pwc-archive/papers-with-abstracts huggingface dataset:
Sparse Bayesian Generative Modeling for Joint Parameter and Channel Estimation
Authors: Benedikt Böck, Franz Weißer, Michael Baur, Wolfgang Utschick
Date: 2025-02-25 00:00:00

Leveraging the inherent connection between sensing systems and wireless communications can improve their overall performance and is the core objective of joint communications and sensing. For effective communications, one has to frequently estimate the channel. Sensing, on the other hand, infers properties of the environment mostly based on estimated physical channel parameters, such as directions of arrival or delays. This work presents a low-complexity generative modeling approach that simultaneously estimates the wireless channel and its physical parameters without additional computational overhead. To this end, we leverage a recently proposed physics-informed generative model for wireless channels based on sparse Bayesian generative modeling and exploit the feature of conditionally Gaussian generative models to approximate the conditional mean estimator.
Cost-Effective Single-Antenna RSSI Positioning Through Dynamic Radiation Pattern Analysis
Authors: Zhisheng Rong, Wenzhi Liu, Xiayue Liu, Zhixiang Xu, Yufei Jiang, Xu Zhu
Date: 2025-02-25 00:00:00
Tasks: Position

This paper presents a novel indoor positioning approach that leverages antenna radiation pattern characteristics through Received Signal Strength Indication (RSSI) measurements in a single-antenna system. By rotating the antenna or reconfiguring its radiation pattern, we derive a maximum likelihood estimation (MLE) algorithm that achieves near-optimal positioning accuracy approaching the Cramer-Rao lower bound (CRLB). Through theoretical analysis, we establish three fundamental theorems characterizing the estimation accuracy bounds and demonstrating how performance improves with increased signal-to-noise ratio, antenna rotation count, and radiation pattern variations. Additionally, we propose a two-position measurement strategy that eliminates dependence on receiving antenna patterns. Simulation results validate that our approach provides an effective solution for indoor robot tracking applications where both accuracy and system simplicity are essential considerations.
A Deep-Unfolding Approach to RIS Phase Shift Optimization Via Transformer-Based Channel Prediction
Authors: Ishan Koralege, Arthur S. de Sena, Nurul H. Mahmood, Farjam Karim, Dimuthu Lesthuruge, Samitha Gunarathne
Date: 2025-02-25 00:00:00
Tasks: Multivariate Time Series Forecasting, Prediction, Time Series Forecasting

Reconfigurable intelligent surfaces (RISs) have emerged as a promising solution that can provide dynamic control over the propagation of electromagnetic waves. The RIS technology is envisioned as a key enabler of sixth-generation networks by offering the ability to adaptively manipulate signal propagation through the smart configuration of its phase shift coefficients, thereby optimizing signal strength, coverage, and capacity. However, the realization of this technology's full potential hinges on the accurate acquisition of channel state information (CSI). In this paper, we propose an efficient CSI prediction framework for a RIS-assisted communication system based on the machine learning (ML) transformer architecture. Architectural modifications are introduced to the vanilla transformer for multivariate time series forecasting to achieve high prediction accuracy. The predicted channel coefficients are then used to optimize the RIS phase shifts. Simulation results present a comprehensive analysis of key performance metrics, including data rate and outage probability. Our results confirm the effectiveness of the proposed ML approach and demonstrate its superiority over other baseline ML-based CSI prediction schemes such as conventional deep neural networks and long short-term memory architectures, albeit at the cost of slightly increased complexity.
Integrated Localization and Communication with Sparse MIMO: Will Virtual Array Technology also Benefit Wireless Communication?
Authors: Hongqi Min, Xinrui Li, Ruoguang Li, Yong Zeng
Date: 2025-02-25 00:00:00

For the 6G wireless networks, achieving high-performance integrated localization and communication (ILAC) is critical to unlock the full potential of wireless networks. To simultaneously enhance localization and communication performance cost-effectively, this paper proposes sparse multiple-input multiple-output (MIMO) based ILAC with nested and co-prime sparse arrays deployed at the base station. Sparse MIMO relaxes the traditional half-wavelength antenna spacing constraint to enlarge the antenna aperture, thus enhancing localization degrees of freedom and providing finer spatial resolution. However, it also leads to undesired grating lobes, which may cause severe inter-user interference for communication and angular ambiguity for localization. While the latter issue can be effectively addressed by the virtual array technology, by forming sum or difference co-arrays via signal (conjugate) correlation among array elements, it is unclear whether the similar virtual array technology also benefits wireless communications for ILAC systems. In this paper, we first reveal that the answer to the above question is negative, by showing that forming virtual arrays for wireless communication will cause destruction of phase information, degradation of signal-to-noise ratio and aggravation of multi-user interference. Therefore, we propose the hybrid processing for sparse MIMO based ILAC, i.e., physical array based communication while virtual array based localization. To this end, we characterize the beam pattern of sparse arrays by three metrics, demonstrating that despite of the introduction of grating lobes, sparse arrays can also bring benefits to communications thanks to its narrower main lobe beam width than the conventional compact arrays. Extensive simulation results are presented to demonstrate the performance gains of sparse MIMO based ILAC over that based on the conventional compact MIMO.
Software implemented fault diagnosis of natural gas pumping unit based on feedforward neural network
Authors: Mykola Kozlenko, Olena Zamikhovska, Leonid Zamikhovskyi
Date: 2025-02-25 00:00:00
Tasks: Descriptive, Diagnostic, Fault Diagnosis, GPU

In recent years, more and more attention has been paid to the use of artificial neural networks (ANN) for diagnostics of gas pumping units (GPU). Usually, ANN training is carried out on models of GPU workflows, and generated sets of diagnostic data are used to simulate defect conditions. At the same time, the results obtained do not allow assessing the real state of the GPU. It is proposed to use the values of the characteristics of the acoustic and vibration processes of the GPU as the input data of the ANN. A descriptive statistical analysis of real vibration and acoustic processes generated by the operation of the GPU type GTK-25-i (Nuovo Pignone, Italy) has been carried out. The formation of packets of diagnostic signs arriving at the input of the ANN has been carried out. The diagnostic features are the five maximum amplitude components of the acoustic and vibration signals, as well as the value of the standard deviation for each sample. Diagnostic signs are calculated directly in the input pipeline of ANN data in real time for three technical states of the GPU. Using the frameworks TensorFlow, Keras, NumPy, pandas, in the Python 3 programming language, an architecture was developed for a deep fully connected feedforward ANN, training on the error backpropagation algorithm. The results of training and testing of the developed ANN are presented. During testing, it was found that the signal classification precision for the "nominal" state of all 1475 signal samples is 1.0000, for the "current" state, precision equils 0.9853, and for the "defective" state, precision is 0.9091. The use of the developed ANN makes it possible to classify the technical states of the GPU with an accuracy sufficient for practical use, which will prevent the occurrence of GPU failures. ANN can be used to diagnose GPU of any type and power.
Zero-Shot Semantic Communication with Multimodal Foundation Models
Authors: Jiangjing Hu, Haotian Wu, Wenjing Zhang, Fengyu Wang, Wenjun Xu, Hui Gao, Deniz Gündüz
Date: 2025-02-25 00:00:00
Tasks: Prompt Learning, Semantic Communication

Most existing semantic communication (SemCom) systems use deep joint source-channel coding (DeepJSCC) to encode task-specific semantics in a goal-oriented manner. However, their reliance on predefined tasks and datasets significantly limits their flexibility and generalizability in practical deployments. Multi-modal foundation models provide a promising solution by generating universal semantic tokens. Inspired by this, we introduce SemCLIP, a zero-shot SemCom framework leveraging the contrastive language-image pre-training (CLIP) model. By transmitting CLIP-generated image tokens instead of raw images, SemCLIP enables efficient SemCom under low bandwidth and challenging channel conditions, facilitating diverse downstream tasks and zero-shot applications. Specifically, we propose a DeepJSCC scheme for efficient CLIP token encoding. To mitigate potential degradation caused by compression and channel noise, a multi-modal transmission-aware prompt learning mechanism is designed at the receiver, which adapts prompts based on transmission quality, enhancing system robustness and channel adaptability. Simulation results demonstrate that SemCLIP outperforms the baselines, achieving a $41\%$ improvement in zero-shot performance at low signal-to-noise ratios. Meanwhile, SemCLIP reduces bandwidth usage by more than $50$-fold compared to alternative image transmission methods, demonstrating the potential of foundation models towards a generalized, task-agnostic SemCom solution.
Edge Training and Inference with Analog ReRAM Technology for Hand Gesture Recognition
Authors: Victoria Clerico, Anirvan Dutta, Donato Francesco Falcone, Wooseok Choi, Matteo Galetta, Tommaso Stecconi, András Horváth, Shokoofeh Varzandeh, Bert Jan Offrein, Mohsen Kaboli, Valeria Bragaglia
Date: 2025-02-25 00:00:00
Tasks: Feature Engineering, Gesture Recognition, Hand Gesture Recognition, Hand-Gesture Recognition

Tactile hand gesture recognition is a crucial task for user control in the automotive sector, where Human-Machine Interactions (HMI) demand low latency and high energy efficiency. This study addresses the challenges of power-constrained edge training and inference by utilizing analog Resistive Random Access Memory (ReRAM) technology in conjunction with a real tactile hand gesture dataset. By optimizing the input space through a feature engineering strategy, we avoid relying on large-scale crossbar arrays, making the system more suitable for edge deployment. Through realistic hardware-aware simulations that account for device non-idealities derived from experimental data, we demonstrate the functionalities of our analog ReRAM-based analog in-memory computing for on-chip training, utilizing the state-of-the-art Tiki-Taka algorithm. Furthermore, we validate the classification accuracy of approximately 91.4% for post-deployment inference of hand gestures. The results highlight the potential of analog ReRAM technology and crossbar architecture with fully parallelized matrix computations for real-time HMI systems at the Edge.
Generative AI-enabled Wireless Communications for Robust Low-Altitude Economy Networking
Authors: Changyuan Zhao, Jiacheng Wang, Ruichen Zhang, Dusit Niyato, Geng Sun, Hongyang Du, Dong In Kim, Abbas Jamalipour
Date: 2025-02-25 00:00:00

Low-Altitude Economy Networks (LAENets) have emerged as significant enablers of social activities, offering low-altitude services such as the transportation of packages, groceries, and medical supplies. Unlike traditional terrestrial networks, LAENets are characterized by control mechanisms and ever-changing operational factors, which make them more complex and susceptible to vulnerabilities. As applications of LAENet continue to expand, robustness of these systems becomes crucial. In this paper, we investigate a novel application of Generative Artificial Intelligence (GenAI) to improve the robustness of LAENets. We conduct a systematic analysis of robustness requirements for LAENets, complemented by a comprehensive review of robust Quality of Service (QoS) metrics from the wireless physical layer perspective. We then investigate existing GenAI-enabled approaches for robustness enhancement. This leads to our proposal of a novel diffusion-based optimization framework with a Mixture of Expert (MoE)-transformer actor network. In the robust beamforming case study, the proposed framework demonstrates its effectiveness by optimizing beamforming under uncertainties, achieving a more than 44% increase in the worst-case achievable secrecy rate. These findings highlight the significant potential of GenAI in strengthening LAENet robustness.
Multi-Cell Coordinated Beamforming for Integrate Communication and Multi-TMT Localization
Authors: Meidong Xia, Wei Xu, Jindan Xu, Zhenyao He, Zhaohui Yang, Derrick Wing Kwan Ng
Date: 2025-02-25 00:00:00
Tasks: Integrated sensing and communication, ISAC

This paper investigates integrated localization and communication in a multi-cell system and proposes a coordinated beamforming algorithm to enhance target localization accuracy while preserving communication performance. Within this integrated sensing and communication (ISAC) system, the Cramer-Rao lower bound (CRLB) is adopted to quantify the accuracy of target localization, with its closed-form expression derived for the first time. It is shown that the nuisance parameters can be disregarded without impacting the CRLB of time of arrival (TOA)-based target localization. Capitalizing on the derived CRLB, we formulate a nonconvex coordinated beamforming problem to minimize the CRLB while satisfying signal-to-interference-plus-noise ratio (SINR) constraints in communication. To facilitate the development of solution, we reformulate the original problem into a more tractable form and solve it through semi-definite programming (SDP). Notably, we show that the proposed algorithm can always obtain rank-one global optimal solutions under mild conditions. Finally, numerical results demonstrate the superiority of the proposed algorithm over benchmark algorithms and reveal the performance trade-off between localization accuracy and communication SINR.
Generating Correlation Matrices with Graph Structures Using Convex Optimization
Authors: Ali Fakhar, Kévin Polisano, Irène Gannaz, Sophie Achard
Date: 2025-02-25 00:00:00

This work deals with the generation of theoretical correlation matrices with specific sparsity patterns, associated to graph structures. We present a novel approach based on convex optimization, offering greater flexibility compared to existing techniques, notably by controlling the mean of the entry distribution in the generated correlation matrices. This allows for the generation of correlation matrices that better represent realistic data and can be used to benchmark statistical methods for graph inference.
Designing Waveforms with Adjustable PAPR for Integrated Sensing and Communication
Authors: Ahmad Bazzi, Marwa Chafii
Date: 2025-02-25 00:00:00
Tasks: Integrated sensing and communication, ISAC

This paper presents a new optimization framework dedicated for integrated sensing and communication (ISAC) waveform design. In particular, the problem aims at maximizing the total achievable sum-rate, through multi-user interference minimization, while preserving a certain level of similarity to a given desired radar waveform. Aiming towards feasible and practical PHY architectures, we also offer the flexibility of tuning the peak-to-average power ratio to a desired level. Towards this design, a non-convex optimization problem is formulated, and an alternating direction method of multipliers based solution is derived to converge towards the superiority of the final ISAC waveform. Finally, simulation results validate the proposed ISAC waveform design, as compared to state-of-the-art solutions.
Upper Mid-Band Spectrum for 6G: Vision, Opportunity and Challenges
Authors: Ahmad Bazzi, Roberto Bomfin, Marco Mezzavilla, Sundeep Rangan, Theodore Rappaport, Marwa Chafii
Date: 2025-02-25 00:00:00
Tasks: Astronomy

Driven by the pursuit of gigabit-per-second data speeds for future 6G mobile networks, in addition to the support of sensing and artificial intelligence applications, the industry is expanding beyond crowded sub-6 GHz bands with innovative new spectrum allocations. In this paper, we chart a compelling vision for 6G within the frequency range 3 (FR3) spectrum, i.e. $7.125$-$24.25$ $\GHz$, by delving into its key enablers and addressing the multifaceted challenges that lie ahead for these new frequency bands. Here we highlight the physical properties of this \textcolor{black}{never-before} used spectrum by reviewing recent channel measurements for outdoor and indoor environments, including path loss, delay and angular spreads, and material penetration loss, all which offer insights that underpin future 5G/6G wireless communication designs. Building on the fundamental knowledge of the channel properties, we explore FR3 spectrum agility strategies that balance coverage and capacity (e.g. data rate) tradeoffs, while also examining coexistence with incumbent systems, such as satellites, radio astronomy, and earth exploration. Moreover, we discuss the potential of massive multiple-input multiple-output, compact and digital architectures, and evaluate the potential of multiband sensing for FR3 integrated sensing and communications. Finally, we outline 6G standardization features that are likely to emerge from 3GPP radio frame innovations and open radio access network developments.
Optimum Noncoherent Detection of Constant-Envelope Signals using Received Signal Magnitudes -- Energy Detection and Amplitude Detection
Authors: Mu Jia, Junting Chen, Ying-Chang Liang, Pooi-Yuen Kam
Date: 2025-02-25 00:00:00

Constant-envelope signals are widely employed in wireless communication systems due to their hardware-friendly design, energy efficiency, and enhanced reliability. However, detecting these signals reliably using simple, power-efficient receivers remains a critical challenge. While coherent detection methods generally offer superior performance, they require complex frequency synchronization, which increases receiver complexity and power consumption. In contrast, noncoherent detection is inherently simpler since it avoids frequency synchronization. However, traditional noncoherent detection approaches still rely on In-phase and Quadrature-phase (IQ) demodulators to extract the noisy received signal magnitudes, and assume the energy detector as the test statistic according to the IQ structure, without rigorous theoretical justification. Motivated by the practical need for robust and low-complexity detection, this paper proposes a comprehensive framework for optimal signal detection using a simple bandpass-filter envelope-detector (BFED) in conjunction with a Bayesian approach and the generalized likelihood ratio test (GLRT) under unknown amplitude conditions. By leveraging approximations of the modified Bessel functions, we derive two distinct regimes of the optimal detector. In the low SNR regime, we rigorously prove that the energy detector emerges as the Bayesian-optimal solution, thereby establishing its theoretical validity for the first time. In the high SNR regime, we derive a novel amplitude-based detector that directly compares the estimated amplitude against the noise standard deviation, leading to a simple yet optimal detection strategy. Numerical simulations validate the theoretical findings, confirming that both the energy and amplitude detectors achieve the minimum error probability in their respective SNR domains.
Arrhythmia Classification from 12-Lead ECG Signals Using Convolutional and Transformer-Based Deep Learning Models
Authors: Andrei Apostol, Maria Nutu
Date: 2025-02-25 00:00:00
Tasks: Diagnostic

In Romania, cardiovascular problems are the leading cause of death, accounting for nearly one-third of annual fatalities. The severity of this situation calls for innovative diagnosis method for cardiovascular diseases. This article aims to explore efficient, light-weight and rapid methods for arrhythmia diagnosis, in resource-constrained healthcare settings. Due to the lack of Romanian public medical data, we trained our systems using international public datasets, having in mind that the ECG signals are the same regardless the patients' nationality. Within this purpose, we combined multiple datasets, usually used in the field of arrhythmias classification: PTB-XL electrocardiography dataset , PTB Diagnostic ECG Database, China 12-Lead ECG Challenge Database, Georgia 12-Lead ECG Challenge Database, and St. Petersburg INCART 12-lead Arrhythmia Database. For the input data, we employed ECG signal processing methods, specifically a variant of the Pan-Tompkins algorithm, useful in arrhythmia classification because it provides a robust and efficient method for detecting QRS complexes in ECG signals. Additionally, we used machine learning techniques, widely used for the task of classification, including convolutional neural networks (1D CNNs, 2D CNNs, ResNet) and Vision Transformers (ViTs). The systems were evaluated in terms of accuracy and F1 score. We annalysed our dataset from two perspectives. First, we fed the systems with the ECG signals and the GRU-based 1D CNN model achieved the highest accuracy of 93.4% among all the tested architectures. Secondly, we transformed ECG signals into images and the CNN2D model achieved an accuracy of 92.16%.
5G Direct Position Estimation for Precise Localization in Dense Urban Area
Authors: Sijia Li, Sergio Vicenzo, Bing Xu
Date: 2025-02-25 00:00:00
Tasks: Position

In recent years, the fifth-generation (5G) new radio (NR) signals have emerged as a promising supplementary resource for urban navigation. However, a major challenge in utilizing 5G signals lies in their vulnerability to non-line-of-sight (NLoS) propagation effects, which are especially prevalent in urban street canyons. This paper applies the direct position estimation (DPE) method to 5G cellular signals to mitigate the NLoS bias as well as the multipath effects, thereby enabling precise localization in urbanized environments. The feasibility of applying the DPE method to NR positioning is analyzed, followed by a discussion of the tapped delay line (TDL) channel propagation model provided by the 3rd Generation Partnership Project (3GPP). The positioning performance is then evaluated through large-scale system-level simulations. The simulation results demonstrate that 5G DPE achieves satisfactory positioning accuracy in a 10 dB noisy channel, with an overall root mean square error (RMSE) constrained within 6 m. In addition, 5G DPE outperforms the observed time difference of arrival (OTDoA) method by 95.24% in terms of positioning accuracy in an NLoS-dominated propagation environment.