arXiv subjects

Christoph Studer

Publications and source records attributed to Christoph Studer.

At least 19 recordsLinked to original sources

arXiv2026

An Open-Source Standard-Cell Library for IHP 130nm Developed by Students

Standard-cell libraries form the critical interface between transistor-level circuit design and automated digital design flows, yet their design and characterization are rarely covered in depth in digital VLSI courses. This paper presents VLSI 5, a graduate-level course at ETH Zurich in which students design, lay out, characterize, and integrate their own standard cells using IHP's open-source 130nm SG13G2 process design kit. In the first course offering, 19 students developed EZ130 8T, an open-source eight-track standard-cell library comprising 220 cells. We evaluate successive library versions using eight synthesis benchmarks, across which EZ130 8T achieves significant area reduction compared to the SG13G2 library at competitive timing. For a postlayout 8x8 matrix-vector multiplier, EZ130 8T reduces cell area by 35% and energy by 39% while maintaining virtually the same clock period. The library has already been adopted in other ETH Zurich VLSI courses, enabling the creation of chips entirely designed by students down to the transistor level.

cs.AR↗

arXiv2026

Supervised Device Charting with CSI Measurements from Commercial 5G NR User Equipments

Radio frequency fingerprint identification (RFFI) is a promising approach to distinguish physical wireless devices using hardware-induced signal imperfections. Conventional RFFI methods only provide discrete device labels and no human-interpretable representation of the relations among received signals. We propose supervised device charting, which maps location-insensitive channel-state information (CSI) fingerprints to a low-dimensional chart that visualizes cluster compactness, overlap, and outliers. We evaluate the method with real-world 5G New Radio (5G NR) measurements from six commercial smartphones and introduce the neighbor label error rate (NLER) to quantify class-separation accuracy. Our results demonstrate that two- and three-dimensional device charts provide an interpretable visualization of the learned RFFI representation. For three-dimensional device charts, the NLER is 0.22% for same-day measurements and 7.38% for measurements from the next day. The device charts reveal a cross-day distribution shift and map the held-out device close to the known device of the same model. Increasing the device chart dimensions further improves cluster separation at the expense of interpretability.

eess.SP↗

arXiv2026

On the Impact of Site-Specific Training for a Real-World 5G NR System

Site-specific training can improve wireless receiver performance without increasing computational complexity. However, real-world results have so far focused on fully trainable neural receivers and single-layer transmissions. We study site-specific finetuning of three receiver architectures: fully trainable neural, model-driven neural, and model-based. We train and evaluate these receivers using new measurements from a standard-compliant 5G NR testbed at ETH Zurich with dual-layer uplink transmission, including measurement campaigns conducted more than six months apart. Our results show that site-specific finetuning (i) substantially improves fully trainable and model-driven neural receivers, while resulting in only marginal gains for the less tunable model-based receiver; (ii) enables a single neural receiver jointly finetuned for single- and dual-layer transmission to closely match receivers finetuned separately for each configuration; and (iii) remains effective across measurement campaigns separated by more than six months. We also investigate site-specific linear minimum mean-square error channel estimation using covariance matrices estimated from either synthetic channels or site-specific measurements. When combined with iterative detection and decoding, site-specific channel estimation achieves the lowest error rate observed in our datasets. Our finetuning code and measurement datasets are publicly available at https://github.com/IIP-Group/site_specific_training

cs.IT↗

arXiv2026

PolyMap: A 64-Channel Polyphonic Guitar Pickup System

In electric guitars, the vibrations of the strings are typically sensed by coils of wire combined with a magnet, called pickups. The pickups and their position along the strings contribute strongly to the instrument's sound. Most guitars feature one to three pickups, each spanning across all strings with fixed positions and generating a single mono output. The work of this Master's Thesis at ETH Zürich introduces a new pickup system called PolyMap, which senses each string individually and at multiple locations. The system is demonstrated with a custom-made eight-string guitar that contains eight pickups per string for a total of 64 pickups. The signals from these 64 pickups are individually digitized inside the guitar and transmitted over a multichannel audio digital interface (MADI), a low-latency digital audio interface, to a computer for further processing. PolyMap enables high-resolution sensing of an electric guitar's strings and enables extensive post-processing capabilities for musicians, audio engineers, and researchers. To the best of our knowledge, this is the first polyphonic guitar pickup system with such a complete feature set.

eess.AS↗

arXiv2026

Alias-Free Oscillator Synchronization via Additive Synthesis

Oscillator synchronization is a widely used sound-synthesis technique, but straightforward digital implementations suffer from aliasing artifacts. This paper presents an alias-free method for digital emulation of oscillator synchronization of arbitrary periodic waveforms based on additive synthesis. Starting from a finite set of Fourier-series coefficients representing a bandlimited free-running waveform, we derive linear spectral-resampling transforms that map these coefficients to those of the bandlimited synchronized waveform. Beyond conventional hard synchronization, the proposed approach also supports two additional soft-synchronization modes. To address the high computational complexity of the proposed method, we introduce HASY, a 6 mm^2 application-specific integrated circuit (ASIC) fabricated in 65 nm CMOS technology. HASY generates one 96 kHz, 24 bit alias-free synchronized waveform with up to 512 harmonics and computes the spectral-resampling transform within only five audio-sample periods.

eess.AS↗

arXiv2026

A Frequency-Domain Artificial Reverberator Plug-In

We present FDverb, a frequency-domain artificial reverberator, based on the idea of a vocoder with a noise carrier signal. Using a short-time Fourier transform (STFT) for analysis and synthesis, FDverb generates late reverberation by weighting spectral noise components with envelopes. We extend FDverb with early reflections, nonlinear decay, and pitch shifting. These extensions enable creative sound-design applications. We provide FDverb as an open-source DAW plug-in, using the JUCE framework.

eess.AS↗

arXiv2026

A 39pJ/b 7.3Gbps 1.3mm$^2$ Multi-Subcarrier Massive MU-MIMO-OFDM Detector Exploiting Beamspace Sparsity and Frequency-Domain Correlation in 22FDX

We present the first multi-subcarrier massive multi-user (MU) multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) data detector reported in the open literature. By exploiting the channel's beamspace sparsity and frequency-domain (FD) correlation, we achieve up to 3x area and power reduction. Our design supports U=8 user equipments and B=64 basestation antennas, computes soft outputs for QPSK to 256-QAM, and processes 16 subcarriers in parallel. The fabricated 22FDX ASIC has a core cell area of 1.3mm$^2$, consumes 286mW, and delivers a throughput of 7.3Gbps at 0.8V core voltage, achieving best-in-class energy efficiency of 39pJ/b.

eess.SP↗

arXiv2026

Positioning via Digital-Twin-Aided Channel Charting with Large-Scale CSI Features

Channel charting (CC) is a self-supervised positioning technique whose main limitation is that the estimated positions lie in an arbitrary coordinate system that is not aligned with true spatial coordinates. In this work, we propose a novel method to produce CC locations in true spatial coordinates with the aid of a digital twin (DT). Our main contribution is a new framework that (i) extracts large-scale channel-state information (CSI) features from estimated CSI and the DT and (ii) matches these features with a cosine-similarity loss function. The DT-aided loss function is then combined with a conventional CC loss to learn a positioning function that provides true spatial coordinates without relying on labeled data. Our results for a simulated indoor scenario demonstrate that the proposed framework reduces the relative mean distance error by 29% compared to the state of the art. We also show that the proposed approach is robust to DT modeling mismatches and a distribution shift in the testing data.

eess.SP↗

arXiv2026

Physically Consistent Evaluation of Commonly Used Near-Field Models

Near-field multi-antenna wireless communication has attracted growing research interest in recent years. Despite this development, most of the current literature on antennas and reflecting structures relies on simplified models, whose validity for real systems remains unclear. In this paper, we introduce a physically consistent near-field model, which we use to evaluate commonly used models. Our results indicate that common models are sufficient for basic beamfocusing, but fail to accurately predict the sidelobes and frequency dependence of reflecting structures.

eess.SP↗

arXiv2026

A Physically Consistent Assessment of Nearfield Beamfocusing into Occluded Regions

Recent work has explored elaborate beamfocusing techniques for the radiative nearfield, with some studies suggesting that certain beamshapes, such as Airy beams, can enable efficient electromagnetic (EM) wave transmission behind obstacles. In this letter, we ask whether the added complexity of such techniques is justified. We distinguish between partially and fully occluded regions. In the partially occluded region, where Airy beams are commonly employed, we show that a simple line-of-sight (LoS) strategy, which activates only antennas having an unobstructed view of the receiver, is near-optimal and outperforms Airy beams at substantially lower complexity. In the fully occluded region, we argue that accurate beamfocusing requires a physically consistent EM wave propagation model that captures propagation effects such as diffraction. Once such a model is available, however, the optimal beamfocusing strategy has a closed-form solution and can be computed directly. These results suggest that elaborate techniques, such as Airy beams, offer little benefit over simpler alternatives for beamfocusing into occluded regions.

eess.SP↗

arXiv2026

Recovery of Signals with Low Density

Sparse signals (i.e., vectors with a small number of non-zero entries) build the foundation of most kernel (or nullspace) results, uncertainty relations, and recovery guarantees in the sparse signal-processing and compressive-sensing literature. In this report, we study a signal-density measure, the ratio between the $\ell_1$-norm and the $\ell_\infty$-norm of a vector, which extends the common notion of sparsity to non-sparse signals whose entries' magnitudes decay rapidly. By taking into account such magnitude information, we derive a kernel result and an uncertainty relation that are more general and less restrictive than those based on the $\ell_0$-pseudonorm. Furthermore, we use this density measure to analyze orthogonal matching pursuit (OMP). We show that OMP provably (i) recovers sparse signals with decaying magnitudes using up to 2$\boldsymbol\times$ more non-zero coefficients than guaranteed by standard, sparsity-based results and (ii) identifies the largest entries of arbitrary signals under a suitable magnitude-decay condition.

cs.IT↗

arXiv2026

Multibit Quantized Precoding for MU-mMIMO

We propose a novel multibit quantized precoding method for the downlink of multi-user massive MIMO systems with low-resolution digital-to-analog converters. The new method, termed multibit quantized precoding (MQP), enforces the finite-alphabet constraint through an l0-norm penalty, approximated by a smooth surrogate so as to yield a reformulated problem, which is then convexized via fractional programming, ultimately extending quantized precoding beyond 1-bit alphabets. The regularization parameter of the proposed method is selected via a discrepancy principle integrated with graduated non-convexity continuation, resulting in a principled and reproducible hyperparameter tuning method and an efficient iterative algorithm with a closed-form, least-squares-type update per iteration. In order to further reduce the computational complexity of the method, we include a Gaussian belief propagation (GaBP) step for turning the least-squares update in linear-time. Simulations performed for systems with different sizes demonstrate that both methods, namely the MQP with and without GaBP, achieve competitive or superior error-rate performance compared to state-of-the-art quantized precoding algorithms under various channel conditions.

eess.SP↗

arXiv2026

Spatial and Temporal Generalization of CSI-based Neural Positioning

Channel state information (CSI)-based neural positioning learns a mapping from CSI measurements to user equipment (UE) positions using neural networks. However, most existing performance evaluations utilize randomly partitioned train/test CSI-dataset splits, which fail to reflect the generalization requirements of practical deployments and present optimistic results. In this paper, we study the spatial and temporal generalization of neural positioning with standard-compliant Wi-Fi and 5G NR systems for three real-world CSI datasets acquired in indoor and outdoor environments. We assess generalization with two different architectures, a conventional multilayer perceptron (MLP) and a novel transformer architecture, to unseen spatial regions, unseen UE trajectories, and CSI measurement campaigns separated by one week. Our experiments show that both architectures generalize well in space and time, and the proposed transformer consistently outperforms the MLP in positioning accuracy while requiring fewer model parameters.

eess.SP↗

arXiv2026

Channel Charting for Position and Orientation

Channel charting (CC) in real-world coordinates is a recently proposed self-supervised machine learning method that maps high-dimensional channel state information (CSI) to user equipment (UE) position. In this paper, we extend CC to also estimate UE orientation, which can further assist tasks such as beamfinding, precoding, and beam- and cell-assignment. To this end, we propose a novel orientation triplet loss that accounts for angle periodicity and an alignment loss that embeds estimated orientations in real-world coordinates in a self-supervised fashion. Using real-world CSI measurements from a standard-compliant 5G NR system, we demonstrate that the proposed method achieves position and orientation estimation accuracy close to that of supervised approaches trained with ground-truth labels.

eess.SP↗

arXiv2026

The Influence of Gain and Phase Mismatches on Beam Patterns in Phased Arrays

Practical implementations of phased arrays suffer from per-antenna gain, phase, and delay mismatches, which can significantly worsen the maximum sidelobe level (SLL) of beampatterns. The existing literature either analyzes specific structured mismatch patterns or derives per-angle marginal statistics under random mismatches, which fail to characterize global beampattern metrics such as the maximum SLL. To address this limitation, we propose a frequency-domain framework in which the beampattern is described by a tapering-window-dependent base function evaluated along a deformation determined by the array architecture and signal bandwidth. This formulation enables a spectral analysis of mismatches, revealing that element-wise errors generate weighted replicas of the ideal beampattern whose amplitudes are given by the discrete Fourier transform of the mismatch sequence. Building on this insight, we derive an approximation of the maximum SLL distribution under random gain and phase mismatches. The resulting expressions enable yield-oriented design and rapid design-space exploration without relying on computationally intensive Monte-Carlo simulations.

eess.SP↗

arXiv2026

Mitigating SAR-ADC Non-Idealities in Massive MU-MIMO Systems via Affine Models

Low-resolution data converters can significantly reduce the power consumption and silicon area of all-digital massive multi-user (MU) multiple-input multiple-output (MIMO) basestations. However, the existing literature almost exclusively focuses on idealistic quantization models, neglecting the inherent non-idealities present in real-world analog-to-digital converter (ADC) implementations. To overcome this limitation, we propose two affine models, one based on Bussgang's decomposition and one that maximizes the signal-to-distortion ratio (SDR), both accounting for the most prominent non-idealities in successive approximation register (SAR) ADCs. Subsequently, we utilize these models to devise low-complexity methods that mitigate SAR-ADC non-idealities in massive MU-MIMO wireless systems.

eess.SP↗

arXiv2026

Spectral Impact of Mismatches in Interleaved ADCs

Interleaved ADCs are critical for applications requiring multi-gigasample per second (GS/s) rates, but their performance is often limited by offset, gain, and timing skew mismatches across the sub-ADCs. We propose exact but compact expressions that describe the impact of each of those non-idealities on the output spectrum. We derive the distribution of the power of the induced spurs and replicas, critical for yield-oriented derivation of sub-ADC specifications. Finally, we provide a practical example in which calibration step sizes are derived under the constraint of a target production yield.

eess.SP↗

arXiv2026

GazeVLM: Active Vision via Internal Attention Control for Multimodal Reasoning

Human visual reasoning is governed by active vision, a process where metacognitive control drives top-down goal-directed attention, dynamically routing foveal focus toward task-relevant details while maintaining peripheral awareness of the global scene. In contrast, modern Vision-Language Models (VLMs) process visual information passively, relying on the static accumulation of massive token contexts that dilute spatial reasoning and induce linguistic hallucinations. Here we propose the following paradigm shift: GazeVLM, a multimodal architecture that internalizes this metacognitive oversight over its deployment of attention resources directly into the reasoning loop. By empowering the VLM to autonomously generate gaze tokens ($\texttt{ }$), GazeVLM establishes a top-down control mechanism over its own causal attention mask. The model dynamically dictates its focal intent, triggering a continuous suppression bias that dampens irrelevant visual features, implementing spatial selective attention and simulating foveal fixation. Once local reasoning concludes, the bias lifts, seamlessly restoring the global view. This architecture enables the model to fluidly transition between global spatial awareness and localized focal reasoning without relying on external agentic contraptions like cropping tools, or inflating the context window with additional visual tokens derived from localized visual patches. Trained with a bespoke Group Relative Policy Optimization (GRPO) procedure that rewards valid grounding, our 4B-parameter GazeVLM delivers strong high-resolution multimodal reasoning performance, surpassing state-of-the-art VLMs in its parameter class by nearly 4% and agentic multimodal pipelines built around thinking with images by more than 5% on HRBench-4k and HRBench-8k.

cs.CV↗