. Subsequently, an alternating optimization (AO)
algorithm is proposed to decompose this problem into the model selection and joint rate and beamforming optimization subproblems, which are solved by the exhaustive search method and the combination of successive convex approximation and fractional programming, respectively. Finally, simulation results demonstrate that the proposed scheme outperforms the DJSCC-WF-ZF and BPG-WF-ZF
benchmarks.
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All authors Haotian Wang, Dan Wang, Xiaodong Xu, Chuan Huang, Hao Chen, Nan Ma, Ping Zhang
Signal Processing · Cross-listed from q-bio.NC
Modeling and Interpreting Correlations, Null Distributions and Significance Levels in Neural Tracking of Natural Stimuli
Simon Geirnaert, Alexander Bertrand, Tom Francart et al. (+1 more)
Neural tracking - the time-locking of neural responses to continuous stimuli such as
speech, music, and
video - is widely used to study how the
brain processes natural input. Tracking strength is typically quantified as the correlation between the recorded neural response and the stimulus, decoded and/or encoded through data-driven models, and this correlation is routinely used to compare stimulus features, models, or settings. However, its magnitude depends not only on how strongly the
brain tracks the stimulus, but also on the statistical properties of the signals being correlated. For example, a smallband
speech envelope carrying almost no information about
speech content yields among the highest correlations, simply because it is easier to reconstruct. Meaningful interpretation therefore requires comparing each correlation to its null distribution: the correlations expected without any stimulus-response relationship. We show that the randomization procedures commonly used to construct this null distribution are not interchangeable: each implicitly encodes a different null hypothesis, and we motivate stimulus-response misalignment as the most practical and appropriate choice. Because reliable null distributions require many permutations, we introduce a semi-parametric model using the normal distribution after the Fisher transform that yields accurate significance levels from only 3-5 min of data and predicts them across analysis window lengths. Building on this, we propose the null-normalized tracking score, an interpretable measure placing features and models on a common scale, which relates directly to the widely used match-mismatch accuracy. Applied to
EEG from 121 participants listening to continuous
speech, the framework reverses conclusions drawn from raw correlations, providing an efficient and principled methodology for interpreting neural tracking correlations.
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All authors Simon Geirnaert, Alexander Bertrand, Tom Francart, Jonas Vanthornhout
Signal Processing · Cross-listed from cs.NI
Multi-UAV Tracking Evaluation Using 5G Uplink Signals on an O-RAN ISAC Simulation Testbed
Arun K. Gurung and Satha K. Sathananthan
We evaluate multi-target detection, association and tracking end to end on an O-RAN simulation testbed built from OpenAirInterface, FlexRIC and Sionna RT that repurposes the 5G NR uplink sounding reference signal as a passive radar waveform, and against what a counter-UAS command-and- control (C2) consumer requires rather than by detection alone. Three
UAVs differing in altitude, velocity and radar cross section (-8 to -20 dBsm) fly one bistatic pair with an 8-element planar receive array. Once every target is detected the binding limit is contention, not sensitivity - two targets share one nearest detection in about 74% of coherent processing intervals, and targets are detected far more often than they are tracked. Elevation from that array cannot separate targets sharing a range-Doppler
cell, but it decides association in 58% of intervals. Concurrent tracks are exported from the RAN Intelligent Controller (RIC) xApp to a C2 fusion node over a SAPIENT interface carrying a calibrated detection confidence, validated at schema level against a mock fusion node. The evaluation is emulation-only on one geometry with 10 noise seeds, the mechanisms are characterized and analyzed to identify the key factors influencing multi-target tracking performance.
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Signal Processing
Optimal Measurement Point Selection for Radio Environment Maps: A Two-Stage Approach
Bastian Perner, Friedemann Laue, Maximilian Lubke et al. (+1 more)
Electromagnetic (EM) situational awareness is essential for the deployment and operation of local and temporary non-public networks (NPNs).
Radio Environment Maps (REMs) provide an effective basis for network planning and operation by capturing the prevailing EM conditions. However, efficient measurement location selection for REM construction remains a key challenge, particularly under strict time and cost constraints. This paper presents a two-stage measurement selection framework that employs Optimal Experimental Design (OED)-based strategies to generate a reduced candidate set during a pre-selection phase. In a subsequent optimization stage, combinatorial optimization is applied to select an improved subset of measurement locations by minimizing the Mean Absolute Error (MAE) of the resulting REM, using a scenario-specific propagation simulation model. The results demonstrate that the proposed framework reduces the number of required measurement locations while consistently improving REM construction accuracy across all evaluated strategies. Validation using real-world measurement data confirms the practical applicability of the approach, with the optimization stage achieving MAE reductions from about 4% to around 50%, independent of the chosen pre-selection strategy.
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All authors Bastian Perner, Friedemann Laue, Maximilian Lubke, Norman Franchi
Signal Processing
Unified Analytical Framework for Emergency RIS-UAV Networks Under Practical Impairments
Yinong Chen, Wenchi Cheng, Jingqing Wang et al. (+1 more)
Heterogeneous unmanned aerial vehicle (
UAV) networks embedded with reconfigurable intelligent surfaces (RISs) present a promising paradigm for emergency wireless communications (EWC), offering enhanced coverage and resilience in harsh environments. However, extreme conditions in disaster areas necessitate robust performance evaluation under practical impairments, including outdated/imperfect channel state information (CSI) and discrete RIS phase shifts. Existing works lack a unified analytical framework for modeling CSI errors, employing inconsistent approaches that treat errors either as channel gain or as equivalent interference, leading to ambiguous
benchmarks. To address this, we propose the $\zeta$-Model, a unified receiver-equivalent signal-to-noise (SNR) framework that continuously parameterizes residual-error exploitability via $\zeta$. This framework unifies the information-theoretic model (ITM) and the engineering baseline model (EBM) as the optimistic and pessimistic
benchmark receiver treatments, while incorporating the simplified engineering model (SEM) as a tractable approximation. By employing the Fisher-Snedecor $\mathcal{F}$ distribution to capture severe fading and shadowing, we derive moment-matching-based closed-form or finite-sum approximate expressions and asymptotic expressions for average capacity (AC), effective capacity (EC), and outage probability (OP) under the proposed unified framework and its boundary cases. Validated by Monte Carlo simulations, our framework quantifies performance limits and provides crucial insights for designing robust and efficient EWC systems under various channel conditions and system impairments.
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All authors Yinong Chen, Wenchi Cheng, Jingqing Wang, Jiangzhou Wang
Signal Processing
Robust Beamforming and Power Allocation for Coherent Cell-Free Massive MIMO with Residual Calibration Errors
Mingjun Sun, Xidong Mu, Shaochuan Wu et al. (+2 more)
This paper investigates robust downlink transmission to tolerate calibration aging in time-division duplex
cell-free massive multiple-input multiple-output (CF-mMIMO) systems with residual calibration errors (RCEs). Unlike existing studies that typically treat RCEs as static impairments, we develop a time-evolving RCE model that characterizes the joint effects of residual phase mismatches, residual carrier frequency offsets, and oscillator phase noise. Based on this model, two practical processing architectures are considered: instantaneous calibrated-channel-based robust beamforming (BF) and statistical beamformed-channel-based robust power allocation (PA). For both architectures, tractable achievable rate lower bounds are derived, which explicitly reveal the impact of calibration aging on coherent combining, BF-gain uncertainty, and inter-user interference. Using these lower bounds as design metrics, we formulate an effective weighted sum-rate (EWSR) maximization problem over the data transmission interval, so that the resulting BF and PA designs can account for the temporal
evolution of RCEs rather than a single calibrated instant. To efficiently solve the resulting problems, a Gauss–Legendre quadrature-based weighted minimum mean square error (WMMSE) optimization framework is developed, where both robust BF and PA are updated in an access point (AP)-block manner with closed-form solutions under per-AP power constraints. Simulation results demonstrate that: i) the proposed
algorithms exhibit stable convergence; ii) the proposed robust BF and PA schemes achieve higher EWSR by explicitly accounting for calibration aging than their non-robust counterparts; and iii) robust BF achieves higher spectral efficiency (SE), whereas robust PA provides a more favorable tradeoff between SE and implementation cost in terms of computational complexity and fronthaul overhead.
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All authors Mingjun Sun, Xidong Mu, Shaochuan Wu, Chongjun Ouyang, Hyundong Shin
Signal Processing
Statistical Channel Model for FSO Systems Assisted by a UAV-Mounted IRS
Ferdaous Tarhouni, Vasilis K. Papanikolaou, Laura Cottatellucci et al. (+2 more)
Integrating optical intelligent reflecting surfaces (IRSs) into aerial platforms, such as unmanned aerial vehicles (
UAVs), has been proposed to relax the line-of-sight (LoS) constraint, extend coverage, and enhance deployment flexibility of free space optical (FSO) systems. However, misalignment errors induced by the
UAV hovering, in both position and orientation, may degrade connectivity and impair the end-to-end channel quality. In this paper, we develop novel expressions for the electric fields incident on and reflected by an optical IRS, based on the Huygens-Fresnel principle. The resulting expressions are applicable for any combination of incident and reflected propagation directions. Building on this framework, we derive a closed-form statistical channel model that captures the
geometric and misalignment losses of an FSO link assisted by a
UAV-mounted IRS in the presence of random
UAV fluctuations. In particular, we develop a statistical model for the beam misalignment at the receiver lens, assuming Gaussian fluctuations in both the
UAV position and orientation. The proposed analytical model is validated through Monte Carlo (MC) simulations and is further used to provide practical design guidelines regarding the optimal placement of the
UAV-mounted IRS for the minimization of the outage probability.
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All authors Ferdaous Tarhouni, Vasilis K. Papanikolaou, Laura Cottatellucci, Mohammad-Ali Khalighi, Robert Schober
Signal Processing
AI-Assisted ISAC Localization-as-a-Service for 6G UAV-IoT Networks
Ruhul Amin Khalil
Integrated sensing and communication (
ISAC) can enable sixth-generation (6G) unmanned aerial vehicle-assisted Internet of Things (
UAV-IoT) networks to provide reliable Localization-as-a-Service (LaaS), but activating all aerial/terrestrial anchors and beams increases pilot overhead, energy use, and beam-training delay. This article proposes artificial intelligence (AI)-assisted
ISAC resource selection for LaaS (AIRS-LaaS), an edge-intelligent framework that ranks candidate anchor–beam pairs using line-of-sight (LoS) likelihood, signal-to-interference-plus-noise ratio (SINR), sensing confidence, geometry, mobility risk, and resource cost. A lightweight selector then activates only a compact subset before localization. Simulations compare AIRS-LaaS with all-anchor, Fisher information
matrix/Cramér–Rao lower bound (FIM/CRLB)-greedy, strongest-SINR, nearest-anchor, and random schemes under LoS/non-line-of-sight (NLoS) conditions and
UAV mobility. Results show a balanced localization–communication–overhead tradeoff, while the discussion highlights standard-driven key performance indicators (KPIs),
ISAC reporting, localization confidence, fallback operation, AI model management, and
privacy-aware data exchange.
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Signal Processing
Covert Communication with Spatially Heterogeneous User Cooperation Against a Geometry-Aware Warden
Hyeonsik Yeom and Jinyoung Lee
This paper investigates covert communication in which a single covert user is assisted by multiple spatially distributed non-covert users against a geometry-aware warden. Unlike prior works based on receiver-centric activation, homogeneous channels, or averaged geometry, the proposed framework retains the individual user-to-warden large-scale fading coefficients in both cooperative user activation and the warden's detection analysis. Under the adopted analytical formulation for large-scale user deployments, we establish an on–off activation structure that balances the interference caused at Bob and the interference effectiveness at warden. Based on the resulting aggregate-interference statistics, we derive a closed-form approximation of the detection error probability, the detection threshold that minimizes it, and the minimum number of cooperative users required to satisfy the covert constraint. Then, we reduce the original high-dimensional power-control problem to a one-dimensional piecewise search and develop a low-complexity
algorithm that avoids resolution-dependent grid search. The analysis shows that greater warden-side spatial dispersion can reduce the required cooperative users, whereas a larger average user-to-warden distance increases it. Numerical results validate the analysis and show that finite-sample detection approaches the large-sample
benchmark, while imperfect user-to-Bob channel estimation mainly reduces the achievable rate through activation errors.
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Signal Processing
Digital Twin-Aided Prescreening for User Scheduling in MU-MIMO Downlink Systems
Namhyun Kim, Mahmoud Saad Abouamer, Jeonghun Park et al. (+2 more)
In dense deployments, massive multi-user multiple-input multiple-output (MU-
MIMO) base stations can acquire instantaneous channel state information (CSI) for only a limited subset of users per scheduling interval, restricting multiuser diversity. We therefore propose Digital Twin User pre-Screening (DiTUS), a digital-twin (DT)-aided framework that identifies promising users before instantaneous CSI acquisition. DiTUS forms spatial covariances from DT-inferred departure angles and path powers. Optional Gaussian-process (GP) calibration mitigates path-power bias, while the dominant rank-r eigenspace of the aggregate covariance yields common reference beams. It prescreens the pool using DiTUS-P, a low-complexity projection-energy rule, or DiTUS-L, a greedy log-determinant rule that promotes spatial compatibility. A two-level protocol collects scalar beam reports from shortlisted users and requests r-dimensional effective-channel vectors only from the scheduled set. The framework also supports proportional-fair scheduling. At 15 dB under DT imperfections, simulations with 128 candidates, a 64-user effective-CSI acquisition budget, and a 64-user shortlist show that DiTUS-L achieves 35.06 +/- 0.49 bps/Hz versus 30.28 +/- 0.60 bps/Hz for semi-orthogonal user selection (SUS) with full-dimensional CSI from 64 users, demonstrating that DT-based prescreening preserves substantial multiuser-diversity gains by identifying strong, spatially compatible users before acquiring effective-channel vectors.
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All authors Namhyun Kim, Mahmoud Saad Abouamer, Jeonghun Park, Petar Popovski, Ahmed Alkhateeb
Signal Processing
Neural implants and human safety: single-fault detection for DC-coupled recording front ends
Dimitris Antoniadis and Timothy Constandinou
DC-coupled analogue front ends (AFEs) for neural implants provide a low-area solution. However, removing the coupling capacitor eliminates the intrinsic barrier that protects cortical tissue: a single-fault event, such as gate-oxide breakdown of a low-noise amplifier (LNA) input transistor, can open a direct DC path from the supply rail into the
brain. On the stimulation side this hazard is well understood, and single-fault tolerance is enforced by a series DC-blocking capacitor; on the recording side, DC-coupled front ends discard the equivalent safeguard, yet their protection has gone almost unexamined. This paper presents a single-fault detection mechanism that monitors the LNA for the DC imbalance produced by such a failure and disables the amplifier before the resulting fault current can irreversibly damage tissue. The imbalance is encoded in the duty cycle of a current-starved relaxation oscillator and read out as a time-to-digital measurement. Designed in 65 nm, the mechanism resolves a worst-case fault of 6.4 nA across all corners within 0.81 ms - compliant with the ISO~14708-3 limit for an 8533 um2 electrode - opening a broader discussion of safety in DC-coupled recording.
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