arXiv subjects

Christoph Studer

Publications and source records attributed to Christoph Studer.

At least 37 records · Page 2Linked to original sources

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.

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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.

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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

Enhancing Semantic Segmentation with Continual Self-Supervised Pre-training

Self-supervised learning (SSL) has emerged as a central paradigm for training foundation models by leveraging large-scale unlabeled datasets, often producing representations with strong generalization capabilities. These models are typically pre-trained on general-purpose datasets such as ImageNet and subsequently adapted to various downstream tasks through finetuning. While prior work has investigated parameter-efficient adaptation methods like adapters, LoRA, and prompt tuning, primarily targeting downstream finetuning, extending the SSL pre-training itself in a continual manner to new domains under limited data remains largely underexplored, especially for downstream dense prediction tasks like semantic segmentation. In this work, we address the challenge of adapting vision foundation models to low-data target domains through continual self-supervised pre-training, specifically targeting downstream semantic segmentation. We propose GLARE (Global Local and Regional Enforcement), a novel continual self-supervised pre-training task designed to enhance downstream semantic segmentation performance. GLARE introduces patch-level augmentations to encourage local consistency and incorporates a regional consistency constraint that leverages spatial semantics in the data. For efficient continual pre-training, we initialize Vision Transformers (ViTs) with weights from existing SSL models and update only lightweight adapter modules specifically UniAdapter - while keeping the rest of the backbone frozen. Experiments across multiple semantic segmentation benchmarks on different domains demonstrate that GLARE consistently improves downstream performance with minimal computational and parameter overhead.

cs.CV↗

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↗

arXiv2026

Learning Sparsifying Transforms for mmWave Communication via $\ell^4$-Norm Maximization

The high directionality of wave propagation at millimeter-wave (mmWave) carrier frequencies results in only a small number of significant transmission paths between user equipments and the basestation (BS). This sparse nature of wave propagation is revealed in the beamspace domain, which is traditionally obtained by taking the spatial discrete Fourier transform (DFT) across a uniform linear antenna array at the BS, where each DFT output is associated with a distinct beam. In recent years, beamspace processing has emerged as a promising technique to reduce baseband complexity and power consumption in all-digital massive multiuser (MU) multiple-input multiple-output (MIMO) systems operating at mmWave frequencies. However, it remains unclear whether the DFT is the optimal sparsifying transform for finite-dimensional antenna arrays. In this paper, we extend the framework of Zhai et al. for complete dictionary learning via $\ell^4$-norm maximization to the complex case in order to learn new sparsifying transforms. We provide a theoretical foundation for $\ell^4$-norm maximization and propose two suitable learning algorithms. We then utilize these algorithms (i) to assess the optimality of the DFT for sparsifying channel vectors theoretically and via simulations and (ii) to learn improved sparsifying transforms for real-world and synthetically generated channel vectors.

cs.IT↗

arXiv2025

A 14ns-Latency 9Gb/s 0.44mm$^2$ 62pJ/b Short-Blocklength LDPC Decoder ASIC in 22FDX

Ultra-reliable low latency communication (URLLC) is a key part of 5G wireless systems. Achieving low latency necessitates codes with short blocklengths for which polar codes with successive cancellation list (SCL) decoding typically outperform message-passing (MP)-based decoding of low-density parity-check (LDPC) codes. However, SCL decoders are known to exhibit high latency and poor area efficiency. In this paper, we propose a new short-blocklength multi-rate binary LDPC code that outperforms the 5G-LDPC code for the same blocklength and is suitable for URLLC applications using fully parallel MP. To demonstrate our code's efficacy, we present a 0.44mm$^2$ GlobalFoundries 22FDX LDPC decoder ASIC which supports three rates and achieves the lowest-in-class decoding latency of 14ns while reaching an information throughput of 9Gb/s at 62pJ/b energy efficiency for a rate-1/2 code with 128-bit blocklength.

cs.AR↗

arXiv2025

Efficient and Physically-Consistent Modeling of Reconfigurable Electromagnetic Structures

Reconfigurable electromagnetic structures (REMSs), such as reconfigurable reflectarrays (RRAs) or reconfigurable intelligent surfaces (RISs), hold significant potential to improve the spectral efficiency of wireless communication systems and the accuracy of wireless sensing systems. Even though several REMS modeling approaches have been proposed in recent years, the literature lacks models that are both computationally efficient and physically consistent. As a result, algorithms that control the reconfigurable elements of REMSs (e.g., the phase shifts of a RIS) are often built on simplistic and thus inaccurate models. To enable physically accurate REMS-parameter tuning, we present a new framework for efficient and physically consistent modeling of general REMSs. Our modeling method combines a circuit-theoretic approach with a new formalism that describes a REMS's interaction with the electromagnetic (EM) waves in its far-field region. Our modeling method enables efficient computation of the entire far-field radiation pattern for arbitrary configurations of the REMS reconfigurable elements once a single full-wave EM simulation of the non-reconfigurable parts of the REMS has been performed. The predictions made by our framework align with the physical laws of classical electrodynamics and model effects caused by inter-antenna coupling, non-reciprocal materials, polarization, ohmic losses, matching losses, influence of metallic housings, noise from low-noise amplifiers, and noise arising in or received by antennas. In order to validate the efficiency and accuracy of our modeling approach, we (i) compare our modeling method to EM simulations and (ii) conduct a case study involving an RRA that enables simultaneous multiuser beam- and null-forming using a new, computationally efficient, and physically accurate parameter tuning algorithm.

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arXiv2025

CSI-Based User Positioning, Channel Charting, and Device Classification with an NVIDIA 5G Testbed

Channel-state information (CSI)-based sensing will play a key role in future cellular systems. However, no CSI dataset has been published from a real-world 5G NR system that facilitates the development and validation of suitable sensing algorithms. To close this gap, we publish three real-world wideband multi-antenna multi-open RAN radio unit (O-RU) CSI datasets from the 5G NR uplink channel: an indoor lab/office room dataset, an outdoor campus courtyard dataset, and a device classification dataset with six commercial-off-the-shelf (COTS) user equipments (UEs). These datasets have been recorded using a software-defined 5G NR testbed based on NVIDIA Aerial RAN CoLab Over-the-Air (ARC-OTA) with COTS hardware, which we have deployed at ETH Zurich. We demonstrate the utility of these datasets for three CSI-based sensing tasks: neural UE positioning, channel charting in real-world coordinates, and closed-set device classification. For all these tasks, our results show high accuracy: neural UE positioning achieves 0.6cm (indoor) and 5.7cm (outdoor) mean absolute error, channel charting in real-world coordinates achieves 73cm mean absolute error (outdoor), and device classification achieves 99% (same day) and 95% (next day) accuracy. The CSI datasets, ground-truth UE position labels, CSI features, and simulation code are publicly available at https://caez.ethz.ch

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arXiv2025

Joint Beamforming and Matching for Ultra-Dense Massive Antenna Arrays

Massive multiple-input multiple-output (MIMO) offers substantial spectral-efficiency gains, but scaling to very large antenna arrays with conventional all-digital and hybrid beamforming architectures quickly results in excessively high costs and power consumption. Low-cost, switch-based architectures have recently emerged as a potential alternative. However, prior studies rely on simplified models that ignore (among others) antenna coupling, radiation patterns, and matching losses, resulting in inaccurate performance predictions. In this paper, we use a physically consistent electromagnetic modeling framework to analyze an ultra-dense patch-antenna array architecture that performs joint beamforming and matching using networks of inexpensive RF switches. Our results demonstrate that simple, switch-based beamforming architectures can approach the antenna-gain of all-digital solutions at significantly lower cost and complexity.

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arXiv2025

Variable Point: A Number Format for Area- and Energy-Efficient Multiplication of High-Dynamic-Range Numbers

Fixed-point number representation is commonly employed in digital VLSI designs that have stringent hardware efficiency constraints. However, fixed-point numbers cover a relatively small dynamic range for a given bitwidth. In contrast, floating-point numbers offer a larger dynamic range at the cost of increased hardware complexity. In this paper, we propose a novel number format called variable-point (VP). VP numbers cover a larger dynamic range than fixed-point numbers with similar bitwidth, without notably increasing hardware complexity -- this allows for a more efficient representation of signals with high dynamic range. To demonstrate the efficacy of the proposed VP number format, we consider a matrix-vector multiplication engine for spatial equalization in multi-antenna wireless communication systems involving high-dynamic-range signals. Through post-layout VLSI implementation results, we demonstrate that the proposed VP-based design achieves 20% and 10% area and power savings, respectively, compared to a fully optimized fixed-point design, without incurring any noticeable performance degradation.

cs.AR↗

arXiv2025

Interference and Multipath Resilient ToA Estimation

We present a computationally-efficient algorithm for time-of-arrival (ToA) estimation that is robust under multipath propagation and strong interference. Our algorithm leverages multiple receive antennas to combine adaptive spatial filtering with autodifferentiation in order to super-resolve the tap of the first-arriving path at low computational complexity and without requiring model-order estimation. We use simulations with ray-traced indoor propagation channels to demonstrate significant performance improvements over conventional correlation-based ToA estimation methods and subspace techniques such as JADE.

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arXiv2025

A Jammer-Resilient 2.87 mm$^2$ 1.28 MS/s 310 mW Multi-Antenna Synchronization ASIC in 65 nm

We present the first ASIC implementation of jammer-resilient multi-antenna time synchronization. The ASIC implements a recent algorithm that mitigates jamming attacks on synchronization signals using multi-antenna processing. Our design supports synchronization between a single-antenna transmitter and a 16-antenna receiver while mitigating smart jammers with up to two transmit antennas. The fabricated 65 nm ASIC has a core area of 2.87 mm$^2$, consumes a power of 310 mW, and supports a sampling rate of 1.28 mega-samples per second (MS/s).

cs.AR↗

arXiv2025

A 0.32 mm$^2$ 100 Mb/s 223 mW ASIC in 22FDX for Joint Jammer Mitigation, Channel Estimation, and SIMO Data Detection

We present the first single-input multiple-output (SIMO) receiver ASIC that jointly performs jammer mitigation, channel estimation, and data detection. The ASIC implements a recent algorithm called siMultaneous mitigAtion, Estimation, and Detection (MAED). MAED mitigates smart jammers via spatial filtering using a nonlinear optimization problem that unifies jammer estimation and nulling, channel estimation, and data detection to achieve state-of-the-art error-rate performance under jamming. The design supports eight receive antennas and enables mitigation of smart jammers as well as of barrage jammers. The ASIC is fabricated in 22 nm FD-SOI, has a core area of 0.32 mm$^2$, and achieves a throughput of 100 Mb/s at 223 mW, thus delivering 3$\times$ higher per-user throughput and 4.5$\times$ higher area efficiency than the state-of-the-art jammer-resilient detector.

cs.AR↗

arXiv2025

Neural Positioning Without External Reference

Channel state information (CSI)-based user equipment (UE) positioning with neural networks -- referred to as neural positioning -- is a promising approach for accurate off-device UE localization. Most existing methods train their neural networks with ground-truth position labels obtained from external reference positioning systems, which requires costly hardware and renders label acquisition difficult in large areas. In this work, we propose a novel neural positioning pipeline that avoids the need for any external reference positioning system. Our approach trains the positioning network only using CSI acquired off-device and relative displacement commands executed on commercial off-the-shelf (COTS) robot platforms, such as robotic vacuum cleaners -- such an approach enables inexpensive training of accurate neural positioning functions over large areas. We evaluate our method in three real-world scenarios, ranging from small line-of-sight (LoS) areas to larger non-line-of-sight (NLoS) environments, using CSI measurements acquired in IEEE 802.11 Wi-Fi and 5G New Radio (NR) systems. Our experiments demonstrate that the proposed neural positioning pipeline achieves UE localization accuracies close to state-of-the-art methods that require externally acquired high-precision ground-truth position labels for training.

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arXiv2025

Beamspace Equalization for mmWave Massive MIMO: Algorithms and VLSI Implementations

Massive multiuser multiple-input multiple-output (MIMO) and millimeter-wave (mmWave) communication are key physical layer technologies in future wireless systems. Their deployment, however, is expected to incur excessive baseband processing hardware cost and power consumption. Beamspace processing leverages the channel sparsity at mmWave frequencies to reduce baseband processing complexity. In this paper, we review existing beamspace data detection algorithms and propose new algorithms as well as corresponding VLSI architectures that reduce data detection power. We present VLSI implementation results for the proposed architectures in a 22nm FDSOI process. Our results demonstrate that a fully-parallelized implementation of the proposed complex sparsity-adaptive equalizer (CSPADE) achieves up to 54% power savings compared to antenna-domain equalization. Furthermore, our fully-parallelized designs achieve the highest reported throughput among existing massive MIMO data detectors, while achieving better energy and area efficiency. We also present a sequential multiply-accumulate (MAC)-based architecture for CSPADE, which enables even higher power savings, i.e., up to 66%, compared to a MAC-based antenna-domain equalizer.

cs.AR↗

arXiv2025

Joint Jammer Mitigation and Data Detection

Multi-antenna (or MIMO) processing is a promising solution to the problem of jammer mitigation. Existing methods mitigate the jammer based on an estimate of its spatial signature that is acquired through a dedicated training phase. This strategy has two main drawbacks: (i) it reduces the communication rate since no data can be transmitted during the training phase and (ii) it can be evaded by smart or multi-antenna jammers that do not transmit during the training phase or that dynamically change their subspace through time-varying beamforming. To address these drawbacks, we propose Joint jammer Mitigation and data Detection (JMD), a novel paradigm for MIMO jammer mitigation. The core idea of JMD is to estimate and remove the jammer interference subspace jointly with detecting the legitimate transmit data over multiple time slots. Doing so removes the need for a dedicated and rate-reducing training period while being able to mitigate smart and dynamic multi-antenna jammers. We provide two JMD-type algorithms, SANDMAN and MAED, that differ in the way they estimate the channels of the legitimate transmitters and achieve different complexity-performance tradeoffs. Extensive simulations demonstrate the efficacy of JMD for jammer mitigation.

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arXiv2025

Neural Integrated Sensing and Communication for the MIMO-OFDM Downlink

The ongoing convergence of spectrum and hardware requirements for wireless sensing and communication applications has fueled the integrated sensing and communication (ISAC) paradigm in next-generation networks. Neural-network-based ISAC leverages data-driven learning techniques to add sensing capabilities to existing communication infrastructure. This paper presents a novel signal-processing framework for such neural ISAC systems based on the multiple-input multiple-output (MIMO) and orthogonal frequency-division multiplexing (OFDM) downlink. Our approach enables generalized sensing functionality without modifying the MIMO-OFDM communication link. Specifically, our neural ISAC pipeline measures the backscattered communication signals to generate discrete map representations of spatial occupancy, formulated as multiclass or multilabel classification problems, which can then be utilized by specialized downstream tasks. To improve sensing performance in closed or cluttered environments, our neural ISAC pipeline relies on features specifically designed to mitigate strong reflective paths. Extensive simulations using ray-tracing models demonstrate that our neural ISAC framework reliably reconstructs scene maps without altering the MIMO-OFDM communication pipeline or reducing data rates.

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