pwc-archive/papers-with-abstracts huggingface dataset:
A Comparative Study of Invariance-Aware Loss Functions for Deep Learning-based Gridless Direction-of-Arrival Estimation
Authors: Kuan-Lin Chen, Bhaskar D. Rao
Date: 2025-03-16 00:00:00
Tasks: Deep Learning, Direction of Arrival Estimation

Covariance matrix reconstruction has been the most widely used guiding objective in gridless direction-of-arrival (DoA) estimation for sparse linear arrays. Many semidefinite programming (SDP)-based methods fall under this category. Although deep learning-based approaches enable the construction of more sophisticated objective functions, most methods still rely on covariance matrix reconstruction. In this paper, we propose new loss functions that are invariant to the scaling of the matrices and provide a comparative study of losses with varying degrees of invariance. The proposed loss functions are formulated based on the scale-invariant signal-to-distortion ratio between the target matrix and the Gram matrix of the prediction. Numerical results show that a scale-invariant loss outperforms its non-invariant counterpart but is inferior to the recently proposed subspace loss that is invariant to the change of basis. These results provide evidence that designing loss functions with greater degrees of invariance is advantageous in deep learning-based gridless DoA estimation.
Revisiting MUSIC: A Finite-Precision Perspective
Authors: Yiming Fang, Li Chen, Ang Chen, Weidong Wang
Date: 2025-03-16 00:00:00

The high computational complexity of the multiple signal classification (MUSIC) algorithm is mainly caused by the subspace decomposition and spectrum search, especially for frequent real-time applications or massive sensors. In this paper, we propose a low-complexity MUSIC algorithm from a finite-precision arithmetic perspective. First, we analyze the computational bottlenecks of the classic low-complexity randomized unitary-based MUSIC (RU-MUSIC), formulating this computational issue as an inner product problem. Then, a mixed-precision method is introduced to address this problem. Specifically, this method partitions summations in inner products into blocks, where intra-block computations use low-precision arithmetic and inter-block sums use high-precision arithmetic. To further improve computational accuracy, we develop an adaptive-precision method that supports adaptive block sizes and multiple precision levels. Finally, simulation results show that the proposed finite-precision MUSIC design achieves direction-of-arrival (DOA) estimation performance similar to that using full-precision arithmetic while reducing more than 50\% computational cost.
AI-driven 6G Air Interface: Technical Usage Scenarios and Balanced Design Methodology
Authors: XiaoYun Wang, Shuangfeng Han, Zhiming Liu, Qixing Wang, Jiangzhou Wang, Chih-Lin I
Date: 2025-03-16 00:00:00

This paper systematically analyzes the typical application scenarios and key technical challenges of AI in 6G air interface transmission, covering important areas such as performance enhancement of single functional modules, joint optimization of multiple functional modules, and low-complexity solutions to complex mathematical problems. Innovatively, a three-dimensional joint optimization design criterion is proposed, which comprehensively considers AI capability, quality, and cost. By maximizing the ratio of multi-scenario communication capability to comprehensive cost, a triangular equilibrium is achieved, effectively addressing the lack of consideration for quality and cost dimensions in existing design criteria. The effectiveness of the proposed method is validated through multiple design examples, and the technical pathways and challenges for air interface AI standardization are thoroughly discussed. This provides significant references for the theoretical research and engineering practice of 6G air interface AI technology.
How Do Microstrip Losses Impact Near-Field Beam Depth in Dynamic Metasurface Antennas?
Authors: Panagiotis Gavriilidis, George C. Alexandropoulos
Date: 2025-03-15 00:00:00
Tasks: Position

The convergence of eXtremely Large (XL) antenna arrays and high-frequency bands in future wireless networks will inevitably give rise to near-field communications, localization, and sensing. Dynamic Metasurface Antennas (DMAs) have emerged as a key enabler of the XL Multiple-Input Multiple-Output (MIMO) paradigm, leveraging reconfigurable metamaterials to support large antenna arrays. However, DMAs are inherently lossy due to propagation losses in the microstrip lines and radiative losses from the metamaterial elements, which reduce their gain and alter their beamforming characteristics compared to a lossless aperture. In this paper, we address the gap in understanding how DMA losses affect near-field beamforming performance, by deriving novel analytical expressions for the beamforming gain of DMAs under misalignments between the focusing position and the intended user's position in 3D space. Additionally, we derive beam depth limits for varying attenuation conditions, from lossless to extreme attenuation, offering insights into the impact of losses on DMA near-field performance.
Indoor Positioning for Public Safety: Role of UAVs, LEOs, and Propagation-Aware Techniques
Authors: Gaurav Duggal, Harish K. Dureppagari, Harpreet S. Dhillon, Jeffrey H. Reed, R. Michael Buehrer
Date: 2025-03-15 00:00:00

Effective indoor positioning is critical for public safety, enabling first responders to locate at-risk individuals accurately during emergency scenarios. However, traditional Global Navigation Satellite Systems (GNSS) often perform poorly indoors due to poor coverage and non-line-of-sight (NLOS) conditions. Moreover, relying on fixed cellular infrastructure, such as terrestrial networks (TNs), may not be feasible, as indoor signal coverage from a sufficient number of base stations or WiFi access points cannot be guaranteed for accurate positioning. In this paper, we propose a rapidly deployable indoor positioning system (IPS) leveraging mobile anchors, including uncrewed aerial vehicles (UAVs) and Low-Earth-Orbit (LEO) satellites, and discuss the role of GNSS and LEOs in localizing the mobile anchors. Additionally, we discuss the role of sidelink-based positioning, which is introduced in 3rd Generation Partnership Project (3GPP) Release 18, in enabling public safety systems. By examining outdoor-to-indoor (O2I) signal propagation, particularly diffraction-based approaches, we highlight how propagation-aware positioning methods can outperform conventional strategies that disregard propagation mechanism information. The study highlights how emerging 5G Advanced and Non-Terrestrial Networks (NTN) features offer new avenues to improve positioning in challenging indoor environments, ultimately paving the way for cost-effective and resilient IPS solutions tailored to public safety applications.
Optimal Hybrid Transmit Beamforming for mm-Wave Integrated Sensing and Communication
Authors: Jitendra Singh, Banda Naveen, Suraj Srivastava, Aditya K. Jagannatham, Lajos Hanzo
Date: 2025-03-15 00:00:00
Tasks: Fairness, Integrated sensing and communication, ISAC, Unity

A hybrid beamformer (HBF) is designed for integrated sensing and communication (ISAC)-aided millimeter wave (mmWave) systems. The ISAC base station (BS), relying on a limited number of radio frequency (RF) chains, supports multiple communication users (CUs) and simultaneously detects the radar target (RT). To maximize the probability of detection (PD) of the RT, and achieve rate fairness among the CUs, we formulate two problems for the optimization of the RF and baseband (BB) transmit precoders (TPCs): PD-maximization (PD-max) and geometric mean rate-maximization (GMR-max), while ensuring the quality of services (QoS) of the RT and CUs. Both problems are highly non-convex due to the intractable expressions of the PD and GMR and also due to the non-convex unity magnitude constraints imposed on each element of the RF TPC. To solve these problems, we first transform the intractable expressions into their tractable counterparts and propose a power-efficient bisection search and majorization and minimization-based alternating algorithms for the PD-max and GMR-max problems, respectively. Furthermore, both algorithms optimize the BB TPC and RF TPCs in an alternating fashion via the successive convex approximation (SCA) and penalty-based Riemannian conjugate gradient (PRCG) techniques, respectively. Specifically, in the PRCG method, we initially add all the constraints except for the unity magnitude constraint to the objective function as a penalty term and subsequently employ the RCG method for optimizing the RF TPC. Finally, we present our simulation results and compare them to the benchmarks for demonstrating the efficacy of the proposed algorithms.
A Novel Environment Object Modeling Method for Vehicular ISAC Scenarios
Authors: Hanyuan Jiang, Yuxiang Zhang, Yameng Liu, Jianhua Zhang, Lei Tian, Tao Jiang
Date: 2025-03-15 00:00:00
Tasks: Integrated sensing and communication, ISAC

Integrated Sensing and Communication (ISAC), as a fundamental technology of 6G, empowers Vehicle-to-Everything (V2X) systems with enhanced sensing capabilities. One of its promising applications is the reliance on constructed maps for vehicle positioning. Traditional positioning methods primarily rely on Line-of-Sight (LOS), but in urban vehicular scenarios, obstructions often result in predominantly Non-Line-of-Sight (NLOS) conditions. Existing research indicates that NLOS paths, characterized by one-bounce reflection on building walls with determined delay and angle, can support sensing and positioning. However, experimental validation remains insufficient. To address this gap, channel measurements are conducted in an urban street to explore the existence of strong reflected paths in the presence of a vehicle target. The results show significant power contribution from NLOS paths, with large Environmental Objects (EOs) playing a key role in shaping NLOS propagation. Then, a novel model for EO reflection is proposed to extend the Geometry-Based Stochastic Model (GBSM) for ISAC channel standardization. Simulation results validate the model's ability to capture EO's power and position characteristics, showing that higher EO-reflected power and closer distance to Rx reduce Delay Spread (DS), which is more favorable for positioning. This model provides theoretical guidance and empirical support for ISAC positioning algorithms and system design in vehicular scenarios.
WiFi-Diffusion: Achieving Fine-Grained WiFi Radio Map Estimation With Ultra-Low Sampling Rate by Diffusion Models
Authors: Zhiyuan Liu, Shuhang Zhang, Qingyu Liu, Hongliang Zhang, Lingyang Song
Date: 2025-03-15 00:00:00

Fine-grained radio map presents communication parameters of interest, e.g., received signal strength, at every point across a large geographical region. It can be leveraged to improve the efficiency of spectrum utilization for a large area, particularly critical for the unlicensed WiFi spectrum. The problem of fine-grained radio map estimation is to utilize radio samples collected by sparsely distributed sensors to infer the map. This problem is challenging due to the ultra-low sampling rate, where the number of available samples is far less than the fine-grained resolution required for radio map estimation. We propose WiFi-Diffusion -- a novel generative framework for achieving fine-grained WiFi radio map estimation using diffusion models. WiFi-Diffusion employs the creative power of generative AI to address the ultra-low sampling rate challenge and consists of three blocks: 1) a boost block, using prior information such as the layout of obstacles to optimize the diffusion model; 2) a generation block, leveraging the diffusion model to generate a candidate set of radio maps; and 3) an election block, utilizing the radio propagation model as a guide to find the best radio map from the candidate set. Extensive simulations demonstrate that 1) the fine-grained radio map generated by WiFi-Diffusion is ten times better than those produced by state-of-the-art (SOTA) when they use the same ultra-low sampling rate; and 2) WiFi-Diffusion achieves comparable fine-grained radio map quality with only one-fifth of the sampling rate required by SOTA.
Impact of Frequency on Diffraction-Aided Wireless Positioning
Authors: Gaurav Duggal, Anand M. Kumar, R. Michael Buehrer, Harpreet S. Dhillon, Nishith Tripathi, Jeffrey H. Reed
Date: 2025-03-15 00:00:00

This paper tackles the challenge of accurate positioning in Non-Line-of-Sight (NLoS) environments, with a focus on indoor public safety scenarios where NLoS bias severely impacts localization performance. We explore Diffraction MultiPath Components (MPC) as a critical mechanism for Outdoor-to-Indoor (O2I) signal propagation and its role in positioning. The proposed system comprises outdoor Uncrewed Aerial Vehicle (UAV) transmitters and indoor receivers that require localization. To facilitate diffraction-based positioning, we develop a method to isolate diffraction MPCs at indoor receivers and validate its effectiveness using a ray-tracing-generated dataset, which we have made publicly available. Our evaluation across the FR1, FR2, and FR3 frequency bands within the 5G/6G spectrum confirms the viability of diffraction-based positioning techniques for next-generation wireless networks.
Internet of Things-Based Smart Precision Farming in Soilless Agriculture:Opportunities and Challenges for Global Food Security
Authors: Monica Dutta, Deepali Gupta, Sumegh Tharewal, Deepam Goyal, Jasminder Kaur Sandhu, Manjit Kaur, Ahmad Ali Alzubi, Jazem Mutared Alanazi
Date: 2025-03-15 00:00:00
Tasks: Decision Making

The rapid growth of the global population and the continuous decline in cultivable land pose significant threats to food security. This challenge worsens as climate change further reduces the availability of farmland. Soilless agriculture, such as hydroponics, aeroponics, and aquaponics, offers a sustainable solution by enabling efficient crop cultivation in controlled environments. The integration of the Internet of Things (IoT) with smart precision farming improves resource efficiency, automates environmental control, and ensures stable and high-yield crop production. IoT-enabled smart farming systems utilize real-time monitoring, data-driven decision-making, and automation to optimize water and nutrient usage while minimizing human intervention. This paper explores the opportunities and challenges of IoT-based soilless farming, highlighting its role in sustainable agriculture, urban farming, and global food security. These advanced farming methods ensure greater productivity, resource conservation, and year-round cultivation. However, they also face challenges such as high initial investment, technological dependency, and energy consumption. Through a comprehensive study, bibliometric analysis, and comparative analysis, this research highlights current trends and research gaps. It also outlines future directions for researchers, policymakers, and industry stakeholders to drive innovation and scalability in IoT-driven soilless agriculture. By emphasizing the benefits of vertical farming and Controlled Environment Agriculture (CEA)-enabled soilless techniques, this paper supports informed decision-making to address food security challenges and promote sustainable agricultural innovations.
Low Range-Doppler Sidelobe ISAC Waveform Design: A Low-Complexity Approach
Authors: Peishi Li, Ming Li, Rang Liu, Qian Liu, A. Lee Swindlehurst
Date: 2025-03-15 00:00:00
Tasks: Integrated sensing and communication, ISAC

Integrated sensing and communication (ISAC) is a pivotal enabler for next-generation wireless networks. A key challenge in ISAC systems lies in designing dual-functional waveforms that can achieve satisfactory radar sensing accuracy by effectively suppressing range-Doppler sidelobes. However, existing solutions are often computationally intensive, limiting their practicality in multi-input multi-output (MIMO) orthogonal frequency division multiplexing (OFDM) ISAC deployments. This paper presents a novel low-complexity algorithm leveraging the augmented Lagrangian method (ALM) and Riemannian conjugate gradient (RCG) optimization techniques to address these challenges. The proposed algorithm achieves superior sidelobe suppression compared to state-of-the-art methods while dramatically reducing computational complexity, making it highly suitable for real-world MIMO-OFDM ISAC systems. Simulation results demonstrate that the proposed approach not only outperforms existing benchmarks in sidelobe reduction but also accelerates convergence, ensuring efficient performance across communication and sensing tasks.
Channel and Spectrum Consumption Models for Urban Outdoor-to-Outdoor 28 GHz Wireless
Authors: Manav Kohli, Carlos E. Caicedo, Tingjun Chen, Irfan Tamim, Angel D. Estigarribia, Tianyi Dai, Igor Kadota, Dmitry Chizhik, Jinfeng Du, Rodolfo Feick, Reinaldo A. Valenzuela, Gil Zussman
Date: 2025-03-14 00:00:00

Next generation wireless and mobile networks will utilize millimeter-wave (mmWave) communication to achieve significantly increased data rates. However, since mmWave radio signals experience high path loss, the operation of mmWave networks will require accurate channel models designed for specific deployment sites. In this paper, we focus on the deployment area of the PAWR COSMOS testbed in New York City and report extensive 28 GHz channel measurements. These include over 46 million power measurements collected from over 3,000 links on 24 sidewalks at 4 different sites and in different settings. Using these measurements, we study the effects of the setup and environments (e.g., transmitter height and seasonal effects). We then discuss the obtained path gain values and their fitted lines, and the resulting effective azimuth beamforming gain. Based on these results, we also study the link SNR values that can be supported on individual sidewalks and the corresponding theoretically achievable data rates. Finally, we develop a process to transform the measurements and generate Spectrum Consumption Models (SCMs) based on the IEEE 1900.5.2 standard. The generated SCMs facilitate the evaluation of spectrum sharing and interference management scenarios since they capture all the directional propagation effects reflected in the measurements and provide a way to easily share the main propagation characterization results derived from the measurements. We believe that the results can inform the COSMOS testbed deployment process and provide a benchmark for other deployment efforts in dense urban areas.
Semi-Supervised Co-Training of Time and Time-Frequency Models: Application to Bearing Fault Diagnosis
Authors: Tuomas Jalonen, Mohammad Al-Sa'd, Serkan Kiranyaz, Moncef Gabbouj
Date: 2025-03-14 00:00:00
Tasks: Fault Diagnosis

Neural networks require massive amounts of annotated data to train intelligent solutions. Acquiring many labeled data in industrial applications is often difficult; therefore, semi-supervised approaches are preferred. We propose a new semi-supervised co-training method, which combines time and time-frequency (TF) machine learning models to improve performance and reliability. The developed framework collaboratively co-trains fast time-domain models by utilizing high-performing TF techniques without increasing the inference complexity. Besides, it operates in cloud-edge networks and offers holistic support for many applications covering edge-real-time monitoring and cloud-based updates and corrections. Experimental results on bearing fault diagnosis verify the superiority of our technique compared to a competing self-training method. The results from two case studies show that our method outperforms self-training for different noise levels and amounts of available data with accuracy gains reaching from 10.6% to 33.9%. They demonstrate that fusing time-domain and TF-based models offers opportunities for developing high-performance industrial solutions.
Federated Learning for Secure and Efficient Device Activity Detection in mMTC Networks
Authors: Ali Elkeshawy, Ibrahim Al Ghosh, Haifa Fares, Amor Nafkha
Date: 2025-03-14 00:00:00
Tasks: Action Detection, Activity Detection, Federated Learning

Grant-free random access in massive machine-type communications enables low-latency connectivity with minimal signaling. However, sporadic device activation requires efficient device activity detection. We propose a federated learning-based device activity detection approach, leveraging distributed training to enhance security and privacy while maintaining low computational complexity. Compared to existing methods, our solution achieves competitive detection performance, addressing scalability and security challenges in mMTC networks.
Analysis and Knowledge Discovery from Sensors Data to Improve Energy Efficiency
Authors: Xavier Vasques, Thibaut Possompes, Herve Rey, Marine Le Touze, Nicolas Auboin, Emmanuelle Passot, Benoit Lange
Date: 2025-03-12 00:00:00
Tasks: energy management, Management

Increases in energy prices and the global goal of mitigating CO2 emissions necessitate the development of intelligent Building Management Systems (BMS) that operate on an energy-efficient basis. Data Centers, buildings and/or group of buildings are often responsible for huge energy consumption. One way to monitor and optimize energy consumption is to instrument buildings using sensors (temperature, pressure, humidity ...) in order to track and solve wrong usage of energy management systems. The majority of the BMS are processing the data dynamically without taking into account the data history due to their constraint problems (time, bandwidth and calculation capability) and data resources. The RIDER project brings together a consortium of research laboratories and enterprises including IBM, to share their expertise in research and development of smart Information Technology (IT) energy platforms. In this context, we aim to improve energy efficiency of buildings or group of building (including data centers) using IT. One of the objectives is to identify valid, potentially useful, and ultimately understandable patterns in data for improving energy efficiency. We propose in this paper an approach of using an integrated platform able to interconnect instrumented buildings and sites, and to provide a high-level point of view for increasing our knowledge from sensors. The expected results are to estimate physical parameters that influence energy consumption based on data set history. Different correlation could be found between different variables, for example, indoor air quality and energy consumption. These results could be applied at a location where no sensor is placed and predict energy consumption from different variables.
Is Limited Participant Diversity Impeding EEG-based Machine Learning?
Authors: Philipp Bomatter, Henry Gouk
Date: 2025-03-11 00:00:00
Tasks: Diversity, EEG, Self-Supervised Learning

The application of machine learning (ML) to electroencephalography (EEG) has great potential to advance both neuroscientific research and clinical applications. However, the generalisability and robustness of EEG-based ML models often hinge on the amount and diversity of training data. It is common practice to split EEG recordings into small segments, thereby increasing the number of samples substantially compared to the number of individual recordings or participants. We conceptualise this as a multi-level data generation process and investigate the scaling behaviour of model performance with respect to the overall sample size and the participant diversity through large-scale empirical studies. We then use the same framework to investigate the effectiveness of different ML strategies designed to address limited data problems: data augmentations and self-supervised learning. Our findings show that model performance scaling can be severely constrained by participant distribution shifts and provide actionable guidance for data collection and ML research.