Christoph Studer

Last Name

Studer

First Name

Christoph

ORCID

0000-0001-8950-6267

Organisational unit

09695 - Studer, Christoph / Studer, Christoph

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Publications1 - 10 of 307

Channel Charting in Real-World Coordinates With Distributed MIMO
Taner, Sueda; Palhares, Victoria; Studer, Christoph (2025)
IEEE Transactions on Wireless Communications
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.
PhasePack: A Phase Retrieval Library
Chandra, Rohan; Goldstein, Tom; Studer, Christoph (2019)
2019 13th International conference on Sampling Theory and Applications (SampTA)
Phase retrieval deals with the estimation of complex-valued signals solely from the magnitudes of linear measurements. While there has been a recent explosion in the development of phase retrieval algorithms, the lack of a common interface has made it difficult to compare new methods against the state-of-the-art. The purpose of PhasePack is to create a common software interface for a wide range of phase retrieval algorithms and to provide a common testbed using both synthetic data and empirical imaging datasets. PhasePack is able to benchmark a large number of recent phase retrieval methods against one another to generate comparisons using a range of different performance metrics. The software package handles single method testing as well as multiple method comparisons.The algorithm implementations in PhasePack differ slightly from their original descriptions in the literature in order to achieve faster speed and improved robustness. In particular, PhasePack uses adaptive stepsizes, line-search methods, and fast eigensolvers to speed up and automate convergence.
Time-varying Learning and Content Analytics via Sparse Factor Analysis
Lan, Andrew S.; Studer, Christoph; Baraniuk, Richard G. (2014)
Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining
We propose SPARFA-Trace, a new machine learning-based framework for time-varying learning and content analytics for educational applications. We develop a novel message passing-based, blind, approximate Kalman filter for sparse factor analysis (SPARFA) that jointly traces learner concept knowledge over time, analyzes learner concept knowledge state transitions (induced by interacting with learning resources, such as textbook sections, lecture videos, etc., or the forgetting effect), and estimates the content organization and difficulty of the questions in assessments. These quantities are estimated solely from binary-valued (correct/incorrect) graded learner response data and the specific actions each learner performs (e.g., answering a question or studying a learning resource) at each time instant. Experimental results on two online course datasets demonstrate that SPARFA-Trace is capable of tracing each learner's concept knowledge evolution over time, analyzing the quality and content organization of learning resources, and estimating the question--concept associations and the question difficulties. Moreover, we show that SPARFA-Trace achieves comparable or better performance in predicting unobserved learner responses compared to existing collaborative filtering and knowledge tracing methods.
GPU Acceleration of a Configurable N-Way MIMO Detector for Wireless Systems
Wu, Michael; Yin, Bei; Wang, Guohui; et al. (2014)
Journal of Signal Processing Systems
VLSI Design of a 3-bit Constant-Modulus Precoder for Massive MU-MIMO
Castañeda Fernández, Oscar; Jacobsson, Sven; Durisi, Giuseppe; et al. (2018)
IEEE International Symposium on Circuits and Systems (ISCAS). Proceedings, 27–30 May 2018, Florence, Italy
Fifth-generation (5G) cellular systems will build on massive multi-user (MU) multiple-input multiple-output (MIMO) technology to attain high spectral efficiency. However, having hundreds of antennas and radio-frequency (RF) chains at the base station (BS) entails prohibitively high hardware costs and power consumption. This paper proposes a novel nonlinear precoding algorithm for the massive MU-MIMO downlink in which each RF chain contains an 8-phase (3-bit) constant-modulus transmitter, enabling the use of low-cost and power-efficient analog hardware. We present a high-throughput VLSI architecture and show implementation results on a Xilinx Virtex-7 FPGA. Compared to a recently-reported nonlinear precoder for BS designs that use two 1-bit digital-to-analog converters per RF chain, our design enables up to 3.75 dB transmit power reduction at no more than a 2.7x increase in FPGA resources.
Blind SNR Estimation and Nonparametric Channel Denoising in Multi-Antenna mmWave Systems
Gallyas-Sanhueza, Alexandra; Studer, Christoph (2021)
ICC 2021 - IEEE International Conference on Communications
We propose blind estimators for the average noise power, receive signal power, signal-to-noise ratio (SNR), and mean-square error (MSE), suitable for multi-antenna millimeter wave (mmWave) wireless systems. The proposed estimators can be computed at low complexity and solely rely on beamspace sparsity, i.e., the fact that only a small number of dominant propagation paths exist in typical mmWave channels. Our estimators can be used (i) to quickly track some of the key quantities in multi-antenna mmWave systems while avoiding additional pilot overhead and (ii) to design efficient nonparametric algorithms that require such quantities. We provide a theoretical analysis of the proposed estimators, and we demonstrate their efficacy via synthetic experiments and using a nonparametric channel-vector denoising task with realistic multi-antenna mmWave channels.
Beamspace Channel Estimation for Massive MIMO mmWave Systems: Algorithm and VLSI Design
Mirfarshbafan, Seyedhadi; Gallyas-Sanhueza, Alexandra; Ghods, Ramina; et al. (2020)
IEEE Transactions on Circuits and Systems I: Regular Papers
A 283 pJ/b 240 Mb/s Floating-Point Baseband Accelerator for Massive MU-MIMO in 22FDX
Castañeda Fernández, Oscar; Benini, Luca; Studer, Christoph (2022)
ESSCIRC 2022- IEEE 48th European Solid State Circuits Conference (ESSCIRC)
We present PULPO, a floating-point baseband-processing accelerator for massive multi-user multiple-input multiple-output (MU-MIMO) basestations (BSs). PULPO accelerates matrix-vector products, not only with a matrix but also with its Hermitian, as well as affine transforms and nonlinear projections used in iterative algorithms that outclass traditional linear methods in various applications. PULPO is integrated in a system-on-chip (SoC) with a tight integration to the system's data memory, facilitating data exchange and co-operation with 8 RISC-V cores. The fabricated accelerator achieves comparable efficiency as recently-proposed fixed-point baseband processors, while eliminating the burdens associated with fixed-point design, thus simplifying massive MU-MIMO BS development.
SNR Prediction in Cellular Systems based on Channel Charting
Kazemi, Parham; Al-Tous, Hanan; Studer, Christoph; et al. (2020)
2020 IEEE Eighth International Conference on Communications and Networking (ComNet)
On the complexity distribution of sphere decoding
Seethaler, Dominik; Jalden, Joakim; Studer, Christoph; et al. (2011)
IEEE Transactions on Information Theory

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Christoph Studer

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