pwc-archive/papers-with-abstracts huggingface dataset:
Generalized Scattering Matrix of Antenna: Moment Solution, Compression Storage and Application
Authors: Chenbo Shi, Jin Pan, Xin Gu, Shichen Liang, Le Zuo
Date: 2024-10-29 00:00:00

This paper presents a computation method of generalized scattering matrix (GSM) based on integral equations and the method of moments (MoM), specifically designed for antennas excited through waveguide ports. By leveraging two distinct formulations -- magnetic-type and electric-type integral equations -- we establish concise algebraic relations linking the GSM directly to the impedance matrices obtained from MoM. To address practical challenges in storing GSM data across wide frequency bands and multiple antenna scenarios, we propose a efficient compression scheme. This approach alleviates memory demands by selectively storing the dominant eigencomponents that govern scattering behavior. Numerical validation examples confirm the accuracy of our method by comparisons with full-wave simulation results. Furthermore, we introduce an efficient iterative procedure to predict antenna array performance, highlighting remarkable improvements in computational speed compared to conventional numerical methods. These results collectively demonstrate the GSM framework's strong potential for antenna-array design processes.
Wideband Cognitive Radio for Joint Communication and Sensing: Optimization of Subcarrier Allocation and beamforming
Authors: Diluka Galappaththige, Chintha Tellambura
Date: 2025-03-22 00:00:00

As data traffic grows, wireless systems shift to higher frequency bands (6 GHz and above), where radar systems also operate. This coexistence demands effective interference management and efficient wideband utilization. Cognitive Radio (CR) offers a solution but remains limited to single-node or narrowband systems. This paper introduces a generalized wideband CR-enabled communication and sensing system with multiple users and targets. We propose a communication and sensing sub-carrier allocations framework, followed by transmit beamforming for the primary communication BS and sensing signal design for the secondary radar BS. The goal is to maximize the communication sum rate while ensuring sensing requirements, minimizing interference, and adhering to power constraints. To solve the resulting non-convex problem, we develop a manifold optimization algorithm for communication-only sub-carriers and an alternating optimization approach using the generalized Rayleigh quotient and semidefinite relaxation for communication-sensing sub-carriers. Compared to a non-cooperative benchmark, the proposed system achieves a \qty{10}{\percent} gain in communication sum rate and a \qty{32}{\percent} gain in sensing sum rate with \num{12} BS antennas.
Joint Superimposed Pilot-aided Channel Estimation and Data Detection for FTN Signaling over Doubly-Selective Channels
Authors: Simin Keykhosravi, Ebrahim Bedeer
Date: 2025-03-21 00:00:00

Faster-than-Nyquist (FTN) signaling and superimposed pilot (SP) techniques are effective solutions for significantly enhancing the spectral efficiency (SE) in next-generation wireless communication systems. This paper proposes an innovative SP-aided channel estimation method for FTN signaling enhancing the SE over doubly-selective (i.e., time- and frequency-selective) channels. To avoid complex channel tracking, we utilize a basis expansion model (BEM) to characterize doubly-selective channel variations. We propose a frame structure that superimposes a known periodic pilot sequence onto the information sequence, avoiding SE loss by eliminating the additional overhead of multiplexed pilot (MP). Additionally, we find the optimal FTN signaling SP sequence that minimizes the mean square error (MSE) of the channel estimation. Expanding on our proposed SP-aided channel estimation method, we propose two detection methods: (1) an SP-aided separate channel estimation and data detection (SCEDD) method performing a single channel estimation followed by iterative data detection via a turbo equalizer, serving as a baseline for evaluating the SP-aided channel estimation method, and (2) an SP-aided joint channel estimation and data detection (JCEDD) method, which extends the SCEDD by updating the channel estimate in each turbo equalization iteration, becoming our primary focus for its superior performance. At equivalent SE and a higher fading rate on the order of $10^{-3}$, our simulations show that SP-aided SCEDD method outperforms MP-aided techniques in both MSE and BER, while the SP-aided JCEDD method delivers remarkable performance, where reference approaches fail to track rapid channel variations.
Sequential HW-Aware Precoding: Over-the-air cancellation of HWI in Downlink Cell-Free Massive MIMO with Serial Fronthaul
Authors: Antoine Durant, Asma Mabrouk, Rafik Zayani
Date: 2025-03-21 00:00:00

This paper addresses the critical challenge of mitigating hardware impairments (HWIs) in downlink cell-free massive MIMO (CF-mMIMO) networks while ensuring computational scalability. We propose a novel sequential hardware-aware (HW-aware) precoding technique that leverages the serial fronthaul topology to perform over-the-air HWI cancellation. This approach involves sequentially exchanging approximated user-perceived distortion information among successive access points (APs) for over-the-air HWI mitigation. Each AP independently computes its spatial multiplexing weights and transmits signals that counteract the distortions introduced by the preceding AP. We develop a problem formulation and present a closed-form solution for this method. For performance evaluation, we study two reference methods taken either from centralized massive MIMO literature (Tone Reservation [TR]) or tailored for CF-mMIMO networks (PAPR-aware precoding), both focusing on reducing the PAPR of OFDM signals in the downlink. Results indicate that the sequential HW-aware approach achieves a substantial increase in spectral efficiency (SE) in high-distortion scenarios, with an average SE increase factor of 1.8 under severe distortions. Additionally, the proposed method, which is executed locally, demonstrates better scalability, achieving a reduction of up to 40\% and 72\% in the total number of complex multiplications compared to the PAPR-aware and TR approaches, respectively. Finally, the sequential HW-aware precoder offers high performance even when applied to cost-effective APs with few antennas, presenting a promising and practical solution for HWI compensation in CF-mMIMO systems with serial fronthaul.
Cross-Band Modulation Design for Hybrid RF-Optical Systems
Authors: Thrassos K. Oikonomou, Sotiris A. Tegos, Panagiotis D. Diamantoulakis, George K. Karagiannidis
Date: 2025-03-21 00:00:00

We present a novel cross-band modulation framework that combines 3D modulation in the RF domain with intensity modulation and direct detection in the optical domain, the first such integration to enhance communication reliability. By harnessing cross-band diversity, the framework optimizes symbol mapping across RF and optical links, significantly boosting mutual information (MI) and reducing symbol error probability (SEP). Two practical modulation schemes implement this framework, both using quadrature amplitude modulation in the RF subsystem. The first is a linear cross-band mapping scheme, where RF symbols are mapped to optical intensity values via an analytically tractable optimization that ensures O(1) detection complexity while minimizing SEP. The second employs a deep neural network-generated (DNN-Gen) 3D constellation with a custom loss function that adaptively optimizes symbol placement to maximize MI and minimize SEP. Although DNN-Gen incurs higher computational complexity than the linear approach, it adapts the 3D constellation to varying signal-to-noise ratios, yielding significant performance gains. Furthermore, we derive a theoretical MI benchmark for the linear scheme, offering insights into the fundamental limits of RF-optical cross-band communication. Extensive Monte Carlo simulations confirm that both schemes outperform SoA cross-band modulation techniques, including cross-band pulse amplitude modulation, with notable improvements. Additionally, DNN-Gen maintains high performance over a range of RF SNRs, lessening the need for exhaustive training at every operating condition. Overall, these results establish our cross-band modulation framework as a scalable, high-performance solution for next-generation hybrid RF-optical networks, balancing low complexity with optimized symbol mapping to maximize system reliability and efficiency.
On the Sensing Performance of FMCW-based Integrated Sensing and Communications with Arbitrary Constellations
Authors: Daniel Gil Gaviria, Benedikt Geiger, Charlotte Muth, Laurent Schmalen
Date: 2025-03-21 00:00:00
Tasks: ISAC

Integrated sensing and communications (ISAC) is expected to play a major role in numerous future applications, e.g., smart cities. Leveraging native radar signals like the frequency modulated continuous wave (FMCW) waveform additionally for data transmission offers a highly efficient use of valuable physical radio frequency (RF) resources allocated for automotive radar applications. In this paper, we propose the adoption of higher-order modulation formats for data modulation onto an FMCW waveform and provide a comprehensive overview of the entire signal processing chain. We evaluate the impact of each component on the overall sensing performance. While alignment algorithms are essential for removing the information signal at the sensing receiver, they also introduce significant dispersion to the received signal. We analyze this effect in detail. Notably, we demonstrate that the impact of non-constant amplitude modulation on sensing performance is statistically negligible when the complete signal processing chain is considered. This finding highlights the potential for achieving high data rates in FMCW-ISAC systems without compromising the sensing capabilities.
Minimum Mean Squared Error Holographic Beamforming for Sum-Rate Maximization
Authors: Chandan Kumar Sheemar, Wali Ullah Khan, George C. Alexandropoulos, Manzoor Ahmed, Symeon Chatzinotas
Date: 2025-03-21 00:00:00

This paper studies the problem of hybrid holographic beamforming for sum-rate maximization in a communication system assisted by a reconfigurable holographic surface. Existing methodologies predominantly rely on gradient-based or approximation techniques necessitating iterative optimization for each update of the holographic response, which imposes substantial computational overhead. To address these limitations, we establish a mathematical relationship between the mean squared error (MSE) criterion and the holographic response of the RHS to enable alternating optimization based on the minimum MSE (MMSE). Our analysis demonstrates that this relationship exhibits a quadratic dependency on each element of the holographic beamformer. Exploiting this property, we derive closed-form optimal expressions for updating the holographic beamforming weights. Our complexity analysis indicates that the proposed approach exhibits only linear complexity in terms of the RHS size, thus, ensuring scalability for large-scale deployments. The presented simulation results validate the effectiveness of our MMSE-based holographic approach, providing useful insights.
Efficient Deployment of Deep MIMO Detection Using Learngene
Authors: Jinya Zhang, Jiajia Guo, Xiangyi Li, Chao-Kai Wen, Xin Geng, Shi Jin
Date: 2025-03-21 00:00:00
Tasks: Transfer Learning

Deep learning (DL) has introduced a new paradigm in multiple-input multiple-output (MIMO) detection, balancing performance and complexity. However, the practical deployment of DL-based detectors is hindered by poor generalization, necessitating costly retraining for different devices and scenarios. To address this challenge, this paper presents a novel knowledge transfer technique, termed learngene, for the design of a DL-based MIMO detector and proposes an efficient deployment framework. The proposed detector, SDNet, leverages zero-forcing detection outputs and least squares-estimated channel state information (CSI) as inputs. It is further optimized through a collective-individual paradigm to enhance knowledge transfer. In this paradigm, learngene, a reusable neural network (NN) segment, encapsulates detection meta-knowledge acquired from large-scale collective models trained by manufacturers. This segment can then be distributed to device-specific teams. By integrating learngene into different lightweight individual models, detection meta-knowledge is efficiently transferred across heterogeneous NNs, enabling adaptation to diverse devices and scenarios. Simulation results demonstrate that the proposed scheme enhances performance, enables rapid adaptation, and ensures high scalability, with transferred parameters comprising only 10.8% of the total model size.
Multifractal analysis based on the weak scaling exponent and applications to MEG recordings in neuroscience
Authors: Patrice Abry, Phipippe Ciuciu, Merlin Dumeur, Stéphane Jaffard, Guillaume Saës
Date: 2025-03-21 00:00:00
Tasks: valid

We develop the mathematical properties of a multifractal analysis of data based on the weak scaling exponent. The advantage of this analysis is that it does not require any a priori global regularity assumption on the analyzed signal, in contrast with the previously used H{\"o}lder or p-exponents. As an illustration, we show that this technique allows one to perform a multifractal analysis of MEG signals, which records electromagnetic brain activity, that was not theoretically valid using the formerly introduced methods based on H{\"o}lder or p-exponents.
Exploring Deep Learning Models for EEG Neural Decoding
Authors: Laurits Dixen, Stefan Heinrich, Paolo Burelli
Date: 2025-03-20 00:00:00
Tasks: Deep Learning, EEG

Neural decoding is an important method in cognitive neuroscience that aims to decode brain representations from recorded neural activity using a multivariate machine learning model. The THINGS initiative provides a large EEG dataset of 46 subjects watching rapidly shown images. Here, we test the feasibility of using this method for decoding high-level object features using recent deep learning models. We create a derivative dataset from this of living vs non-living entities test 15 different deep learning models with 5 different architectures and compare to a SOTA linear model. We show that the linear model is not able to solve the decoding task, while almost all the deep learning models are successful, suggesting that in some cases non-linear models are needed to decode neural representations. We also run a comparative study of the models' performance on individual object categories, and suggest how artificial neural networks can be used to study brain activity.
EEG-CLIP : Learning EEG representations from natural language descriptions
Authors: Tidiane Camaret N'dir, Robin Tibor Schirrmeister
Date: 2025-03-18 00:00:00
Tasks: Contrastive Learning, EEG, Eeg Decoding, Electroencephalogram (EEG)

Deep networks for electroencephalogram (EEG) decoding are currently often trained to only solve a specific task like pathology or gender decoding. A more general approach leveraging the medical reports of clinical EEG recordings is to learn mappings between medical reports and EEG recordings. This approach was pioneered in the computer vision domain matching images and their text captions and subsequently allowed to do successful zero-shot decoding using textual class prompts. In this work, we follow this approach and develop a contrastive learning framework EEG-CLIP that aligns EEG time series and their corresponding clinical text descriptions in a shared embedding space. We investigate its potential for versatile EEG decoding, assessing performance on a range of few-shot and zero-shot settings. Overall, results show that EEG-CLIP manages to nontrivially align text and EEG representations. Our work presents a promising approach to learn general EEG representations, which could enable easier analyses of diverse decoding questions through zero shot decoding or training task-specific models from fewer training examples. The code for reproducing our results is available at https://github.com/tidiane-camaret/EEGClip.
A Topological Data Analysis Framework for Quantifying Necrosis in Glioblastomas
Authors: Francisco Tellez, Enrique Torres-Giese
Date: 2025-03-21 00:00:00
Tasks: Topological Data Analysis

In this paper, we introduce a shape descriptor that we call "interior function". This is a Topological Data Analysis (TDA) based descriptor that refines previous descriptors for image analysis. Using this concept, we define subcomplex lacunarity, a new index that quantifies geometric characteristics of necrosis in tumors such as conglomeration. Building on this framework, we propose a set of indices to analyze necrotic morphology and construct a diagram that captures the distinct structural and geometric properties of necrotic regions in tumors. We present an application of this framework in the study of MRIs of Glioblastomas (GB). Using cluster analysis, we identify four distinct subtypes of Glioblastomas that reflect geometric properties of necrotic regions.
Theory-to-Practice Gap for Neural Networks and Neural Operators
Authors: Philipp Grohs, Samuel Lanthaler, Margaret Trautner
Date: 2025-03-23 00:00:00
Tasks: Operator learning

This work studies the sampling complexity of learning with ReLU neural networks and neural operators. For mappings belonging to relevant approximation spaces, we derive upper bounds on the best-possible convergence rate of any learning algorithm, with respect to the number of samples. In the finite-dimensional case, these bounds imply a gap between the parametric and sampling complexities of learning, known as the \emph{theory-to-practice gap}. In this work, a unified treatment of the theory-to-practice gap is achieved in a general $L^p$-setting, while at the same time improving available bounds in the literature. Furthermore, based on these results the theory-to-practice gap is extended to the infinite-dimensional setting of operator learning. Our results apply to Deep Operator Networks and integral kernel-based neural operators, including the Fourier neural operator. We show that the best-possible convergence rate in a Bochner $L^p$-norm is bounded by Monte-Carlo rates of order $1/p$.
Universal approximation property of neural stochastic differential equations
Authors: Anna P. Kwossek, David J. Prömel, Josef Teichmann
Date: 2025-03-20 00:00:00

We identify various classes of neural networks that are able to approximate continuous functions locally uniformly subject to fixed global linear growth constraints. For such neural networks the associated neural stochastic differential equations can approximate general stochastic differential equations, both of It\^o diffusion type, arbitrarily well. Moreover, quantitative error estimates are derived for stochastic differential equations with sufficiently regular coefficients.
Safe and Efficient Social Navigation through Explainable Safety Regions Based on Topological Features
Authors: Victor Toscano-Duran, Sara Narteni, Alberto Carlevaro, Rocio Gonzalez-Diaz, Maurizio Mongelli, Jerome Guzzi
Date: 2025-02-14 00:00:00
Tasks: Social Navigation, Topological Data Analysis

The recent adoption of artificial intelligence (AI) in robotics has driven the development of algorithms that enable autonomous systems to adapt to complex social environments. In particular, safe and efficient social navigation is a key challenge, requiring AI not only to avoid collisions and deadlocks but also to interact intuitively and predictably with its surroundings. To date, methods based on probabilistic models and the generation of conformal safety regions have shown promising results in defining safety regions with a controlled margin of error, primarily relying on classification approaches and explicit rules to describe collision-free navigation conditions. This work explores how topological features contribute to explainable safety regions in social navigation. Instead of using behavioral parameters, we leverage topological data analysis to classify and characterize different simulation behaviors. First, we apply global rule-based classification to distinguish between safe (collision-free) and unsafe scenarios based on topological properties. Then, we define safety regions, $S_\varepsilon$, in the topological feature space, ensuring a maximum classification error of $\varepsilon$. These regions are built with adjustable SVM classifiers and order statistics, providing robust decision boundaries. Local rules extracted from these regions enhance interpretability, keeping the decision-making process transparent. Our approach initially separates simulations with and without collisions, outperforming methods that not incorporate topological features. It offers a deeper understanding of robot interactions within a navigable space. We further refine safety regions to ensure deadlock-free simulations and integrate both aspects to define a compliant simulation space that guarantees safe and efficient navigation.
Sample-Efficient Reinforcement Learning of Koopman eNMPC
Authors: Daniel Mayfrank, Mehmet Velioglu, Alexander Mitsos, Manuel Dahmen
Date: 2025-03-24 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Reinforcement learning (RL) can be used to tune data-driven (economic) nonlinear model predictive controllers ((e)NMPCs) for optimal performance in a specific control task by optimizing the dynamic model or parameters in the policy's objective function or constraints, such as state bounds. However, the sample efficiency of RL is crucial, and to improve it, we combine a model-based RL algorithm with our published method that turns Koopman (e)NMPCs into automatically differentiable policies. We apply our approach to an eNMPC case study of a continuous stirred-tank reactor (CSTR) model from the literature. The approach outperforms benchmark methods, i.e., data-driven eNMPCs using models based on system identification without further RL tuning of the resulting policy, and neural network controllers trained with model-based RL, by achieving superior control performance and higher sample efficiency. Furthermore, utilizing partial prior knowledge about the system dynamics via physics-informed learning further increases sample efficiency.
Active Inference for Energy Control and Planning in Smart Buildings and Communities
Authors: Seyyed Danial Nazemi, Mohsen A. Jafari, Andrea Matta
Date: 2025-03-23 00:00:00
Tasks: Decision Making, Decision Making Under Uncertainty, energy management, Management, Privacy Preserving

Active Inference (AIF) is emerging as a powerful framework for decision-making under uncertainty, yet its potential in engineering applications remains largely unexplored. In this work, we propose a novel dual-layer AIF architecture that addresses both building-level and community-level energy management. By leveraging the free energy principle, each layer adapts to evolving conditions and handles partial observability without extensive sensor information and respecting data privacy. We validate the continuous AIF model against both a perfect optimization baseline and a reinforcement learning-based approach. We also test the community AIF framework under extreme pricing scenarios. The results highlight the model's robustness in handling abrupt changes. This study is the first to show how a distributed AIF works in engineering. It also highlights new opportunities for privacy-preserving and uncertainty-aware control strategies in engineering applications.