pwc-archive/papers-with-abstracts huggingface dataset:
Friction and Road Condition Estimation by Combining Cause- and Effect-Based Methods using Bayesian Networks
Authors: Björn Volkmann, Karl-Philipp Kortmann, Ulrich Mair, Julian King
Date: 2024-07-16 00:00:00
Tasks: Friction

Knowledge about the maximum tire-road friction potential is an important factor to ensure the driving stability and traffic safety of the vehicle. Many authors proposed systems that either measure friction related parameters or estimate the friction coefficient directly via a mathematical model. However these systems can be negatively impacted by environmental factors or require a sufficient level of excitation in the form of tire slip, which is often too low under practical conditions. Therefore, this work investigates, if a more robust estimation can be achieved by fusing the information of multiple systems using a Bayesian network, which models the statistical relationship between the sensors and the maximum friction coefficient. First, the Bayesian network is evaluated over its entire domain to compare the inference process to all possible road conditions. After that, the algorithm is applied to data from a test vehicle to demonstrate the performance under real conditions.
RIS-Assisted High Resolution Radar Sensing
Authors: Martin Voigt Vejling, Hyowon Kim, Christophe A. N. Biscio, Henk Wymeersch, Petar Popovski
Date: 2024-07-16 00:00:00
Tasks: Diversity, parameter estimation

This paper analyzes monostatic sensing by a user equipment (UE) for a setting in which the UE is unable to resolve multiple targets due to their interference within a single resolution bin. It is shown how sensing accuracy, in terms of both detection rate and localization accuracy, can be boosted by a reconfigurable intelligent surface (RIS), which can be advantageously used to provide signal diversity and aid in resolving the targets. Specifically, assuming prior information on the presence of a cluster of targets, a RIS beam sweep procedure is used to facilitate the high resolution sensing. We derive the Cram\'er-Rao lower bounds (CRLBs) for channel parameter estimation and sensing and an upper bound on the detection probability. The concept of coherence is defined and analyzed theoretically. Then, we propose an orthogonal matching pursuit (OMP) channel estimation algorithm combined with data association to fuse the information of the non-RIS signal and the RIS signal and perform sensing. Finally, we provide numerical results to verify the potential of RIS for improving sensor resolution, and to demonstrate that the proposed methods can realize this potential for RIS-assisted high resolution sensing.
Batch SLAM with PMBM Data Association Sampling and Graph-Based Optimization
Authors: Yu Ge, Ossi Kaltiokallio, Yuxuan Xia, Ángel F. García-Fernández, Hyowon Kim, Jukka Talvitie, Mikko Valkama, Henk Wymeersch, Lennart Svensson
Date: 2024-07-16 00:00:00
Tasks: Simultaneous Localization and Mapping

Simultaneous localization and mapping (SLAM) methods need to both solve the data association (DA) problem and the joint estimation of the sensor trajectory and the map, conditioned on a DA. In this paper, we propose a novel integrated approach to solve both the DA problem and the batch SLAM problem simultaneously, combining random finite set (RFS) theory and the graph-based SLAM approach. A sampling method based on the Poisson multi-Bernoulli mixture (PMBM) density is designed for dealing with the DA uncertainty, and a graph-based SLAM solver is applied for the conditional SLAM problem. In the end, a post-processing approach is applied to merge SLAM results from different iterations. Using synthetic data, it is demonstrated that the proposed SLAM approach achieves performance close to the posterior Cram\'er-Rao bound, and outperforms state-of-the-art RFS-based SLAM filters in high clutter and high process noise scenarios.
A Deep Learning-Based Target Radial Length Estimation Method through HRRP Sequence
Authors: Lingfeng Chen, Panhe Hu, Zhiliang Pan, Xiao Sun, Zehao Wang
Date: 2024-07-16 00:00:00

This paper introduces an innovative deep learning-based method for end-to-end target radial length estimation from HRRP (High Resolution Range Profile) sequences. Firstly, the HRRP sequences are normalized and transformed into GAF (Gram Angular Field) images to effectively capture and utilize the temporal information. Subsequently, these GAF images serve as the input for a pretrained ResNet-101 model, which is then fine-tuned for target radial length estimation. The simulation results show that compared to traditional threshold method and simple networks e.g. one-dimensional CNN (Convolutional Neural Network), the proposed method demonstrates superior noise resistance and higher accuracy under low SNR (Signal-to-Noise Ratio) conditions.
Machine Learning in Communications: A Road to Intelligent Transmission and Processing
Authors: Shixiong Wang, Geoffrey Ye Li
Date: 2024-07-16 00:00:00

Prior to the era of artificial intelligence and big data, wireless communications primarily followed a conventional research route involving problem analysis, model building and calibration, algorithm design and tuning, and holistic and empirical verification. However, this methodology often encountered limitations when dealing with large-scale and complex problems and managing dynamic and massive data, resulting in inefficiencies and limited performance of traditional communication systems and methods. As such, wireless communications have embraced the revolutionary impact of artificial intelligence and machine learning, giving birth to more adaptive, efficient, and intelligent systems and algorithms. This technological shift opens a road to intelligent information transmission and processing. This overview article discusses the typical roles of machine learning in intelligent wireless communications, as well as its features, challenges, and practical considerations.
Green Resource Allocation in Cloud-Native O-RAN Enabled Small Cell Networks
Authors: Rana M. Sohaib, Syed Tariq Shah, Oluwakayode Onireti, Yusuf Sambo, M. A. Imran
Date: 2024-07-16 00:00:00
Tasks: Deep Reinforcement Learning, Transfer Learning

In the rapidly evolving landscape of 5G and beyond, cloud-native Open Radio Access Networks (O-RAN) present a paradigm shift towards intelligent, flexible, and sustainable network operations. This study addresses the intricate challenge of energy efficient (EE) resource allocation that services both enhanced Mobile Broadband (eMBB) and ultra-reliable low-latency communications (URLLC) users. We propose a novel distributed learning framework leveraging on-policy and off-policy transfer learning strategies within a deep reinforcement learning (DRL)--based model to facilitate online resource allocation decisions under different channel conditions. The simulation results explain the efficacy of the proposed method, which rapidly adapts to dynamic network states, thereby achieving a green resource allocation.
DRL-based Joint Resource Scheduling of eMBB and URLLC in O-RAN
Authors: Rana M. Sohaib, Syed Tariq Shah, Oluwakayode Onireti, Yusuf Sambo, Qammer H. Abbasi, M. A. Imran
Date: 2024-07-16 00:00:00
Tasks: Decision Making, Deep Reinforcement Learning, Scheduling, Thompson Sampling

This work addresses resource allocation challenges in multi-cell wireless systems catering to enhanced Mobile Broadband (eMBB) and Ultra-Reliable Low Latency Communications (URLLC) users. We present a distributed learning framework tailored to O-RAN network architectures. Leveraging a Thompson sampling-based Deep Reinforcement Learning (DRL) algorithm, our approach provides real-time resource allocation decisions, aligning with evolving network structures. The proposed approach facilitates online decision-making for resource allocation by deploying trained execution agents at Near-Real Time Radio Access Network Intelligent Controllers (Near-RT RICs) located at network edges. Simulation results demonstrate the algorithm's effectiveness in meeting Quality of Service (QoS) requirements for both eMBB and URLLC users, offering insights into optimising resource utilisation in dynamic wireless environments.
Generative AI Driven Task-Oriented Adaptive Semantic Communications
Authors: Yuzhou Fu, Wenchi Cheng, Jingqing Wang, Liuguo Yin, Wei zhang
Date: 2024-07-16 00:00:00
Tasks: Instance Segmentation, object-detection, Object Detection, Semantic Communication, Semantic Segmentation

Task-Oriented Semantic Communication (TOSC) has been regarded as a promising communication framework, serving for various Artificial Intelligence (AI) task driven applications. The existing TOSC frameworks focus on extracting the full semantic features of source data and learning low-dimensional channel inputs to transmit them within limited bandwidth resources. Although transmitting full semantic features can preserve the integrity of data meaning, this approach does not attain the performance threshold of the TOSC. In this paper, we propose a Task-oriented Adaptive Semantic Communication (TasCom) framework, which aims to effectively facilitate the execution of AI tasks by only sending task-related semantic features. In the TasCom framework, we first propose a Generative AI (GAI) architecture based Generative Joint Source-Channel Coding (G-JSCC) for efficient semantic transmission. Then, an Adaptive Coding Controller (ACC) is proposed to find the optimal coding scheme for the proposed G-JSCC, which allows the semantic features with significant contributions to the AI task to preferentially occupy limited bandwidth resources for wireless transmission. Furthermore, we propose a generative training algorithm to train the proposed TasCom for optimal performance. The simulation results show that the proposed TasCom outperforms the existing TOSC and traditional codec schemes on the object detection and instance segmentation tasks at all considered channel conditions.
Digital-Analog Transmission Framework for Task-Oriented Semantic Communications
Authors: Yuzhou Fu, Wenchi Cheng, Wei zhang
Date: 2024-07-16 00:00:00
Tasks: Semantic Communication

Task-Oriented Semantic Communication (TOSC) has been considered as a new communication paradigm to serve various samrt devices that depend on Artificial Intelligence (AI) tasks in future wireless networks. The existing TOSC frameworks rely on the Neural Network (NN) model to extract the semantic feature from the source data. The semantic feature, constituted by analog vectors of a lower dimensionality relative to the original source data, reserves the meaning of the source data. By conveying the semantic feature, TOSCs can significantly reduce the amount of data transmission while ensuring the correct execution of the AI-driven downstream task. However, standardized wireless networks depend on digital signal processing for data transmission, yet they necessitate the conveyance of semantic features that are inherently analog. Although existing TOSC frameworks developed the Deep Learning (DL) based \emph{analog approach} or conventional \emph{digital approach} to transmit the semantic feature, but there are still many challenging problems to urgently be solved in actual deployment. In this article, we first propose several challenging issues associated with the development of the TOSC framework in the standardized wireless network. Then, we develop a Digital-Analog transmission framework based TOSC (DA-TOSC) to resolve these challenging issues. Future research directions are discussed to further improve the DA-TOSC.
Phases Calibration of RIS Using Backpropagation Algorithm
Authors: Wei zhang, Bin Zhou, Tianyi Zhang, Yi Jiang, Zhiyong Bu
Date: 2024-07-16 00:00:00

Reconfigurable intelligent surface (RIS) technology has emerged in recent years as a promising solution to the ever-increasing demand for wireless communication capacity. In practice, however, elements of RIS may suffer from phase deviations, which need to be properly estimated and calibrated. This paper models the problem of over-the-air (OTA) estimation of the RIS elements as a quasi-neural network (QNN) so that the phase estimates can be obtained using the classic backpropagation (BP) algorithm. We also derive the Cram\'{e}r Rao Bounds (CRBs) for the phases of the RIS elements as a benchmark of the proposed approach. The simulation results verify the effectiveness of the proposed algorithm by showing that the root mean square errors (RMSEs) of the phase estimates are close to the CRBs.
Reconfigurable-Intelligent-Surface Assisted Orbital-Angular-Momentum Secure Communications
Authors: Minmin Wang, Liping Liang, Wenchi Cheng, Wei zhang, Ruirui Chen, Hailin Zhang
Date: 2024-07-16 00:00:00

As a kind of wavefront with helical phase, orbital angular momentum (OAM) shows the great potential to enhance the security results of wireless communications due to its unique orthogonality and central hollow electromagnetic wave structure. Therefore, in this paper we propose the reconfigurable-intelligent-surface (RIS) assisted OAM scheme, where RIS is deployed to weaken the information acquisition at eavesdroppers by adjusting the OAM beams pointed to the eavesdropper and artificial noise (AN) is applied to interfere with the eavesdropper, thus significantly increasing the secrecy rates of short-range secure communications. Aiming at obtaining the maximum secrecy rate, we develop the Riemannian manifold conjugate gradient (RMCG) based alternative optimization (AO) algorithm to assign much power to low-order OAM-modes and optimize the OAM beams direction with the programmable RIS, thus respectively enhancing and weakening the received signal strength at the legitimate receiver and the eavesdropper. Numerical results show that our proposed scheme outperforms the existing works in terms of the secrecy rate and the eavesdropper's bit error rate.
Fluid Antenna-Assisted Simultaneous Wireless Information and Power Transfer Systems
Authors: Liaoshi Zhou, Junteng Yao, Tuo Wu, Ming Jin, Chau Yuen, Fumiyuki Adachi
Date: 2024-07-16 00:00:00

This paper examines a fluid antenna (FA)-assisted simultaneous wireless information and power transfer (SWIPT) system. Unlike traditional SWIPT systems with fixed-position antennas (FPAs), our FA-assisted system enables dynamic reconfiguration of the radio propagation environment by adjusting the positions of FAs. This capability enhances both energy harvesting and communication performance. The system comprises a base station (BS) equipped with multiple FAs that transmit signals to an energy receiver (ER) and an information receiver (IR), both equipped with a single FA. Our objective is to maximize the communication rate between the BS and the IR while satisfying the harvested power requirement of the ER. This involves jointly optimizing the BS's transmit beamforming and the positions of all FAs. To address this complex convex optimization problem, we employ an alternating optimization (AO) approach, decomposing it into three sub-problems and solving them iteratively using first and second-order Taylor expansions. Simulation results validate the effectiveness of our proposed FA-assisted SWIPT system, demonstrating significant performance improvements over traditional FPA-based systems.
S-RAN: Semantic-Aware Radio Access Networks
Authors: Yao Sun, Lan Zhang, Linke Guo, Jian Li, Dusit Niyato, Yuguang Fang
Date: 2024-07-15 00:00:00
Tasks: Management, Semantic Communication

Semantic communication (SemCom) has been a transformative paradigm, emphasizing the precise exchange of meaningful information over traditional bit-level transmissions. However, existing SemCom research, primarily centered on simplified scenarios like single-pair transmissions with direct wireless links, faces significant challenges when applied to real-world radio access networks (RANs). This article introduces a Semantic-aware Radio Access Network (S-RAN), offering a holistic systematic view of SemCom beyond single-pair transmissions. We begin by outlining the S-RAN architecture, introducing new physical components and logical functions along with key design challenges. We then present transceiver design for end-to-end transmission to overcome conventional SemCom transceiver limitations, including static channel conditions, oversimplified background knowledge models, and hardware constraints. Later, we delve into the discussion on radio resource management for multiple users, covering semantic channel modeling, performance metrics, resource management algorithms, and a case study, to elaborate distinctions from resource management for legacy RANs. Finally, we highlight open research challenges and potential solutions. The objective of this article is to serve as a basis for advancing SemCom research into practical wireless systems.
Performance analysis for a rotary compressor at high speed: experimental study and mathematical modeling
Authors: Chuntai Zheng, Wei Zhao, Benshuai Lyu, Keke Gao, Hongjun Cao, Lei Zhong, Yi Gao, Ren Liao
Date: 2024-07-13 00:00:00

This paper conducted a comprehensive study on the performance of a rotary compressor over a rotational speed range of 80Hz to 200Hz through experimental tests and mathematical modeling. A compressor performance test rig was designed to conduct the performance tests, with fast-response pressure sensors and displacement sensors capturing the P-V diagram and dynamic motion of the moving components. Results show that the compressor efficiency degrades at high speeds due to the dominant loss factors of leakage and discharge power loss. Supercharging effects become significant at speeds above 160Hz, and its net effects reduce the compressor efficiency, especially at high speeds. This study identifies and analyzes the loss factors on the mass flow rate and power consumption based on experimental data, and hypothesizes possible mechanisms for each loss factor, which can aid in the design of a high-speed rotary compressor with higher efficiency.
ECG Signal Denoising Using Multi-scale Patch Embedding and Transformers
Authors: Ding Zhu, Vishnu Kabir Chhabra, Mohammad Mahdi Khalili
Date: 2024-07-12 00:00:00
Tasks: Denoising

Cardiovascular disease is a major life-threatening condition that is commonly monitored using electrocardiogram (ECG) signals. However, these signals are often contaminated by various types of noise at different intensities, significantly interfering with downstream tasks. Therefore, denoising ECG signals and increasing the signal-to-noise ratio is crucial for cardiovascular monitoring. In this paper, we propose a deep learning method that combines a one-dimensional convolutional layer with transformer architecture for denoising ECG signals. The convolutional layer processes the ECG signal by various kernel/patch sizes and generates an embedding called multi-scale patch embedding. The embedding then is used as the input of a transformer network and enhances the capability of the transformer for denoising the ECG signal.
Design Guidelines and Applications for Dual-Band Rat-Race Couplers and Gysel Power Dividers with Unequal Amplitude Imbalances
Authors: Rakesh Sinha
Date: 2024-07-11 00:00:00

This paper provides the design guidelines for a dual-band rat-race coupler and Gysel power divider with two different amplitude imbalances. The dual-band conditions are established based on the electrical nature of the coupler and dual-band C-section coupled lines/ T-structure/ $\Pi$-structures instead of using the dual-band equivalent transmission lines. It has been shown here that the same design equations can be utilized in the design of the rat-race coupler and Gysel power divider. The design equations are solved numerically to obtain the electrical parameters of the proposed coupler and power divider. The possible application of dual-band different power division ratios is presented for dual-band polarization controlled by a patch antenna. A design tool has been developed to obtain the coupler and power divider's design parameters and circuit analysis results. A design example of the coupler has been provided to validate the proposed concept.
Advancements in Signal Processing and Control Systems Using Z and L-Transforms
Authors: A. Jeeva, Vijayabalan D, Maria Singaraj Rosary, Nasir Ali, Fikadu Tesgera Tolasa
Date: 2024-07-11 00:00:00

To excel in signal processing or control systems, a deep understanding of transforms is essential. But what exactly is this mathematical tool, and how does it function? In this article, we will explore the fundamentals of transforms, their properties, and their applications. Let's dive into one of the key concepts of modern signal processing. Additionally, we will discuss Z-transforms and integrated Z-transforms, highlighting their roles in signal processing. Moreover, to demonstrate the practical application of transforms, we will introduce a novel concept involving fuzzy random variables.
Invariants for level-1 phylogenetic networks under the random walk 4-state Markov model
Authors: M. Frohn, N. Holtgrefe, L. van Iersel, M. Jones, S. Kelk
Date: 2024-07-16 00:00:00

Phylogenetic networks can represent evolutionary events that cannot be described by phylogenetic trees, such as hybridization, introgression, and lateral gene transfer. Studying phylogenetic networks under a statistical model of DNA sequence evolution can aid the inference of phylogenetic networks. Most notably Markov models like the Jukes-Cantor or Kimura-3 model can been employed to infer a phylogenetic network using phylogenetic invariants. In this article we determine all quadratic invariants for sunlet networks under the random walk 4-state Markov model, which includes the aforementioned models. Taking toric fiber products of trees and sunlet networks, we obtain a new class of invariants for level-1 phylogenetic networks under the same model. Furthermore, we apply our results to the identifiability problem of a network parameter. In particular, we prove that our new class of invariants of the studied model is not sufficient to derive identifiability of quarnets (4-leaf networks). Moreover, we provide an efficient method that is faster and more reliable than the state-of-the-art in finding a significant number of invariants for many level-1 phylogenetic networks.
Infinitesimal Homeostasis in Mass-Action Systems
Authors: Jiaxin Jin, Grzegorz A. Rempala
Date: 2024-07-15 00:00:00

Homeostasis occurs in a biological system when a chosen output variable remains approximately constant despite changes in an input variable. In this work we specifically focus on biological systems which may be represented as chemical reaction networks and consider their infinitesimal homeostasis, where the derivative of the input-output function is zero. The specific challenge of chemical reaction networks is that they often obey various conservation laws complicating the standard input-output analysis. We derive several results that allow to verify the existence of infinitesimal homeostasis points both in the absence of conservation and under conservation laws where conserved quantities serve as input parameters. In particular, we introduce the notion of infinitesimal concentration robustness, where the output variable remains nearly constant despite fluctuations in the conserved quantities. We provide several examples of chemical networks which illustrate our results both in deterministic and stochastic settings.