pwc-archive/papers-with-abstracts huggingface dataset:
Path Evolution Model for Endogenous Channel Digital Twin towards 6G Wireless Networks
Authors: Haoyu Wang, Zhi Sun, Shuangfeng Han, XiaoYun Wang, Shidong Zhou, Zhaocheng Wang
Date: 2025-01-25 00:00:00

Massive Multiple Input Multiple Output (MIMO) is critical for boosting 6G wireless network capacity. Nevertheless, high dimensional Channel State Information (CSI) acquisition becomes the bottleneck of 6G massive MIMO system. Recently, Channel Digital Twin (CDT), which replicates physical entities in wireless channels, has been proposed, providing site-specific prior knowledge for CSI acquisition. However, external devices (e.g., cameras and GPS devices) cannot always be integrated into existing communication systems, nor are they universally available across all scenarios. Moreover, the trained CDT model cannot be directly applied in new environments, which lacks environmental generalizability. To this end, Path Evolution Model (PEM) is proposed as an alternative CDT to reflect physical path evolutions from consecutive channel measurements. Compared to existing CDTs, PEM demonstrates virtues of full endogeneity, self-sustainability and environmental generalizability. Firstly, PEM only requires existing channel measurements, which is free of other hardware devices and can be readily deployed. Secondly, self-sustaining maintenance of PEM can be achieved in dynamic channel by progressive updates. Thirdly, environmental generalizability can greatly reduce deployment costs in dynamic environments. To facilitate the implementation of PEM, an intelligent and light-weighted operation framework is firstly designed. Then, the environmental generalizability of PEM is rigorously analyzed. Next, efficient learning approaches are proposed to reduce the amount of training data practically. Extensive simulation results reveal that PEM can simultaneously achieve high-precision and low-overhead CSI acquisition, which can serve as a fundamental CDT for 6G wireless networks.
Exploring the Limitations of Structured Orthogonal Dictionary Learning
Authors: Anirudh Dash, Aditya Siripuram
Date: 2025-01-25 00:00:00
Tasks: Dictionary Learning

This work is motivated by recent applications of structured dictionary learning, in particular when the dictionary is assumed to be the product of a few Householder atoms. We investigate the following two problems: 1) How do we approximate an orthogonal matrix $\mathbf{V}$ with a product of a specified number of Householder matrices, and 2) How many samples are required to learn a structured (Householder) dictionary from data? For 1) we discuss an algorithm that decomposes $\mathbf{V}$ as a product of a specified number of Householder matrices. We see that the algorithm outputs the decomposition when it exists, and give bounds on the approximation error of the algorithm when such a decomposition does not exist. For 2) given data $\mathbf{Y}=\mathbf{HX}$, we show that when assuming a binary coefficient matrix $\mathbf{X}$, the structured (Householder) dictionary learning problem can be solved with just $2$ samples (columns) in $\mathbf{Y}$.
Signal Whisperers: Enhancing Wireless Reception Using DRL-Guided Reflector Arrays
Authors: Hieu Le, Oguz Bedir, Mostafa Ibrahim, Jian Tao, Sabit Ekin
Date: 2025-01-25 00:00:00
Tasks: Deep Reinforcement Learning

This paper presents a novel approach for enhancing wireless signal reception through self-adjustable metallic surfaces, termed reflectors, which are guided by deep reinforcement learning (DRL). The designed reflector system aims to improve signal quality for multiple users in scenarios where a direct line-of-sight (LOS) from the access point (AP) and reflector to users is not guaranteed. Utilizing DRL techniques, the reflector autonomously modifies its configuration to optimize beam allocation from the AP to user equipment (UE), thereby maximizing path gain. Simulation results indicate substantial improvements in the average path gain for all UEs compared to baseline configurations, highlighting the potential of DRL-driven reflectors in creating adaptive communication environments.
Fast Inventory for 3GPP Ambient IoT Considering Device Unavailability due to Energy Harvesting
Authors: Zhikun Wu, Kazuk Takeda, Piyush Gupta, Ruiming Zheng, Luanxia Yang, Chengjin Zhang, Zhifei Fan, Hao Xu, Kiran Mukkavilli, Tingfang Ji
Date: 2025-01-25 00:00:00

With the growing demand for massive internet of things (IoT), new IoT technology, namely ambient IoT (A-IoT), has been studied in the 3rd Generation Partnership Project (3GPP). A-IoT devices are batteryless and consume ultra-low power, relying on energy harvesting and energy storage to capture a small amount of energy for communication. A promising usecase of A-IoT is inventory, where a reader communicates with hundreds of A-IoT devices to identify them. However, energy harvesting required before communication can significantly delay or even fail inventory completion. In this work, solutions including duty cycled monitoring (DCM), device grouping and low-power receiving chain are proposed. Evaluation results show that the time required for a reader to complete an inventory procedure for hundreds of A-IoT devices can be reduced by 50% to 83% with the proposed methods.
Graph Neural Network Based Beamforming and RIS Reflection Design in A Multi-RIS Assisted Wireless Network
Authors: Byungju Lim, Mai Vu
Date: 2025-01-24 00:00:00
Tasks: Graph Neural Network

We propose a graph neural network (GNN) architecture to optimize base station (BS) beamforming and reconfigurable intelligent surface (RIS) phase shifts in a multi-RIS assisted wireless network. We create a bipartite graph model to represent a network with multi-RIS, then construct the GNN architecture by exploiting channel information as node and edge features. We employ a message passing mechanism to enable information exchange between RIS nodes and user nodes and facilitate the inference of interference. Each node also maintains a representation vector which can be mapped to the BS beamforming or RIS phase shifts output. Message generation and update of the representation vector at each node are performed using two unsupervised neural networks, which are trained off-line and then used on all nodes of the same type. Simulation results demonstrate that the proposed GNN architecture provides strong scalability with network size, generalizes to different settings, and significantly outperforms conventional algorithms.
Channel-Aware Constellation Design for Digital OTA Computation
Authors: Zeyang Li, Chen Chen, Carlo Fischione
Date: 2025-01-24 00:00:00
Tasks: Quantization

Over-the-air (OTA) computation has emerged as a promising technique for efficiently aggregating data from massive numbers of wireless devices. OTA computations can be performed by analog or digital communications. Analog OTA systems are often constrained by limited function adaptability and their reliance on analog amplitude modulation. On the other hand, digital OTA systems may face limitations such as high computational complexity and limited adaptability to varying network configurations. To address these challenges, this paper proposes a novel digital OTA computation system with a channel-aware constellation design for demodulation mappers. The proposed system dynamically adjusts the constellation based on the channel conditions of participating nodes, enabling reliable computation of various functions. By incorporating channel randomness into the constellation design, the system prevent overlap of constellation points, reduces computational complexity, and mitigates excessive transmit power consumption under poor channel conditions. Numerical results demonstrate that the system achieves reliable NMSE performance across a range of scenarios, offering valuable insights into the choice of signal processing methods and weighting strategies under varying computation point configurations, node counts, and quantization levels. This work advances the state of digital OTA computation by addressing critical challenges in scalability, transmit power consumption, and function adaptability.
Joint Precoder and Reflector Design for RIS-assisted Multi-user OAM Communication Systems
Authors: Xiaoyan Ma, Yufei Zhao, Haixia Zhang, Yong Liang Guan, Chau Yuen
Date: 2025-01-24 00:00:00

Orbital angular momentum (OAM) can enhance the spectral efficiency by multiplying a set of orthogonal modes on the same frequency channel. To maintain the orthogonal among different OAM modes, perfect alignments between transmitters and receivers are strictly required. However, in multi-user OAM communications, the perfect alignments between the transmitter and all the receivers are impossible. The phase turbulence, caused by misaligned transmitters and receivers, leads to serious inter-mode interference, which greatly degrades the capacity of OAM transmissions. To eliminate the negative effects of phase turbulence and further enhance the transmission capacity, we introduce RIS into the system, and propose a joint precoder and reflector design for reconfigurable intelligent surface (RIS)-assisted multi-user OAM communication systems. Specifically, we propose a three-layer design at the transmitter side, which includes inter-user OAM mode interference cancellation, inter-mode self-interference elimination and the power allocation among different users. By analyzing the characteristics of the overall channels, we are able to give the specific expressions of the precoder designs, which significantly reduce the optimization complexity. We further leverage RIS to guarantee the line-ofsight (LoS) transmissions between the transmitter and users for better sum rate performance. To verify the superiority of the proposed multi-user OAM transmission system, we compare it with traditional MIMO transmission schemes, numerical results have shown that our proposed design can achieve better sum rate performance due to the well-designed orthogonality among different users and OAM modes.
Characterisation of exposure to non-ionising electromagnetic fields in the Spanish INMA birth cohort: Study protocol
Authors: M. Gallastegi, M. Guxens, A. Jimenez-Zabala, I. Calvente, M. Fernandez, L. Birks, B. Struchen, M. Vrijheid, M. Estarlich, M. F. Fernandez, M. Torrent, F. Ballester, J. J. Aurrekoetxea, J. Ibarluzea, D. Guerra, J. Gonzalez, M. Roosli, L. Santa-Marina
Date: 2025-01-24 00:00:00

Analysis of the association between exposure to electromagnetic fields of non-ionising radiation (EMF-NIR) and health in children and adolescents is hindered by the limited availability of data, mainly due to the difficulties on the exposure assessment. This study protocol describes the methodologies used for characterising exposure of children to EMF-NIR in the INMA (INfancia y Medio Ambiente- Environment and Childhood) Project, a prospective cohort study. Indirect (proximity to emission sources, questionnaires on sources use and geospatial propagation models) and direct methods (spot and fixed longer-term measurements and personal measurements) were conducted in order to assess exposure levels of study participants aged between 7 and 18 years old. The methodology used varies depending on the frequency of the EMF-NIR and the environment (homes, schools and parks). Questionnaires assessed the use of sources contributing both to Extremely Low Frequency (ELF) and Radiofrequency (RF) exposure levels. Geospatial propagation models (NISMap) are implemented and validated for environmental outdoor sources of RFs using spot measurements. Spot and fixed longer-term ELF and RF measurements were done in the environments where children spend most of the time. Moreover, personal measurements were taken in order to assess individual exposure to RF. The exposure data are used to explore their relationships with proximity and/or use of EMF-NIR sources.
Harmonization of Noise Measurement Methods: Measurements of radio impulsive noise from a specific source
Authors: Marta Fernandez, Iratxe landa, Amaia Arrinda, Ruben Torre, Manuel Velez
Date: 2025-01-24 00:00:00

This article describes a procedure for measuring and evaluating radio impulsive noise (IN) from a specific source. A good knowledge of the noise caused by different sources is essential to plan radio services and to ensure good quality of service. Moreover, it is necessary to harmonize noise measurement methods to achieve results that can be mutually compared. This article not only provides steps that should be followed to make proper measurements, but also specifies appropriate parameters to characterize the IN when it is generated by a principal source. A detailed description of parameter calculation is presented, based on the recommendations of the International Telecommunication Union (ITU). This answers the request made in an ITU-R 214-4/3 question, which suggests determining the appropriate parameters to describe the noise when it has an impulsive characteristic.
Calibrating Wireless AI via Meta-Learned Context-Dependent Conformal Prediction
Authors: Seonghoon Yoo, Sangwoo Park, Petar Popovski, Joonhyuk Kang, Osvaldo Simeone
Date: 2025-01-24 00:00:00
Tasks: Conformal Prediction, Meta-Learning

Modern software-defined networks, such as Open Radio Access Network (O-RAN) systems, rely on artificial intelligence (AI)-powered applications running on controllers interfaced with the radio access network. To ensure that these AI applications operate reliably at runtime, they must be properly calibrated before deployment. A promising and theoretically grounded approach to calibration is conformal prediction (CP), which enhances any AI model by transforming it into a provably reliable set predictor that provides error bars for estimates and decisions. CP requires calibration data that matches the distribution of the environment encountered during runtime. However, in practical scenarios, network controllers often have access only to data collected under different contexts -- such as varying traffic patterns and network conditions -- leading to a mismatch between the calibration and runtime distributions. This paper introduces a novel methodology to address this calibration-test distribution shift. The approach leverages meta-learning to develop a zero-shot estimator of distribution shifts, relying solely on contextual information. The proposed method, called meta-learned context-dependent weighted conformal prediction (ML-WCP), enables effective calibration of AI applications without requiring data from the current context. Additionally, it can incorporate data from multiple contexts to further enhance calibration reliability.
Wearable slot antenna at 2.45 GHz for off-body radiation: analysis of efficiency, frequency shift and body absorption
Authors: Marta Fernandez, Hugo G. Espinosa, David V. Thiel, Amaia Arrinda
Date: 2025-01-24 00:00:00

The interaction of body worn antennas with the human body causes a significant decrease in the antenna efficiency and a shift in the resonant frequency. A resonant slot in a small conductive box placed on the body has been shown to reduce these effects. The specific absorption rate (SAR) is less than international health standards for most wearable antennas due to the small transmitter power. This paper reports the linear relationship between the power absorbed by biological tissues at different locations on the body, and the radiation efficiency based on numerical modeling (r = 0.99). While the -10 dB bandwidth of the antenna remains constant and equal to 12.5%, the maximum frequency shift occurs when the antenna is close to the elbow (6.61%) and on the thigh (5.86%). The smallest change was found on the torso (4.21%). Participants with body-mass index (BMI) between 17 and 29 kg/m2 took part in experimental measurements, where the maximum frequency shift was 2.51%. Measurements show better agreement with simulations on the upper arm. These experimental results demonstrate that the BMI for each individual has little effect on the performance of the antenna.
Human Activity Recognition with a 6.5 GHz Reconfigurable Intelligent Surface for Wi-Fi 6E
Authors: Nuno Paulino, Mariana Oliveira, Francisco Ribeiro, Luís Outeiro, Pedro A. Lopes, Francisco Vilarinho, Sofia Inácio, Luís M. Pessoa
Date: 2025-01-24 00:00:00
Tasks: Activity Recognition, Gesture Recognition, Hand Gesture Recognition, Hand-Gesture Recognition, Human Activity Recognition

Human Activity Recognition (HAR) is the identification and classification of static and dynamic human activities, which find applicability in domains like healthcare, entertainment, security, and cyber-physical systems. Traditional HAR approaches rely on wearable sensors, vision-based systems, or ambient sensing, each with inherent limitations such as privacy concerns or restricted sensing conditions. Recently, Radio Frequency (RF)-based HAR has emerged, relying on the interaction of RF signals with people to infer activities. Reconfigurable Intelligent Surfaces (RISs) offers significant potential in this domain by enabling dynamic control over the wireless environment, thus enhancing the information extracted from RF signals. We present an Hand Gesture Recognition (HGR) approach that employs our own 6.5 GHz RIS design to manipulate the RF medium in an area of interest. We validate the capability of our RIS to control the medium by characterizing its steering response, and further we gather and publish a dataset for HGR classification for three different hand gestures. By employing two Convolutional Neural Networks (CNNs) models trained on data gathered under random and optimized RIS configuration sequences, we achieved classification accuracies exceeding 90%.
A Root Extension to the Propagator Algorithm for Direction of Arrival Estimation
Authors: Pradeep Dheerendra, Zoltan Derzsi
Date: 2025-01-23 00:00:00
Tasks: Direction of Arrival Estimation

In this paper, we propose an extension to the Propagator algorithm for source bearing estimation by performing root decomposition which eliminates the need for spectral search over angles. Further the propagator spatial spectrum is reused after performing the polynomial rooting to alleviate the observed drawback of an increase in RMSE at lower SNRs for estimating the directions-of-arrival of signals impinging on a linear array of sensors. Our proposed algorithm achieves a 98% reduction in computational complexity compared to Propagator alongside an improved angular resolution suitable for real time DOA estimation for wireless communications.
Domain-Factored Untrained Deep Prior for Spectrum Cartography
Authors: Subash Timilsina, Sagar Shrestha, Lei Cheng, Xiao Fu
Date: 2025-01-23 00:00:00
Tasks: Spectrum Cartography

Spectrum cartography (SC) focuses on estimating the radio power propagation map of multiple emitters across space and frequency using limited sensor measurements. Recent advances in SC have shown that leveraging learned deep generative models (DGMs) as structural constraints yields state-of-the-art performance. By harnessing the expressive power of neural networks, these structural "priors" capture intricate patterns in radio maps. However, training DGMs requires substantial data, which is not always available, and distribution shifts between training and testing data can further degrade performance. To address these challenges, this work proposes using untrained neural networks (UNNs) for SC. UNNs, commonly applied in vision tasks to represent complex data without training, encode structural information of data in neural architectures. In our approach, a custom-designed UNN represents radio maps under a spatio-spectral domain factorization model, leveraging physical characteristics to reduce sample complexity of SC. Experiments show that the method achieves performance comparable to learned DGM-based SC, without requiring training data.
A Synchrophasor Estimator Characterized by Attenuated Self-Interference and Robustness Against DC Offsets: the DCSOGI Interpolated DFT
Authors: César García-Veloso, Mario Paolone, José María Maza-Ortega
Date: 2025-01-23 00:00:00

The second-order generalized integrator (SOGI), which can be used to attenuate the self-interference of the fundamental tone, is unable to reject DC offsets on the input signal. Consequently, the performance of any SOGI based synchrophasor estimation (SE) technique might be compromised in the presence of such DC components. The current work presents a SE algorithm which adopts and enhanced SOGI formulation, robust against DC, combined with a three-point IpDFT and a Hanning window. Two alternative formulations relying respectively on the use of two and three nominal fundamental period observation windows are proposed and assessed for simultaneous compliance with both phasor measurement unit (PMU) P and M performance classes. This is done by means of a simulated environment where all the operating conditions defined by the IEC/IEEE Std. 60255-118-1-2018 are evaluated simultaneously combined with a $10\%$ static DC and under two different noise levels. Furthermore, both formulations adopt a dedicated mechanism for the detection and correction of low amplitude $2^{nd}$ harmonic tones to ensure their compliance with the standard can be maintained in the presence of such disturbances even under off-nominal frequency conditions. Finally the resilience of both methods against multiple simultaneous harmonic interferences is also analyzed.
The silent threat of methane to ecosystems: Insights from mechanistic modelling
Authors: Pranali Roy Chowdhury, Tianxu Wang, Shohel Ahmed, Hao Wang
Date: 2025-01-24 00:00:00

Over the past century, atmospheric methane levels have nearly doubled, posing a significant threat to ecosystems. Despite this, studies on its direct impact on species interactions are lacking. Although bioaccumulation theory explains the effects of contaminants in trophic levels, it is inadequate for gaseous pollutants such as methane. This study aims to bridge the gap by developing a methane-population-detritus model to investigate ecological impacts in aquatic and terrestrial ecosystems. Our findings show that low methane concentrations can enhance species growth, while moderate accumulation may induce sub-lethal effects over time. Elevated methane levels, however, lead to ecosystem collapse. Furthermore, prolonged exposure to the gas increases the sensitivity of species towards rising temperatures. Multiscale analysis reveals that rapid methane accumulation leads to long transients near the extinction states. We argue that high emission rates can push the system towards a critical threshold, where the ecosystem shifts to an alternative stable state characterized by elevated methane concentrations. This work highlights the urgent need for a better understanding of the fatal role of methane in ecosystems for developing strategies to mitigate its effects amid climate change.
Matrix Calculus (for Machine Learning and Beyond)
Authors: Paige Bright, Alan Edelman, Steven G. Johnson
Date: 2025-01-07 00:00:00

This course, intended for undergraduates familiar with elementary calculus and linear algebra, introduces the extension of differential calculus to functions on more general vector spaces, such as functions that take as input a matrix and return a matrix inverse or factorization, derivatives of ODE solutions, and even stochastic derivatives of random functions. It emphasizes practical computational applications, such as large-scale optimization and machine learning, where derivatives must be re-imagined in order to be propagated through complicated calculations. The class also discusses efficiency concerns leading to "adjoint" or "reverse-mode" differentiation (a.k.a. "backpropagation"), and gives a gentle introduction to modern automatic differentiation (AD) techniques.