pwc-archive/papers-with-abstracts huggingface dataset:
Graph Chirp Signal and Graph Fractional Vertex-Frequency Energy Distribution
Authors: Manjun Cui, Zhichao Zhang
Date: 2025-03-10 00:00:00
Tasks: Denoising

Graph signal processing (GSP) has emerged as a powerful framework for analyzing data on irregular domains. In recent years, many classical techniques in signal processing (SP) have been successfully extended to GSP. Among them, chirp signals play a crucial role in various SP applications. However, graph chirp signals have not been formally defined despite their importance. Here, we define graph chirp signals and establish a comprehensive theoretical framework for their analysis. We propose the graph fractional vertex-frequency energy distribution (GFED), which provides a powerful tool for processing and analyzing graph chirp signals. We introduce the general fractional graph distribution (GFGD), a generalized vertex-frequency distribution, and the reduced interference GFED, which can suppress cross-term interference and enhance signal clarity. Furthermore, we propose a novel method for detecting graph signals through GFED domain filtering, facilitating robust detection and analysis of graph chirp signals in noisy environments. Moreover, this method can be applied to real-world data for denoising more effective than some state-of-the-arts, further demonstrating its practical significance.
Graph Neural Network for Location- and Orientation-Assisted mmWave Beam Alignment
Authors: Yuzhu Lei, Qiqi Xiao, Yinghui He, Guanding Yu
Date: 2025-03-10 00:00:00
Tasks: Graph Neural Network

In massive multi-input multi-output (MIMO) systems, the main bottlenecks of location- and orientation-assisted beam alignment using deep neural networks (DNNs) are large training overhead and significant performance degradation. This paper proposes a graph neural network (GNN)-based beam selection approach that reduces the training overhead and improves the alignment accuracy, by capitalizing on the strong expressive ability and few trainable parameters of GNN. The channels of beams are correlated according to the beam direction. Therefore, we establish a graph according to the angular correlation between beams and use GNN to capture the channel correlation between adjacent beams, which helps accelerate the learning process and enhance the beam alignment performance. Compared to existing DNN-based algorithms, the proposed method requires only 20\% of the dataset size to achieve equivalent accuracy and improves the Top-1 accuracy by 10\% when using the same dataset.
Distributed Resource Block Allocation for Wideband Cell-free System
Authors: Yang Ma, Shengqian Han, Chenyang Yang
Date: 2025-03-10 00:00:00

This paper studies distributed resource block (RB) allocation in wideband orthogonal frequency-division multiplexing (OFDM) cell-free systems. We propose a novel distributed sequential algorithm and its two variants, which optimize RB allocation based on the information obtained through over-the-air (OTA) transmissions between access points (APs) and user equipments, enabling local decision updates at each AP. To reduce the overhead of OTA transmission, we further develop a distributed deep learning (DL)-based method to learn the RB allocation policy. Simulation results demonstrate that the proposed distributed algorithms perform close to the centralized algorithm, while the DL-based method outperforms existing baseline methods.
Physically Large Apertures for Wireless Power Transfer: Performance and Regulatory Aspects
Authors: Benjamin J. B. Deutschmann, Ulrich Muehlmann, Ahmet Kaplan, Gilles Callebaut, Thomas Wilding, Bert Cox, Liesbet Van der Perre, Fredrik Tufvesson, Erik G. Larsson, Klaus Witrisal
Date: 2025-03-09 00:00:00

Wireless power transfer (WPT) is a promising service for the Internet of Things, providing a cost-effective and sustainable solution to deploy so-called energy-neutral devices on a massive scale. The power received at the device side decays rapidly with the distance from a conventional transmit antenna with a physically small aperture. New opportunities arise from the transition from conventional far-field beamforming to near-field beam focusing. We argue that a "physically large" aperture, i.e., large w.r.t. the distance to the receiver, enables a power budget that remains practically independent of distance. Distance-dependent array gain patterns allow focusing the power density maximum precisely at the device location, while reducing the power density near the infrastructure. The physical aperture size is a key resource in enabling efficient yet regulatory-compliant WPT. We use real-world measurements to demonstrate that a regulatory-compliant system operating at sub-10GHz frequencies can increase the power received at the device into the milliwatt range. Our empirical demonstration shows that power-optimal near-field beam focusing inherently exploits multipath propagation, yielding both increased WPT efficiency and improved human exposure safety in real-world scenarios.
Joint Beamforming and Compressed Sensing for Uplink Grant-Free Access
Authors: Guoqing Xia, Pei Xiao, Bohan Li, Yue Zhang, Huiyu Zhou
Date: 2025-03-09 00:00:00
Tasks: compressed sensing

Compressed sensing (CS)-based techniques have been widely applied in the grant-free non-orthogonal multiple access (NOMA) to a single-antenna base station (BS). In this paper, we consider the multi-antenna reception at the BS for uplink grant-free access for the massive machine type communication (mMTC) with limited channel resources. To enhance the overloading performance of the BS, we develop a general framework for the synergistic amalgamation of the spatial division multiple access (SDMA) technique with the CS-based grant-free NOMA. We derive a closed-form statistical beamforming and a dynamic beamforming scheme for the inter-cluster interference suppression when applying SDMA. Based on this, we further develop a joint adaptive beamforming and subspace pursuit (JABF-SP) algorithm for the multiuser detection and data recovery, with a novel sparsity level decision method without the accurate knowledge of the noise level. To further improve the data recovery performance, we propose an interference cancellation based J-ABF-SP scheme (J-ABF-SP-IC) by using the initial signal estimates generated from the J-ABF-SP algorithm. Illustrative simulations verify the superior user detection and signal recovery performance of our proposed algorithms in comparison with existing CS-based grant-free NOMA techniques.
Joint Location and Velocity Estimation and Fundamental CRLB Analysis for Cell-Free MIMO-ISAC
Authors: Guoqing Xia, Pei Xiao, Qu Luo, Bing Ji, Yue Zhang, Huiyu Zhou
Date: 2025-03-09 00:00:00
Tasks: Computational Efficiency, Integrated sensing and communication, ISAC, parameter estimation

This paper presents a fundamental performance analysis of joint location and velocity estimation in a cell-free (CF) MIMO integrated sensing and communication (ISAC) system. Unlike prior studies that primarily rely on continuous-time signal models, we consider a more practical and challenging scenario in the discrete-time digital domain. Specifically, we first formulate a logarithmic likelihood function (LLF) and corresponding maximum likelihood estimation (MLE) for both single- and multiple-target sensing. Building upon the proposed LLF framework, closed-form Cramer-Rao lower bounds (CRLBs) for joint location and velocity estimation are derived under deterministic, unknown, and spatially varying radar cross-section (RCS) models. These CRLBs can serve as a fundamental performance metric to guide CF MIMO-ISAC system design. To enhance tractability, we also develop a class of simplified closed-form CRLBs, referred to as approximate CRLBs, along with a rigorous analysis of the conditions under which they remain accurate. Furthermore, we investigate how the sampling rate, squared effective bandwidth, and time width influence CRLB performance. For multi-target scenarios, the concepts of safety distance and safety velocity are introduced to characterize the conditions under which the CRLBs converge to their single-target counterparts. Extensive simulations using orthogonal frequency division multiplexing (OFDM) and orthogonal chirp division multiplexing (OCDM) validate the theoretical findings and provide practical insights for CF MIMO-ISAC system design
Learning of Uplink Resource Allocation with Multiuser QoS Constraints
Authors: Manru Yin, Shengqian Han, Chenyang Yang
Date: 2025-03-09 00:00:00

In the paper the joint optimization of uplink multiuser power and resource block (RB) allocation are studied, where each user has quality of service (QoS) constraints on both long- and short-blocklength transmissions. The objective is to minimize the consumption of RBs for meeting the QoS requirements, leading to a mixed-integer nonlinear programming (MINLP) problem. We resort to deep learning to solve the problem with low inference complexity. To provide a performance benchmark for learning based methods, we propose a hierarchical algorithm to find the global optimal solution in the single-user scenario, which is then extended to the multiuser scenario. The design of the learning method, however, is challenging due to the discrete policy to be learned, which results in either vanishing or exploding gradient during neural network training. We introduce two types of smoothing functions to approximate the involved discretizing processes and propose a smoothing parameter adaption method. Another critical challenge lies in guaranteeing the QoS constraints. To address it, we design a nonlinear function to intensify the penalties for minor constraint violations. Simulation results demonstrate the advantages of the proposed method in reducing the number of occupied RBs and satisfying QoS constraints reliably.
Hardware-Accelerated Event-Graph Neural Networks for Low-Latency Time-Series Classification on SoC FPGA
Authors: Hiroshi Nakano, Krzysztof Blachut, Kamil Jeziorek, Piotr Wzorek, Manon Dampfhoffer, Thomas Mesquida, Hiroaki Nishi, Tomasz Kryjak, Thomas Dalgaty
Date: 2025-03-09 00:00:00
Tasks: Graph Neural Network, Time Series, Time Series Classification

As the quantities of data recorded by embedded edge sensors grow, so too does the need for intelligent local processing. Such data often comes in the form of time-series signals, based on which real-time predictions can be made locally using an AI model. However, a hardware-software approach capable of making low-latency predictions with low power consumption is required. In this paper, we present a hardware implementation of an event-graph neural network for time-series classification. We leverage an artificial cochlea model to convert the input time-series signals into a sparse event-data format that allows the event-graph to drastically reduce the number of calculations relative to other AI methods. We implemented the design on a SoC FPGA and applied it to the real-time processing of the Spiking Heidelberg Digits (SHD) dataset to benchmark our approach against competitive solutions. Our method achieves a floating-point accuracy of 92.7% on the SHD dataset for the base model, which is only 2.4% and 2% less than the state-of-the-art models with over 10% and 67% fewer model parameters, respectively. It also outperforms FPGA-based spiking neural network implementations by 19.3% and 4.5%, achieving 92.3% accuracy for the quantised model while using fewer computational resources and reducing latency.
Performance Analysis of Multi-IRS Aided Multiple Operator Systems at mmWave Frequencies
Authors: Souradeep Ghosh, L. Yashvanth, Chandra R. Murthy
Date: 2025-03-09 00:00:00

Intelligent reflecting surfaces (IRSs) are envisioned to enhance the performance of mmWave wireless systems. In practice, multiple mobile operators (MO) coexist in an area and provide simultaneous and independent services to user-equipments (UEs) on different frequency bands. Then, if each MO deploys an IRS to enhance its performance, the IRSs also alter the channels of UEs of other MOs. In this context, this paper addresses the following questions: can an MO still continue to control its IRS independently of other MOs and IRSs? Is joint optimization of IRSs deployed by different MOs and inter-MO cooperation needed? To that end, by considering the mmWave bands, we first derive the ergodic sum spectral efficiency (SE) in a $2$-MO system for the following schemes: 1) joint optimization of an overall phase angle of the IRSs with MO cooperation, 2) MO cooperation via time-sharing, and 3) no cooperation between the MOs. We find that even with no cooperation between the MOs, the performance of a given MO is not degraded by the presence of an out-of-band (OOB) MO deploying and independently controlling its own IRS. On the other hand, the SE gain obtained at a given MO using joint optimization and cooperation over the no-cooperation scheme decreases inversely with the number of elements in the IRS deployed by the other MO. We generalize our results to a multiple MO setup and show that the gain in the sum-SE over the no-cooperation case increases at least linearly with the number of OOB MOs. Finally, we numerically verify our findings and conclude that every MO can independently operate and tune its IRS; cooperation via optimizing an overall phase only brings marginal benefits in practice.
Adaptive Subarray Segmentation: A New Paradigm of Spatial Non-Stationary Near-Field Channel Estimation for XL-MIMO Systems
Authors: Shuhang Yang, Puguang An, Peng Yang, Xianbin Cao, Dapeng Oliver Wu, Tony Q. S. Quek
Date: 2025-03-06 00:00:00
Tasks: Segmentation

To address the complexities of spatial non-stationary (SnS) effects and spherical wave propagation in near-field channel estimation (CE) for extremely large-scale multiple-input multiple-output (XL-MIMO) systems, this paper proposes an SnS-aware CE framework based on adaptive subarray partitioning. We first investigate spherical wave propagation and various SnS characteristics and construct an SnS near-field channel model for XL-MIMO systems. Due to the limitations of uniform array partitioning in capturing SnS, we analyze the adverse effects of the non-ideal array segmentation (over- and under-segmentation) on CE accuracy. To counter these issues, we develop a dynamic hybrid beamforming-assisted power-based subarray segmentation paradigm (DHBF-PSSP), which integrates power measurements with a dynamic hybrid beamforming structure to enable joint subarray partitioning and decoupling. A power-adaptive subarray segmentation (PASS) algorithm leverages the statistical properties of power profiles, while subarray decoupling is achieved via a subarray segmentation-based sampling method (SS-SM) under radio frequency (RF) chain constraints. For subarray CE, we propose a subarray segmentation-based assorted block sparse Bayesian learning algorithm under the multiple measurement vectors framework (SS-ABSBL-MMV). This algorithm exploits angular-domain block sparsity under a discrete Fourier transform (DFT) codebook and inter-subcarrier structured sparsity. Simulation results confirm that the proposed framework outperforms existing methods in CE performance.
UAV-Assisted Coverage Hole Detection Using Reinforcement Learning in Urban Cellular Networks
Authors: Mushfiqur Rahman, Ismail Guvenc, David Ramirez, Chau-Wai Wong
Date: 2025-03-09 00:00:00
Tasks: Reinforcement Learning (RL)

Deployment of cellular networks in urban areas requires addressing various challenges. For example, high-rise buildings with varying geometrical shapes and heights contribute to signal attenuation, reflection, diffraction, and scattering effects. This creates a high possibility of coverage holes (CHs) within the proximity of the buildings. Detecting these CHs is critical for network operators to ensure quality of service, as customers in these areas may experience weak or no signal reception. To address this challenge, we propose an approach using an autonomous vehicle, such as an unmanned aerial vehicle (UAV), to detect CHs, for minimizing drive test efforts and reducing human labor. The UAV leverages reinforcement learning (RL) to find CHs using stored local building maps, its current location, and measured signal strengths. As the UAV moves, it dynamically updates its knowledge of the signal environment and its direction to a nearby CH while avoiding collisions with buildings. We created a wide range of testing scenarios using building maps from OpenStreetMap and signal strength data generated by NVIDIA Sionna raytracing simulations. The results show that the RL-based approach outperforms non-machine learning, geometry-based methods in detecting CHs in urban areas. Additionally, even with a limited number of UAV measurements, the method achieves performance close to theoretical upper bounds that assume complete knowledge of all signal strengths.
Pulse Processing -- Overview and Challenges
Authors: Jonathan H. Manton
Date: 2025-03-09 00:00:00
Tasks: compressed sensing, Diagnostic, Position, Spike Sorting

The detection of irregularly spaced pulses of non-negligible width is a fascinating yet under-explored topic in signal processing. It sits adjacent to other core topics such as radar and symbol detection yet has its own distinctive challenges. Even modern techniques such as compressed sensing perform worse than may be expected on pulse processing problems. Real-world applications include nuclear spectroscopy, flow cytometry, seismic signal processing and neural spike sorting, and these in turn have applications to environmental radiation monitoring, surveying, diagnostic medicine, industrial imaging, biomedical imaging, top-down proteomics, and security screening, to name just a few. This overview paper endeavours to position the pulse processing problem in the context of signal processing. It also describes some current challenges in the field.
Experimental Demonstration of Over the Air Federated Learning for Cellular Networks
Authors: Suyash Pradhan, Asil Koc, Kubra Alemdar, Mohamed Amine Arfaoui, Philip Pietraski, Francois Periard, Guodong Zhang, Mario Hudon, Kaushik Chowdhury
Date: 2025-03-09 00:00:00
Tasks: Federated Learning

Over-the-air federated learning (OTA-FL) offers an exciting new direction over classical FL by averaging model weights using the physics of analog signal propagation. Since each participant broadcasts its model weights concurrently in time and frequency, this paradigm conserves communication bandwidth and model upload latency. Despite its potential, there is no prior large-scale demonstration on a real-world experimental platform. This paper proves for the first time that OTA-FL can be deployed in a cellular network setting within the constraints of a 5G compliant waveform. To achieve this, we identify challenges caused by multi-path fading effects, thermal noise at the radio devices, and maintaining highly precise synchronization across multiple clients to perform coherent OTA combining. To address these challenges, we propose a unified framework for real-time channel estimation, model weight to OFDM symbol mapping and dual-layer synchronization interface to perform OTA model training. We experimentally validate OTA-FL using two relevant applications - Channel Estimation and Object Classification, at a large-scale on ORBIT Testbed and a portable setup respectively, along with analyzing the benefits from the perspective of a telecom operator. Under specific experimental conditions, OTA-FL achieves equivalent model performance, supplemented with 43 times improvement in spectrum utilization and 7 times improvement in energy efficiency over classical FL when considering 5 nodes.
Hybrid CNN-Dilated Self-attention Model Using Inertial and Body-Area Electrostatic Sensing for Gym Workout Recognition, Counting, and User Authentification
Authors: Sizhen Bian, Vitor Fortes Rey, Siyu Yuan, Paul Lukowicz
Date: 2025-03-08 00:00:00
Tasks: Activity Recognition

While human body capacitance ($HBC$) has been explored as a novel wearable motion sensing modality, its competence has never been quantitatively demonstrated compared to that of the dominant inertial measurement unit ($IMU$) in practical scenarios. This work is thus motivated to evaluate the contribution of $HBC$ in wearable motion sensing. A real-life case study, gym workout tracking, is described to assess the effectiveness of $HBC$ as a complement to $IMU$ in activity recognition. Fifty gym sessions from ten volunteers were collected, bringing a fifty-hour annotated $IMU$ and $HBC$ dataset. With a hybrid CNN-Dilated neural network model empowered with the self-attention mechanism, $HBC$ slightly improves accuracy to the $IMU$ for workout recognition and has substantial advantages over $IMU$ for repetition counting. This work helps to enhance the understanding of $HBC$, a novel wearable motion-sensing modality based on the body-area electrostatic field. All materials presented in this work are open-sourced to promote further study \footnote{https://github.com/zhaxidele/Toolkit-for-HBC-sensing}.
Optimization of An Induced Magnetic Field-Based Positioning System
Authors: Sizhen Bian, Gerald Pirkl, Jingyuan Cheng, Paul Lukowicz
Date: 2025-03-08 00:00:00

Using oscillating magnetic fields for indoor positioning is a robust way to resist dynamic environments. This work presents the hard- and software-related optimizations of an induced magnetic field positioning system. We describe a new coil architecture for both the transmitter and receiver, reducing inter-axes cross-talk. A new analog circuit design on the receiver side attains an acceptable noise level and increases the detection range from 4m to 8m (the covered area is increased from $50m^2$ to $200m^2$). The median positioning error is reduced from 0.56~m to 0.25m in the near field with fingerprinting methods. Experiments in office and factory areas (including robotic and industrial equipment) demonstrate the system's robustness in large areas. This work aims to enlighten researchers working on the same topic with constructive optimization directions on their own induced magnetic field-based systems.
A Novel Distributed PV Power Forecasting Approach Based on Time-LLM
Authors: Huapeng Lin, Miao Yu
Date: 2025-03-08 00:00:00
Tasks: Large Language Model, Scheduling, Time Series, Time Series Forecasting, Zero-Shot Learning

Distributed photovoltaic (DPV) systems are essential for advancing renewable energy applications and achieving energy independence. Accurate DPV power forecasting can optimize power system planning and scheduling while significantly reducing energy loss, thus enhancing overall system efficiency and reliability. However, solar energy's intermittent nature and DPV systems' spatial distribution create significant forecasting challenges. Traditional methods often rely on costly external data, such as numerical weather prediction (NWP) and satellite images, which are difficult to scale for smaller DPV systems. To tackle this issue, this study has introduced an advanced large language model (LLM)-based time series forecasting framework Time-LLM to improve the DPV power forecasting accuracy and generalization ability. By reprogramming, the framework aligns historical power data with natural language modalities, facilitating efficient modeling of time-series data. Then Qwen2.5-3B model is integrated as the backbone LLM to process input data by leveraging its pattern recognition and inference abilities, achieving a balance between efficiency and performance. Finally, by using a flatten and linear projection layer, the LLM's high-dimensional output is transformed into the final forecasts. Experimental results indicate that Time-LLM outperforms leading recent advanced time series forecasting models, such as Transformer-based methods and MLP-based models, achieving superior accuracy in both short-term and long-term forecasting. Time-LLM also demonstrates exceptional adaptability in few-shot and zero-shot learning scenarios. To the best of the authors' knowledge, this study is the first attempt to explore the application of LLMs to DPV power forecasting, which can offer a scalable solution that eliminates reliance on costly external data sources and improve real-world forecasting accuracy.