Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2603.04087

A Digital Twin of the FPGA Digital Signal Processing Chain for MKIDs Readout: Root-Cause Analysis and Mitigation of Spurs

Mounir Abdkrimi·Olivier Rossetto·Olivier Bourrion·Christophe Vescovi·Christophe Hoarau
View original source ↗Read source document ↗Export metadata ↓

Abstract

The KID_READOUT board, developed for the CONCERTO millimeter-wave astronomy instrument, implements FPGA-based digital frequency multiplexing to read out large arrays of Microwave Kinetic Inductance Detectors (MKIDs). The complexity of the implemented multirate DSP chain, which combines tones synthesis, interpolation, digital frequency translation, polyphase filter-bank (PFB) channelization, and digital down-conversion (DDC), makes analytical performance optimization difficult. To address this, we developed a cycle-and bitaccurate, Python-based digital twin of the FPGA readout firmware DSP chain. Using this model, we identified the origin of previously measured and unexplained spurs in the readout channels, tracing them to periodicity mismatches between the excitation and analysis paths and to insufficient suppression of negative-frequency components by the DDC filters. Based on these insights, we implemented a mitigation strategy that aligns the periodicities and improves the DDC filter characteristics, effectively eliminating the spurs with a minor increase in FPGA resource usage.

Explore related subjects

eess.SP

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Mounir Abdkrimi, Olivier Rossetto, Olivier Bourrion, Christophe Vescovi, Christophe Hoarau. 2026-03-04. A Digital Twin of the FPGA Digital Signal Processing Chain for MKIDs Readout: Root-Cause Analysis and Mitigation of Spurs. https://arxiv.org/abs/2603.04087

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

From Continuous sEMG Signals to Discrete Muscle State Tokens: A Robust and Interpretable Representation Framework

Surface electromyography (sEMG) signals exhibit substantial inter-subject variability and are highly susceptible to noise, posing challenges for robust and interpretable decoding. To address these limitations, we propose a discrete representation of sEMG signals based on a physiology-informed tokenization framework. The method employs a sliding window aligned with the minimal muscle contraction cycle to isolate individual muscle activation events. From each window, ten time-frequency features, including root mean square (RMS) and median frequency (MDF), are extracted, and K-means clustering is applied to group segments into representative muscle-state tokens. We also introduce a large-scale benchmark dataset, ActionEMG-43, comprising 43 diverse actions and sEMG recordings from 16 major muscle groups across the body. Based on this dataset, we conduct extensive evaluations to assess the inter-subject consistency, representation capacity, and interpretability of the proposed sEMG tokens. Our results show that the token representation exhibits high inter-subject consistency (Cohen's Kappa = 0.82+-0.09), indicating that the learned tokens capture consistent and subject-independent muscle activation patterns. In action recognition tasks, models using sEMG tokens achieve Top-1 accuracies of 75.5% with ViT and 67.9% with SVM, outperforming raw-signal baselines (72.8% and 64.4%, respectively), despite a 96% reduction in input dimensionality. In movement quality assessment, the tokens intuitively reveal patterns of muscle underactivation and compensatory activation, offering interpretable insights into neuromuscular control. Together, these findings highlight the effectiveness of tokenized sEMG representations as a compact, generalizable, and physiologically meaningful feature space for applications in rehabilitation, human-machine interaction, and motor function analysis.

eess.SP↗
arXiv2026

Adaptive 3D-RoPE: Physics-Aligned Rotary Positional Encoding for Wireless Foundation Models

Wireless foundation models (WFMs) have emerged as a promising paradigm for unified channel state information (CSI) acquisition across diverse tasks in sixth-generation (6G) networks. Although WFMs significantly outperform task-specific small models, their zero-shot cross-scenario generalization still remains limited for real-world applications. Existing positional embeddings, the sole interface through which self-attention perceives the temporal-frequency-antenna 3D physical coordinates of CSI, fail to capture the highly dynamic and axis-dependent coherence inherent in wireless channels. This paper proposes Adaptive 3D-RoPE, a channel-driven 3D rotary positional embedding framework for WFMs to dynamically align the 3D positional embeddings with the instantaneous coherence state of heterogeneous CSI. The design proceeds in three stages: first, an axis-wise learnable rotary prior independently preserves the temporal, frequency, and antenna coordinate structures; second, a feature-guided rotary modulation module maps the feature-wise standard deviation of visible CSI tokens to compact, sample-adaptive scales; third, identical coordinate offsets induce dynamically adjusted query-key interactions tailored to the instantaneous channel state. Extensive experiments on both simulated and measured datasets validate the effectiveness of Adaptive 3D-RoPE across three complementary dimensions. It reduces NMSE by 10.14, 6.25, and 4.61 dB relative to baselines under antenna, temporal, and frequency scaling, respectively. It transfers effectively to real-world measured CSI and remains robust under imperfect CSI. Finally, it transfers to the independently designed LWM backbone and beam-prediction task, improving zero-shot Top-1 accuracy by 8.03 percentage points.

eess.SP↗
arXiv2026

Data driven approach for Outdoor Channel Prediction in 5G and Beyond

An evolution of Wireless Communications towards 5G and beyond provides improved user experience in terms of quality of services. Understanding and estimating Channel information plays crucial role in providing better user experience. Traditional methods of channel estimation involves periodically sending pilots (known signals), estimating channel and send back estimated channel information to the BS which increases computational complexity and communication complexity. Hence, we focus on data driven approach for channel estimation. In this work, we explore a channel estimation mechanism at 7GHz frequency band for a given user location. This work involves data generation using Ray tracing mechanism and Machine learning model training that contains feature variables such as transmitter location, user location and target variable as channel coefficient . We explored Support Vector Regression, K-nearest neighbor (KNN), Random Forest, XGBoost and MLP. We found via simulations that XG Boost and proposed MLP performs better than Support Vector Regression, KNN and Random forest regression.

eess.SP↗