arXiv subjects

Christoph Studer

Publications and source records attributed to Christoph Studer.

At least 19 recordsLinked to original sources

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 Z\"urich 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

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

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

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↗

arXiv2026

On Near-Far-Field Boundaries in Wireless Systems

Near-field (NF) multi-antenna wireless communication and sensing have attracted growing research interest in recent years. A core question in this area is how to determine whether a wireless system is operating in the NF or far-field (FF) region. In this work, we propose a framework grounded in Maxwell's equations to analyze the transition region between the NF and FF, following the IEEE definition that specifies where the NF ends and the FF begins. Using this framework, we (i) compare a variety of traditional and recently introduced single-letter distance thresholds, often referred to as near-far-field boundaries, and (ii) conduct numerical experiments with both single- and multi-antenna wireless systems and with analytical models as well as full-wave electromagnetic simulations. Our results indicate that all of the considered single-letter distance thresholds are insufficient to predict the transition region between the NF and FF regions. Moreover, we highlight several important caveats associated with frequently (and recently) used NF and FF concepts.

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

Site-Specific Finetuning of Neural Receivers with Real-World 5G NR Measurements

Finetuning wireless receivers to a specific deployment scenario can yield significant error-rate performance improvements without increasing processing complexity. However, site-specific finetuning has so far only been demonstrated on synthetic channel data and lacks real-world benchmarks. In this work, we empirically study site-specific finetuning of neural receivers using real-world 5G NR physical uplink shared channel (PUSCH) data collected with an over-the-air testbed at ETH Zurich across three scenarios: (i) a small laboratory, (ii) a large office floor, and (iii) a high-mobility outdoor environment. Our results confirm substantial error-rate performance improvements from site-specific finetuning, consistent with earlier findings based on synthetic channel data. Moreover, we demonstrate that these improvements generalize across different user-equipment hardware and deployment scenarios.

eess.SP↗

arXiv2026

An Optimization-Based User Scheduling Framework for Multiuser MIMO Systems

Resource allocation is a key factor in multiuser (MU) multiple-input multiple-output (MIMO) wireless systems to provide high quality of service to all user equipments (UEs). In congested scenarios, UE scheduling enables UEs to be distributed over time, frequency, or space in order to mitigate inter-UE interference. Many existing UE scheduling methods rely on greedy algorithms, which fail at treating the resource-allocation problem globally. In this work, we propose a UE scheduling framework for MU-MIMO wireless systems that approximately solves a nonconvex optimization problem that treats scheduling globally. Our UE scheduling framework determines subsets of UEs that should transmit simultaneously in a given resource slot and is flexible in the sense that it (i) supports a variety of objective functions (e.g., post-equalization mean squared error, capacity, and achievable sum rate) and (ii) enables precise control over the minimum and maximum number of resources the UEs should occupy. We demonstrate the efficacy of our UE scheduling framework for millimeter-wave massive MU-MIMO and sub-6-GHz cell-free massive MU-MIMO systems, and we show that it outperforms existing scheduling algorithms while approaching the performance of an exhaustive search.

cs.IT↗

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

On the Complexity of Electromagnetic Far-Field Modeling

Modern wireless systems are envisioned to employ antenna architectures that not only transmit and receive electromagnetic (EM) waves, but also intentionally reflect and possibly transform incident EM waves. In this paper, we propose a mathematically rigorous framework grounded in Maxwell's equations for analyzing the complexity of EM far-field modeling of general antenna architectures. We show that-under physically meaningful assumptions-such antenna architectures exhibit limited complexity, i.e., can be modeled by finite-rank operators using finitely many parameters. Furthermore, we construct a sequence of finite-rank operators whose approximation error decays super-exponentially once the operator rank exceeds an effective bandwidth associated with the antenna architecture and the analysis frequency. These results constitute a fundamental prerequisite for the efficient and accurate modeling of general antenna architectures on digital computing platforms.

cs.IT↗