arXiv subjects

Christoph Studer

Publications and source records attributed to Christoph Studer.

At least 55 records · Page 3Linked to original sources

arXiv2025

Improving Chip Design Enablement for Universities in Europe -- A Position Paper

The semiconductor industry is pivotal to Europe's economy, especially within the industrial and automotive sectors. However, Europe faces a significant shortfall in chip design capabilities, marked by a severe skilled labor shortage and lagging contributions in the design value chain segment. This paper explores the role of European universities and academic initiatives in enhancing chip design education and research to address these deficits. We provide a comprehensive overview of current European chip design initiatives, analyze major challenges in recruitment, productivity, technology access, and design enablement, and identify strategic opportunities to strengthen chip design capabilities within academic institutions. Our analysis leads to a series of recommendations that highlight the need for coordinated efforts and strategic investments to overcome these challenges.

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arXiv2025

GNSS Jammer and Spoofer Mitigation via Multi-Antenna Processing

Modern positioning relies on radio signals from global navigation satellite systems (GNSS). Their low receive power renders these radio signals susceptible to jamming attacks, in which malicious transmitters emit strong interference to disrupt signal acquisition. Moreover, GNSS are vulnerable to spoofing attacks, in which malicious transmitters mimic legitimate satellites by transmitting spurious GNSS signals. We propose SCHIEBER, a novel method for multi-antenna GNSS receivers that mitigates jammers as well as spoofers without requiring any prior knowledge of the receiver position or attack type: Jammers are mitigated during signal acquisition using a recently developed adaptive spatial filtering technique. Spoofers are identified and rejected after signal acquisition using a novel approach that tests the consistency of acquired signals by comparing their respective direction of arrival (DoA) and pseudorange estimates in a test that is invariant with respect to the unknown receiver position. We demonstrate the efficacy of our method using extensive simulations of a GPS L1 C/A system under spoofing and jamming attacks.

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arXiv2025

CSI2Vec: Towards a Universal CSI Feature Representation for Positioning and Channel Charting

Natural language processing techniques, such as Word2Vec, have demonstrated exceptional capabilities in capturing semantic and syntactic relationships of text through vector embeddings. Inspired by this technique, we propose CSI2Vec, a self-supervised framework for generating universal and robust channel state information (CSI) representations tailored to CSI-based positioning (POS) and channel charting (CC). CSI2Vec learns compact vector embeddings across various wireless scenarios, capturing spatial relationships between user equipment positions without relying on CSI reconstruction or ground-truth position information. We implement CSI2Vec as a neural network that is trained across various deployment setups (i.e., the spatial arrangement of radio equipment and scatterers) and radio setups (RSs) (i.e., the specific hardware used), ensuring robustness to aspects such as differences in the environment, the number of used antennas, or allocated set of subcarriers. CSI2Vec abstracts the RS by generating compact vector embeddings that capture essential spatial information, avoiding the need for full CSI transmission or reconstruction while also reducing complexity and improving processing efficiency of downstream tasks. Simulations with ray-tracing and real-world CSI datasets demonstrate CSI2Vec's effectiveness in maintaining excellent POS and CC performance while reducing computational demands and storage.

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arXiv2025

Channel Charting in Real-World Coordinates with Distributed MIMO

Channel charting is an emerging self-supervised method that maps channel-state information (CSI) to a low-dimensional latent space (the channel chart) that represents pseudo-positions of user equipments (UEs). While channel charts preserve local geometry, i.e., nearby UEs are nearby in the channel chart (and vice versa), the pseudo-positions are in arbitrary coordinates and global geometry is typically not preserved. In order to embed channel charts in real-world coordinates, we first propose a bilateration loss for distributed multiple-input multiple-output (D-MIMO) wireless systems in which only the access point (AP) positions are known. The idea behind this loss is to compare the received power at pairs of APs to determine whether a UE should be placed closer to one AP or the other in the channel chart. We then propose a line-of-sight (LoS) bounding-box loss that places the UE in a predefined LoS area of each AP that is estimated to have a LoS path to the UE. We demonstrate the efficacy of combining both of these loss functions with neural-network-based channel charting using ray-tracing-based and measurement-based channel vectors. Our proposed approach outperforms several baselines and maintains the self-supervised nature of channel charting as it neither relies on geometrical propagation models nor on any ground-truth UE position information.

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arXiv2025

Cauchy-Schwarz Regularizers

We introduce a novel class of regularization functions, called Cauchy-Schwarz (CS) regularizers, which can be designed to induce a wide range of properties in solution vectors of optimization problems. To demonstrate the versatility of CS regularizers, we derive regularization functions that promote discrete-valued vectors, eigenvectors of a given matrix, and orthogonal matrices. The resulting CS regularizers are simple, differentiable, and can be free of spurious stationary points, making them suitable for gradient-based solvers and large-scale optimization problems. In addition, CS regularizers automatically adapt to the appropriate scale, which is, for example, beneficial when discretizing the weights of neural networks. To demonstrate the efficacy of CS regularizers, we provide results for solving underdetermined systems of linear equations and weight quantization in neural networks. Furthermore, we discuss specializations, variations, and generalizations, which lead to an even broader class of new and possibly more powerful regularizers.

math.OC↗

arXiv2025

Deep-Unfolded Massive Grant-Free Transmission in Cell-Free Wireless Communication Systems

Grant-free transmission and cell-free communication are vital in improving coverage and quality-of-service for massive machine-type communication. This paper proposes a novel framework of joint active user detection, channel estimation, and data detection (JACD) for massive grant-free transmission in cell-free wireless communication systems. We formulate JACD as an optimization problem and solve it approximately using forward-backward splitting. To deal with the discrete symbol constraint, we relax the discrete constellation to its convex hull and propose two approaches that promote solutions from the constellation set. To reduce complexity, we replace costly computations with approximate shrinkage operations and approximate posterior mean estimator computations. To improve active user detection (AUD) performance, we introduce a soft-output AUD module that considers both the data estimates and channel conditions. To jointly optimize all algorithm hyper-parameters and to improve JACD performance, we further deploy deep unfolding together with a momentum strategy, resulting in two algorithms called DU-ABC and DU-POEM. Finally, we demonstrate the efficacy of the proposed JACD algorithms via extensive system simulations.

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arXiv2025

Fixed-Throughput GRAND with FIFO Scheduling

Guessing random additive noise decoding (GRAND) is a code-agnostic decoding method that iteratively guesses the noise pattern affecting the received codeword. The number of noise sequences to test depends on the noise realization. Thus, GRAND exhibits random runtime which results in nondeterministic throughput. However, real-time systems must process the incoming data at a fixed rate, necessitating a fixed-throughput decoder in order to avoid losing data. We propose a first-in first-out (FIFO) scheduling architecture that enables a fixed throughput while improving the block error rate (BLER) compared to the common approach of imposing a maximum runtime constraint per received codeword. Moreover, we demonstrate that the average throughput metric of GRAND-based hardware implementations typically provided in the literature can be misleading as one needs to operate at approximately one order of magnitude lower throughput to achieve the BLER of an unconstrained decoder.

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arXiv2025

Jammer-Resilient Time Synchronization in the MIMO Uplink

Spatial filtering based on multiple-input multiple-output (MIMO) processing is a promising approach to jammer mitigation. Effective MIMO data detectors that mitigate smart jammers have recently been proposed, but they all assume perfect time synchronization between transmitter(s) and receiver. However, to the best of our knowledge, there are no methods for resilient time synchronization in the presence of smart jammers. To remedy this situation, we propose JASS, the first method that enables reliable time synchronization for the single-user MIMO uplink while mitigating smart jamming attacks. JASS detects a randomized synchronization sequence based on a novel optimization problem that fits a spatial filter to the time-windowed receive signal in order to mitigate the jammer. We underscore the efficacy of the proposed optimization problem by proving that it ensures successful time synchronization under certain intuitive conditions. We then derive an efficient algorithm for approximately solving our optimization problem. Finally, we use simulations to demonstrate the effectiveness of JASS against a wide range of different jammer types.

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arXiv2025

A Deep-Unfolding-Optimized Coordinate-Descent Data-Detector ASIC for mmWave Massive MIMO

We present a 22 nm FD-SOI (fully depleted silicon-on-insulator) application-specific integrated circuit (ASIC) implementation of a novel soft-output Gram-domain block coordinate descent (GBCD) data detector for massive multi-user (MU) multiple-input multiple-output (MIMO) systems. The ASIC simultaneously addresses the high throughput requirements for millimeter wave (mmWave) communication, stringent area and power budget per subcarrier in an orthogonal frequency-division multiplexing (OFDM) system, and error-rate performance challenges posed by realistic mmWave channels. The proposed GBCD algorithm utilizes a posterior mean estimate (PME) denoiser and is optimized using deep unfolding, which results in superior error-rate performance even in scenarios with highly correlated channels or where the number of user equipment (UE) data streams is comparable to the number of basestation (BS) antennas. The fabricated GBCD ASIC supports up to 16 UEs transmitting QPSK to 256-QAM symbols to a 128-antenna BS, and achieves a peak throughput of 7.1 Gbps at 367 mW. The core area is only 0.97 mm$^2$ thanks to a reconfigurable array of processing elements that enables extensive resource sharing. Measurement results demonstrate that the proposed GBCD data-detector ASIC achieves best-in-class throughput and area efficiency.

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arXiv2024

A Software-Defined and Distributed Wi-Fi Channel-State Information Acquisition Testbed

We propose a software-defined testbed for Wi-Fi channel-state information (CSI) acquisition. This testbed features distributed software-defined radios (SDRs) and a custom IEEE 802.11a software stack that enables the passive collection of CSI data from commercial off-the-shelf (COTS) devices that connect to an existing Wi-Fi network. Unlike commodity Wi-Fi sniffers or channel sounders, our software-defined testbed enables a quick exploration of advanced CSI estimation algorithms in real-world scenarios from naturally-generated Wi-Fi traffic. We explore the effectiveness of two advanced algorithms that denoise CSI estimates, and we demonstrate that CSI-based positioning of COTS Wi-Fi devices with a multilayer perceptron is feasible in an indoor office/lab space in which people are moving.

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arXiv2024

Optimizing Puncturing Patterns of 5G NR LDPC Codes for Few-Iteration Decoding

Rate-matching of low-density parity-check (LDPC) codes enables a single code description to support a wide range of code lengths and rates. In 5G NR, rate matching is accomplished by extending (lifting) a base code to a desired target length and by puncturing (not transmitting) certain code bits. LDPC codes and rate matching are typically designed for the asymptotic performance limit with an ideal decoder. Practical LDPC decoders, however, carry out tens or fewer message-passing decoding iterations to achieve the target throughput and latency of modern wireless systems. We show that one can optimize LDPC code puncturing patterns for such few-iteration-constrained decoders using a method we call swapping of punctured and transmitted blocks (SPAT). Our simulation results show that SPAT yields from 0.20 dB up to 0.55 dB improved signal-to-noise ratio performance compared to the standard 5G NR LDPC code puncturing pattern for a wide range of code lengths and rates.

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arXiv2024

Low-Coherence Sequence Design Under PAPR Constraints

Low-coherence sequences with low peak-to-average power ratio (PAPR) are crucial for multi-carrier wireless communication systems and are used for pilots, spreading sequences, and so on. This letter proposes an efficient low-coherence sequence design algorithm (LOCEDA) that can generate any number of sequences of any length that satisfy user-defined PAPR constraints while supporting flexible subcarrier assignments in orthogonal frequency-division multiple access (OFDMA) systems. We first visualize the low-coherence sequence design problem under PAPR constraints as resolving collisions between hyperspheres. By iteratively adjusting the radii and positions of these hyperspheres, we effectively generate low-coherence sequences that strictly satisfy the imposed PAPR constraints. Simulation results (i) confirm that LOCEDA outperforms existing methods, (ii) demonstrate its flexibility, and (iii) highlight its potential for various applications.

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arXiv2024

A 1.2 mm$^2$ 416 mW 1.44 Mmat/s 64$\times$16 Matrix Preprocessing ASIC for Massive MIMO in 22FDX

Massive multiuser (MU) multiple-input multiple-output (MIMO) enables concurrent transmission of multiple users to a multi-antenna basestation (BS). To detect the users' data using linear equalization, the BS must perform preprocessing, which requires, among other tasks, the inversion of a matrix whose dimension equals the number of user data streams. Explicit inversion of large matrices is notoriously difficult to implement due to high complexity, stringent data dependencies that lead to high latency, and high numerical precision requirements. We propose a novel preprocessing architecture based on the block-LDL matrix factorization, which improves parallelism and, hence, reduces latency. We demonstrate the effectiveness of our architecture through (i) massive MU-MIMO system simulations with mmWave channel vectors and (ii) measurements of a 22FDX ASIC, which is, to our knowledge, the first fabricated preprocessing engine for massive MU-MIMO with 64 BS antennas and 16 single-antenna users. Our ASIC reaches a clock frequency of 870 MHz while consuming 416 mW. At its peak throughput, the ASIC preprocesses 1.44 M 64$\times$16 matrices per second at a latency of only 0.7 $μ$s.

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arXiv2024

Fundamental Limits for Jammer-Resilient Communication in Finite-Resolution MIMO

Spatial filtering based on multiple-input multiple-output (MIMO) processing is a powerful method for jammer mitigation. In principle, a MIMO receiver can null the interference of a single-antenna jammer at the cost of only one degree of freedom - if the number of receive antennas is large, communication performance is barely affected. In this paper, we show that the potential for MIMO jammer mitigation based on the digital outputs of finite-resolution analog-to-digital converters (ADCs) is fundamentally worse: Strong jammers will either cause the ADCs to saturate (when the ADCs' quantization range is small) or drown legitimate communication signals in quantization noise (when the ADCs' quantization range is large). We provide a fundamental bound on the mutual information between the quantized receive signal and the legitimate transmit signal. Our bound shows that, for any fixed ADC resolution, the mutual information tends to zero as the jammer power tends to infinity, regardless of the quantization strategy. Our bound also confirms the intuition that for every 6.02 dB increase in jamming power, the ADC resolution must be increased by 1 bit in order to prevent the mutual information from vanishing.

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arXiv2024

Universal MIMO Jammer Mitigation

Multi-antenna processing enables jammer mitigation through spatial filtering, provided that the receiver knows the spatial signature of the jammer interference. Estimating this signature is easy for barrage jammers that transmit continuously and with static signature, but difficult for more sophisticated jammers. Smart jammers may deliberately suspend transmission when the receiver tries to estimate their spatial signature, or they may use time-varying beamforming to continuously change their spatial signature. To deal with such smart jammers, we propose MASH, the first method that indiscriminately mitigates all types of jammers. Assume that the transmitter and receiver share a common secret. Based on this secret, the transmitter embeds (with a time-domain transform) its signal in a secret subspace of a higher-dimensional space. The receiver applies a reciprocal transform to the receive signal, which (i) raises the legitimate transmit signal from its secret subspace and (ii) provably transforms any jammer into a barrage jammer, making estimation and mitigation via multi-antenna processing straightforward. Focusing on the massive multi-user MIMO uplink, we present three MASH-based data detectors and show their jammer-resilience via extensive simulations. We also introduce strategies for multi-user communication without a global secret as well as methods that use computationally efficient embedding and raising transforms.

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arXiv2024

PyJama: Differentiable Jamming and Anti-Jamming with NVIDIA Sionna

Despite extensive research on jamming attacks on wireless communication systems, the potential of machine learning for amplifying the threat of such attacks, or our ability to mitigate them, remains largely untapped. A key obstacle to such research has been the absence of a suitable framework. To resolve this obstacle, we release PyJama, a fully-differentiable open-source library that adds jamming and anti-jamming functionality to NVIDIA Sionna. We demonstrate the utility of PyJama (i) for realistic MIMO simulations by showing examples that involve forward error correction, OFDM waveforms in time and frequency, realistic channel models, and mobility; and (ii) for learning to jam. Specifically, we use stochastic gradient descent to optimize jamming power allocation over an OFDM resource grid. The learned strategies are non-trivial, intelligible, and effective.

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arXiv2024

A 46 Gbps 12 pJ/b Sparsity-Adaptive Beamspace Equalizer for mmWave Massive MIMO in 22FDX

We present a GlobalFoundries 22FDX FD-SOI application-specific integrated circuit (ASIC) of a beamspace equalizer for millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems. The ASIC implements a recently-proposed power-saving technique called sparsity-adaptive equalization (SPADE). SPADE exploits the inherent sparsity of mmWave channels in the beamspace domain to reduce the dynamic power of matrix-vector products by skipping multiplications for which the magnitude of both operands are below pre-defined thresholds. Simulations with realistic mmWave channels show that SPADE incurs less than 0.7dB SNR degradation at 1% target bit error rate compared to antenna-domain equalization. ASIC measurement results demonstrate an equalization throughput of 46Gbps and show that SPADE offers up to 38% power savings compared to antenna-domain equalization. A comparison with state-of-the-art massive MIMO equalizer designs reveals that our ASIC achieves superior normalized energy efficiency.

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arXiv2024

A Jammer-Mitigating 267 Mb/s 3.78 mm$^2$ 583 mW 32$\times$8 Multi-User MIMO Receiver in 22FDX

We present the first multi-user (MU) multiple-input multiple-output (MIMO) receiver ASIC that mitigates jamming attacks. The ASIC implements a recent nonlinear algorithm that performs joint jammer mitigation (via spatial filtering) and data detection (using a box prior on the data symbols). Our design supports 8 user equipments (UEs) and 32 basestation (BS) antennas, QPSK and 16-QAM with soft-outputs, and enables the mitigation of single-antenna barrage jammers and smart jammers. The fabricated 22 nm FD-SOI ASIC includes preprocessing, has a core area of 3.78 mm$^2$, achieves a throughput of 267 Mb/s while consuming 583 mW, and is the only existing design that enables reliable data detection under jamming attacks.

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