pwc-archive/papers-with-abstracts huggingface dataset:
Spatial, Temporal, and Geometric Fusion for Remote Sensing Images
Authors: Hessah Albanwan
Date: 2024-04-27 00:00:00
Tasks: Stereo Matching, Time Series Analysis

Remote sensing (RS) images are important to monitor and survey earth at varying spatial scales. Continuous observations from various RS sources complement single observations to improve applications. Fusion into single or multiple images provides more informative, accurate, complete, and coherent data. Studies intensively investigated spatial-temporal fusion for specific applications like pan-sharpening and spatial-temporal fusion for time-series analysis. Fusion methods can process different images, modalities, and tasks and are expected to be robust and adaptive to various types of images (e.g., spectral images, classification maps, and elevation maps) and scene complexities. This work presents solutions to improve existing fusion methods that process gridded data and consider their type-specific uncertainties. The contributions include: 1) A spatial-temporal filter that addresses spectral heterogeneity of multitemporal images. 2) 3D iterative spatiotemporal filter that enhances spatiotemporal inconsistencies of classification maps. 3) Adaptive semantic-guided fusion that enhances the accuracy of DSMs and compares them with traditional fusion approaches to show the significance of adaptive methods. 4) A comprehensive analysis of DL stereo matching methods against traditional Census-SGM to obtain detailed knowledge on the accuracy of the DSMs at the stereo matching level. We analyze the overall performance, robustness, and generalization capability, which helps identify the limitations of current DSM generation methods. 5) Based on previous analysis, we develop a novel finetuning strategy to enhance transferability of DL stereo matching methods, hence, the accuracy of DSMs. Our work shows the importance of spatial, temporal, and geometric fusion in enhancing RS applications. It shows that the fusion problem is case-specific and depends on the image type, scene content, and application.
Remote Sensing Image Enhancement through Spatiotemporal Filtering
Authors: Hessah Albanwan
Date: 2024-04-27 00:00:00
Tasks: Image Enhancement, Transfer Learning

The analysis of time-sequence satellite images is a powerful tool in remote sensing; it is used to explore the statics and dynamics of the surface of the earth. Usually, the quality of multitemporal images is influenced by metrological conditions, high reflectance of surfaces, illumination, and satellite sensor conditions. These negative influences may produce noises and different radiances and appearances between the images, which can affect the applications that process them. Thus, a spatiotemporal bilateral filter has been adopted in this research to enhance the quality of an image before using it in any application. The filter takes advantage of the temporal information provided by multi temporal images and attempts to reduce the differences between them to improve transfer learning used in classification. The classification method used here is support vector machine (SVM). Three experiments were conducted in this research, two were on Landsat 8 images with low-medium resolution, and the third on high-resolution images of Planet satellite. The newly developed filter proved that it can enhance the accuracy of classification using transfer learning by about 5%,15%, and 2% for the three experiments respectively.
A Fully Screen-Printed Vanadium-Dioxide Switches Based Wideband Reconfigurable Intelligent Surface for 5G Bands
Authors: Yiming Yang, Mohammad Vaseem, Ruiqi Wang, Behrooz Makki, Atif Shamim
Date: 2024-04-30 00:00:00

Reconfigurable Intelligent Surface (RIS) is attracting more and more research interest because of its ability to reprogram the radio environment. Designing and implementing the RIS, however, is challenging because of limitations of printed circuit board (PCB) technology related to manufacturing of large sizes as well as the cost of switches. Thus, a low-cost manufacturing process suitable for large size and volume of devices, such as screen-printing is necessary. In this paper, for the first time, a fully screen-printed reconfigurable intelligent surface (RIS) with vanadium dioxide (VO2) switches for 5G and beyond communications is proposed. A VO2 ink has been prepared and batches of switches have been printed and integrated with the resonator elements. These switches are a fraction of the cost of commercial switches. Furthermore, the printing of these switches directly on metal patterns negates the need of any minute soldering of the switches. To avoid the complications of multilayer printing and realizing the RIS without vias, the resonators and the biasing lines are realized on a single layer. However, this introduces the challenge of interference between the biasing lines and the resonators, which is tackled in this work by designing the bias lines as part of the resonator. By adjusting the unit cell periodicity and the dimension of the H-shaped resonator, we achieve a 220 to 170{\deg} phase shift from 23.5 GHz to 29.5 GHz covering both n257 and n258 bands. Inside the wide bandwidth, the maximum ON reflection magnitude is 74%, and the maximum OFF magnitude is 94%. The RIS array comprises 20x20 unit cells (4.54x4.54{\lambda}^2 at 29.5 GHz). Each column of unit cells is serially connected to a current biasing circuit. To validate the array's performance, we conduct full-wave simulations as well as near-field and far-field measurements.
Evaluation of An Indoor Localization Engine
Authors: Christophe Villien, Anne Frassati, Bruno Flament
Date: 2024-04-30 00:00:00
Tasks: Indoor Localization, Position

Pedestrian Indoor localization based on modalities available in modern smartphones have been widely studied in literature and many of the specific challenges have been addressed. However, very few approaches consider the whole problem and proposed solutions are very often evaluated under very limited scenarios. We propose a fusion engine for localization that makes use of various data provided by a smartphone (Inertial sensors, pressure sensors, Wi-Fi, BLE, GNSS, map etc.) to provide a fused localization that is robust under harsh conditions (poor RSS coverage, device position change etc.). Moreover, our solution has been evaluated for hardware integration and tested over a large database including more than 250 experiments representing different scenarios, showing feasibility of lightweight implementation and good results over various conditions.
Hybrid Bit and Semantic Communications
Authors: Kaiwen Yu, Renhe Fan, Gang Wu, Zhijin Qin
Date: 2024-04-30 00:00:00
Tasks: Semantic Communication

Semantic communication technology is regarded as a method surpassing the Shannon limit of bit transmission, capable of effectively enhancing transmission efficiency. However, current approaches that directly map content to transmission symbols are challenging to deploy in practice, imposing significant limitations on the development of semantic communication. To address this challenge, we propose a hybrid bit and semantic communication system, named HybridBSC, in which encoded semantic information is inserted into bit information for transmission via conventional digital communication systems utilizing same spectrum resources. The system can be easily deployed using existing communication architecture to achieve bit and semantic information transmission. Particularly, we design a semantic insertion and extraction scheme to implement this strategy. Furthermore, we conduct experimental validation based on the pluto-based software defined radio (SDR) platform in a real wireless channel, demonstrating that the proposed strategy can simultaneously transmit semantic and bit information.
Deep SIMO Auto-Encoder and Radio Frequency Hardware Impairments Modeling for Physical Layer Security
Authors: Abdullahi Mohammad, Mahmoud Tukur Kabir, Mikko Valkama, Bo Tan
Date: 2024-04-30 00:00:00
Tasks: Decoder

This paper presents a novel approach to achieving secure wireless communication by leveraging the inherent characteristics of wireless channels through end-to-end learning using a single-input-multiple-output (SIMO) autoencoder (AE). To ensure a more realistic signal transmission, we derive the signal model that captures all radio frequency (RF) hardware impairments to provide reliable and secure communication. Performance evaluations against traditional linear decoders, such as zero-forcing (ZR) and linear minimum mean square error (LMMSE), and the optimal nonlinear decoder, maximum likelihood (ML), demonstrate that the AE-based SIMO model exhibits superior bit error rate (BER) performance, but with a substantial gap even in the presence of RF hardware impairments. Additionally, the proposed model offers enhanced security features, preventing potential eavesdroppers from intercepting transmitted information and leveraging RF impairments for augmented physical layer security and device identification. These findings underscore the efficacy of the proposed end-to-end learning approach in achieving secure and robust wireless communication.
Dual-Functional Waveform Design with Local Sidelobe Suppression via OTFS Signaling
Authors: Kecheng Zhang, Weijie Yuan, Pingzhi Fan, Xianbin Wang
Date: 2024-04-27 00:00:00
Tasks: Integrated sensing and communication, ISAC

Integrated sensing and communication (ISAC) is viewed as a key technology in future wireless networks. One of the main challenges in realizing ISAC is developing dual-functional waveforms that can communicate with communication receivers and perform radar sensing simultaneously. In this paper, we consider the joint design of a dual-functional orthogonal time-frequency space (OTFS) signal and a receiving filter for the ISAC system. The problem of ISAC waveform design is formulated as the minimization of the weighted integrated sidelobe level (WISL) of the ambiguity function and the interference term from ISAC waveform, with constraints on signal-to-noise ratio loss. The majorization-minimization algorithm combined with alternating iterative minimization is implemented to solve the optimization problem. Simulation results show that the WISL and the interference term can be significantly decreased to guarantee achievable data rates and detection performance.
Channel Performance Metrics and Evaluation for XR Head-Mounted Displays with mmWave Arrays
Authors: Alexander Marinek, Xuesong Cai, Lieven De Strycker, Fredrik Tufvesson, Liesbet Van der Perre
Date: 2024-04-30 00:00:00

Millimeter-wave (mmWave) technology holds the potential to revolutionize head-mounted displays (HMDs) by enabling high-speed wireless communication with nearby processing nodes, where complex video rendering can take place. However, the sparse angular profile of mmWave channels, coupled with the narrow field of view (FoV) of patch-antenna arrays and frequent HMD rotation, can lead to poor performance. We introduce six channel performance metrics to evaluate the performance of an HMD equipped with mmWave arrays. We analyze the metrics using analytical models, discuss their impact for the application, and apply them to 28 GHz channel sounding data, collected in a conference room using eight HMD patch-antenna arrays, offset by 45 degrees from each other in azimuth. Our findings confirm that a single array performs poorly due to the narrow FoV, and featuring multiple arrays along the HMD's azimuth is required. Namely, the broader FoV stabilizes channel gain during HMD rotation, lessens the attenuation caused by line of sight (LoS) obstruction, and increases the channel's spatial multiplexing capability. In light of our findings, we conclude that it is imperative to either equip the HMD with multiple arrays or, as an alternative approach, incorporate macroscopic diversity by leveraging distributed access point (AP) infrastructure.
Robust Proximity Detection using On-Device Gait Monitoring
Authors: Yuqian Hu, Guozhen Zhu, Beibei Wang, K. J. Ray Liu
Date: 2024-04-30 00:00:00

Proximity detection in indoor environments based on WiFi signals has gained significant attention in recent years. Existing works rely on the dynamic signal reflections and their extracted features are dependent on motion strength. To address this issue, we design a robust WiFi-based proximity detector by considering gait monitoring. Specifically, we propose a gait score that accurately evaluates gait presence by leveraging the speed estimated from the autocorrelation function (ACF) of channel state information (CSI). By combining this gait score with a proximity feature, our approach effectively distinguishes different transition patterns, enabling more reliable proximity detection. In addition, to enhance the stability of the detection process, we employ a state machine and extract temporal information, ensuring continuous proximity detection even during subtle movements. Extensive experiments conducted in different environments demonstrate an overall detection rate of 92.5% and a low false alarm rate of 1.12% with a delay of 0.825s.
Location-Based Load Balancing for Energy-Efficient Cell-Free Networks
Authors: Robbert Beerten, Vida Ranjbar, Andrea P. Guevara, Hazem Sallouha, Sofie Pollin
Date: 2024-04-29 00:00:00

Cell-Free Massive MIMO (CF mMIMO) has emerged as a potential enabler for future networks. It has been shown that these networks are much more energy-efficient than classical cellular systems when they are serving users at peak capacity. However, these CF mMIMO networks are designed for peak traffic loads, and when this is not the case, they are significantly over-dimensioned and not at all energy efficient. To this end, Adaptive Access Point (AP) ON/OFF Switching (ASO) strategies have been developed to save energy when the network is not at peak traffic loads by putting unnecessary APs to sleep. Unfortunately, the existing strategies rely on measuring channel state information between every user and every access point, resulting in significant measurement energy consumption overheads. Furthermore, the current state-of-art approach has a computational complexity that scales exponentially with the number of APs. In this work, we present a novel convex feasibility testing method that allows checking per-user Quality-of-Service (QoS) requirements without necessarily considering all possible access point activations. We then propose an iterative algorithm for activating access points until all users' requirements are fulfilled. We show that our method has comparable performance to the optimal solution whilst avoiding solving costly mixed-integer problems and measuring channel state information on only a limited subset of APs.
Enhancing RSS-Based Visible Light Positioning by Optimal Calibrating the LED Tilt and Gain
Authors: Fan Wu, Nobby Stevens, Lieven De Strycker, François Rottenberg
Date: 2024-04-29 00:00:00
Tasks: Gaussian Processes

This paper presents an optimal calibration scheme and a weighted least squares (LS) localization algorithm for received signal strength (RSS) based visible light positioning (VLP) systems, focusing on the often overlooked impact of light emitting diode (LED) tilt. By optimally calibrating LED tilt and gain, we significantly enhance VLP localization accuracy. Our algorithm outperforms both machine learning Gaussian processes (GPs) and traditional multilateration techniques. Against GPs, it achieves improvements of 58% and 74% in the 50th and 99th percentiles, respectively. When compared to multilateration, it reduces the 50th percentile error from 7.4 cm to 3.2 cm and the 99th percentile error from 25.7 cm to 11 cm. We introduce a low-complexity estimator for tilt and gain that meets the Cramer-Rao lower bound (CRLB) for the mean squared error (MSE), emphasizing its precision and efficiency. Further, we elaborate on optimal calibration measurement placement and refine the observation model to include residual calibration errors, thereby improving localization performance. The weighted LS algorithm's effectiveness is validated through simulations and real-world data, consistently outperforming GPs and multilateration, across various training set sizes and reducing outlier errors. Our findings underscore the critical role of LED tilt calibration in advancing VLP system accuracy and contribute to a more precise model for indoor positioning technologies.
Fast Beam Training and Performance Analysis for Extremely Large Aperture Array
Authors: Yuan Si, Hongkang Yu, Yijian Chen
Date: 2024-04-28 00:00:00

Extremely large aperture array (ELAA) can significantly enhance beamforming gain and spectral efficiency. Unfortunately, the use of narrower beams for data transmission results in a substantial increase in the cost of beam training. In this paper, we study a high-efficiency and low-overhead scheme named hash beam training. Specifically, two improved hash codebook design methods, random and fixed, are proposed. Moreover, we analyze beam alignment performance. Since the derived beam alignment success probability is a complex function, we also propose a heuristic metric to evaluate the impact of codebook parameter on performance. Finally, simulation results validate the theoretical analysis, indicating that the proposed beam training scheme can achieve fast beam alignment with lower overhead and higher accuracy.
Energy-Efficient Federated Learning in Cooperative Communication within Factory Subnetworks
Authors: Hamid Reza Hashempour, Gilberto Berardinelli, Ramoni Adeogun, Shashi Raj Pandey
Date: 2024-04-27 00:00:00
Tasks: Federated Learning

This paper investigates energy-efficient transmission protocols in relay-assisted federated learning (FL) setup within industrial subnetworks, considering latency and power constraints. In the subnetworks, devices collaborate to train a global model by transmitting their local models at the edge-enabled primary access (pAP) directly or via secondary access points (sAPs), which act as relays to optimize the training latency. We begin by formulating the energy efficiency problem for our proposed transmission protocol. Given its non-convex nature, we decompose it to minimize computational and transmission energy separately. First, we introduce an algorithm that categorizes devices into single-hop and two-hop groups to decrease transmission energy and then selects associated sAPs. Subsequently, we optimize the transmit power, aiming to maximize energy efficiency. To that end, we propose a Sequential Parametric Convex Approximation (SPCA) method to configure system parameters jointly. Simulation results show a 5% improvement in convergence, significantly reduced outage, and at least a twofold savings in total energy achieved by our proposed algorithm compared to single-hop transmission.
3D Extended Object Tracking by Fusing Roadside Sparse Radar Point Clouds and Pixel Keypoints
Authors: Jiayin Deng, Zhiqun Hu, Yuxuan Xia, Zhaoming Lu, Xiangming Wen
Date: 2024-04-27 00:00:00
Tasks: Object Tracking

Roadside perception is a key component in intelligent transportation systems. In this paper, we present a novel three-dimensional (3D) extended object tracking (EOT) method, which simultaneously estimates the object kinematics and extent state, in roadside perception using both the radar and camera data. Because of the influence of sensor viewing angle and limited angle resolution, radar measurements from objects are sparse and non-uniformly distributed, leading to inaccuracies in object extent and position estimation. To address this problem, we present a novel spherical Gaussian function weighted Gaussian mixture model. This model assumes that radar measurements originate from a series of probabilistic weighted radar reflectors on the vehicle's extent. Additionally, we utilize visual detection of vehicle keypoints to provide additional information on the positions of radar reflectors. Since keypoints may not always correspond to radar reflectors, we propose an elastic skeleton fusion mechanism, which constructs a virtual force to establish the relationship between the radar reflectors on the vehicle and its extent. Furthermore, to better describe the kinematic state of the vehicle and constrain its extent state, we develop a new 3D constant turn rate and velocity motion model, considering the complex 3D motion of the vehicle relative to the roadside sensor. Finally, we apply variational Bayesian approximation to the intractable measurement update step to enable recursive Bayesian estimation of the object's state. Simulation results using the Carla simulator and experimental results on the nuScenes dataset demonstrate the effectiveness and superiority of the proposed method in comparison to several state-of-the-art 3D EOT methods.
Performance Analysis for Downlink Transmission in Multi-Connectivity Cellular V2X Networks
Authors: Luofang Jiao, Jiwei Zhao, Yunting Xu, Tianqi Zhang, Haibo Zhou, Dongmei Zhao
Date: 2024-04-27 00:00:00
Tasks: Point Processes

With the ever-increasing number of connected vehicles in the fifth-generation mobile communication networks (5G) and beyond 5G (B5G), ensuring the reliability and high-speed demand of cellular vehicle-to-everything (C-V2X) communication in scenarios where vehicles are moving at high speeds poses a significant challenge.Recently, multi-connectivity technology has become a promising network access paradigm for improving network performance and reliability for C-V2X in the 5G and B5G era. To this end, this paper proposes an analytical framework for the performance of downlink in multi-connectivity C-V2X networks. Specifically, by modeling the vehicles and base stations as one-dimensional Poisson point processes, we first derive and analyze the joint distance distribution of multi-connectivity. Then through leveraging the tools of stochastic geometry, the coverage probability and spectral efficiency are obtained based on the previous results for general multi-connectivity cases in C-V2X. Additionally, we evaluate the effect of path loss exponent and the density of downlink base station on system performance indicators. We demonstrate through extensive Monte Carlo simulations that multi-connectivity technology can effectively enhance network performance in C-V2X. Our findings have important implications for the research and application of multi-connectivity C-V2X in the 5G and B5G era.
P53 Orchestrates Cancer Metabolism: Unveiling Strategies to Reverse the Warburg Effect
Authors: Roba Abukwaik, Elias Vera-Siguenza, Daniel Tennant, Fabian Spill
Date: 2024-04-29 00:00:00

Cancer cells exhibit significant alterations in their metabolism, characterised by a reduction in oxidative phosphorylation (OXPHOS) and an increased reliance on glycolysis, even in the presence of oxygen. This metabolic shift, known as the Warburg effect, is pivotal in fuelling cancer's uncontrolled growth, invasion, and therapeutic resistance. While dysregulation of many genes contributes to this metabolic shift, the tumour suppressor gene p53 emerges as a master player. Yet, the molecular mechanisms remain elusive. This study introduces a comprehensive mathematical model, integrating essential p53 targets, offering insights into how p53 orchestrates its targets to redirect cancer metabolism towards an OXPHOS-dominant state. Simulation outcomes align closely with experimental data comparing glucose metabolism in colon cancer cells with wild-type and mutated p53. Additionally, our findings reveal the dynamic capability of elevated p53 activation to fully reverse the Warburg effect, highlighting the significance of its activity levels not just in triggering apoptosis (programmed cell death) post-chemotherapy but also in modifying the metabolic pathways implicated in treatment resistance. In scenarios of p53 mutations, our analysis suggests targeting glycolysis-instigating signalling pathways as an alternative strategy, whereas targeting solely synthesis of cytochrome c oxidase 2 (SCO2) does support mitochondrial respiration but may not effectively suppress the glycolysis pathway, potentially boosting the energy production and cancer cell viability.