pwc-archive/papers-with-abstracts huggingface dataset:
How good nnU-Net for Segmenting Cardiac MRI: A Comprehensive Evaluation
Authors: Malitha Gunawardhana, Fangqiang Xu, Jichao Zhao
Date: 2024-07-26 00:00:00
Tasks: Cardiac Segmentation, Image Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation

Cardiac segmentation is a critical task in medical imaging, essential for detailed analysis of heart structures, which is crucial for diagnosing and treating various cardiovascular diseases. With the advent of deep learning, automated segmentation techniques have demonstrated remarkable progress, achieving high accuracy and efficiency compared to traditional manual methods. Among these techniques, the nnU-Net framework stands out as a robust and versatile tool for medical image segmentation. In this study, we evaluate the performance of nnU-Net in segmenting cardiac magnetic resonance images (MRIs). Utilizing five cardiac segmentation datasets, we employ various nnU-Net configurations, including 2D, 3D full resolution, 3D low resolution, 3D cascade, and ensemble models. Our study benchmarks the capabilities of these configurations and examines the necessity of developing new models for specific cardiac segmentation tasks.
Online ML-based Joint Channel Estimation and MIMO Decoding for Dynamic Channels
Authors: Luiz Fernando Moreira Teixeira, Vinicius Henrique Luiz, Jonathan Aguiar Soares, Kayol Soares Mayer, Dalton Soares Arantes
Date: 2024-08-15 00:00:00

This paper presents an online method for joint channel estimation and decoding in massive MIMO-OFDM systems using complex-valued neural networks (CVNNs). The study evaluates the performance of various CVNNs, such as the complex-valued feedforward neural network (CVFNN), split-complex feedforward neural network (SCFNN), complex radial basis function (C-RBF), fully-complex radial basis function (FC-RBF) and phase-transmittance radial basis function (PT-RBF), in realistic 5G communication scenarios. Results demonstrate improvements in mean squared error (MSE), convergence, and bit error rate (BER) accuracy. The C-RBF and PT-RBF architectures show the most promising outcomes, suggesting that RBF-based CVNNs provide a reliable and efficient solution for complex and noisy communication environments. These findings have potential implications for applying advanced neural network techniques in next-generation wireless systems.
On the Spectral Efficiency of Movable and Rotary Antenna Arrays under Rician Fading
Authors: Eduardo Noboro Tominaga, Onel Luis Alcaraz López, Tommy Svensson, Richard Demo Souza, Hirley Alves
Date: 2024-08-15 00:00:00

Most works evaluating the performance of Multi-User Multiple-Input Multiple-Output (MU-MIMO) systems consider Access Points (APs) with fixed antennas, that is, without any movement capability. Recently, the idea of APs with antenna arrays that are able to move have gained traction among the research community. Many works evaluate the communications performance of Movable Antenna Arrays (MAAs) that can move on the horizontal plane. However, they require a very bulky, complex and expensive movement system. In this work, we propose a simpler and cheaper alternative: the utilization of Rotary Antenna Arrays (RAA)s, i.e. antenna arrays that can rotate. We also analyze the performance of a system in which the array is able to both move and rotate. The movements and/or rotations of the array are computed in order to maximize the mean per-user achievable spectral efficiency, based on estimates of the locations of the active devices and using particle swarm optimization. We adopt a spatially correlated Rician fading channel model, and evaluate the resulting optimized performance of the different setups in terms of mean per-user achievable spectral efficiencies. Our numerical results show that both the optimal rotations and movements of the arrays can provide substantial performance gains when the line-of-sight components of the channel vectors are strong. Moreover, the simpler RAAs can outperform the MAAs when their movement area is constrained.
RIS-Aided Bistatic Radar for Rapid NLOS Sensing in the Teraharetz Band
Authors: Furkan H. Ilgac, Emrah Cisija, Aya Mostafa Ahmed, Musa Furkan Keskin, Aydin Sezgin, Henk Wymeersch
Date: 2024-08-15 00:00:00

In this paper, we investigate a non-lineof-sight (NLOS) sensing problem at terahertz frequencies. To be able to observe the targets shadowed by a blockage, we propose a method using reconfigurable intelligent surfaces (RIS). We employ a bistatic radar system and scan the obstructed area with RIS using hierarchical codebooks (HCB). Moreover, we propose an iterative maximum likelihood estimation (MLE) scheme to yield the optimum sensing accuracy, converging to Cramer-Rao lower bound (CRLB). We take band-specific effects such as diffraction and beam squint into account and show that these effects are relevant factors affecting localization performance in RIS-employed radar setups. The results show that under NLOS conditions, the system can still localize all the targets with very good accuracy using the RIS. The initial estimates obtained by the HCBs can provide centimeter-level accuracy, and when the optimal performance is needed, at the cost of a few extra transmissions, the proposed iterative MLE method improves the accuracy to sub-millimeter accuracy, yielding the position error bound.
Optimal Joint Fronthaul Compression and Beamforming Design for Networked ISAC Systems
Authors: Kexin Zhang, Yanqing Xu, Ruisi He, Chao Shen, Tsung-Hui Chang
Date: 2024-08-15 00:00:00
Tasks: Integrated sensing and communication, ISAC

This study investigates a networked integrated sensing and communication (ISAC) system, where multiple base stations (BSs), connected to a central processor (CP) via capacity-limited fronthaul links, cooperatively serve communication users while simultaneously sensing a target. The primary objective is to minimize the total transmit power while meeting the signal-to-interference-plus-noise ratio (SINR) requirements for communication and sensing under fronthaul capacity constraints, resulting in a joint fronthaul compression and beamforming design (J-FCBD) problem. We demonstrate that the optimal fronthaul compression variables can be determined in closed form alongside the beamformers, a novel finding in this field. Leveraging this insight, we show that the remaining beamforming design problem can be solved globally using the semidefinite relaxation (SDR) technique, albeit with considerable complexity. Furthermore, the tightness of its SDR reveals zero duality gap between the considered problem and its Lagrangian dual. Building on this duality result, we exploit the novel UL-DL duality within the ISAC framework to develop an efficient primal-dual (PD)-based algorithm. The algorithm alternates between solving beamforming with a fixed dual variable via fixed-point iteration and updating dual variable via bisection, ensuring global optimality and achieving high efficiency due to the computationally inexpensive iterations. Numerical results confirm the global optimality, effectiveness, and efficiency of the proposed PD-based algorithm.
User-Centric Machine Learning for Resource Allocation in MPTCP-Enabled Hybrid LiFi and WiFi Networks
Authors: Han Ji, Declan T. Delaney, Xiping Wu
Date: 2024-08-14 00:00:00

As an emerging paradigm of heterogeneous networks (HetNets) towards 6G, the hybrid light fidelity (LiFi) and wireless fidelity (WiFi) networks (HLWNets) have potential to explore the complementary advantages of the optical and radio spectra. Like other cooperation-native HetNets, HLWNets face a crucial load balancing (LB) problem due to the heterogeneity of access points (APs). The existing literature mostly formulates this problem as joint AP selection and resource allocation (RA), presuming that each user equipment (UE) is served by one AP at a time, under the constraint of the traditional transmission control protocol (TCP). In contrast, multipath TCP (MPTCP), which allows for the simultaneous use of multiple APs, can significantly boost the UE's throughput as well as enhancing its network resilience. However, the existing TCP-based LB methods, particularly those aided by machine learning, are not suitable for the MPTCP scenario. In this paper, we discuss the challenges when developing learning-aided LB in MPTCP-enabled HLWNets, and propose a novel user-centric learning model to tackle this tricky problem. Unlike the conventional network-centric learning methods, the proposed method determines the LB solution for a single target UE, rendering low complexity and high flexibility in practical implementations. Results show that the proposed user-centric approach can greatly outperform the network-centric learning method. Against the TCP-based LB method such as game theory, the proposed method can increase the throughput of HLWNets by up to 40\%.
On the Parameter Selection of Phase-transmittance Radial Basis Function Neural Networks for Communication Systems
Authors: Jonathan A. Soares, Kayol S. Mayer, Dalton S. Arantes
Date: 2024-08-14 00:00:00
Tasks: Efficient Neural Network

In the ever-evolving field of digital communication systems, complex-valued neural networks (CVNNs) have become a cornerstone, delivering exceptional performance in tasks like equalization, channel estimation, beamforming, and decoding. Among the myriad of CVNN architectures, the phase-transmittance radial basis function neural network (PT-RBF) stands out, especially when operating in noisy environments such as 5G MIMO systems. Despite its capabilities, achieving convergence in multi-layered, multi-input, and multi-output PT-RBFs remains a daunting challenge. Addressing this gap, this paper presents a novel Deep PT-RBF parameter initialization technique. Through rigorous simulations conforming to 3GPP TS 38 standards, our method not only outperforms conventional initialization strategies like random, $K$-means, and constellation-based methods but is also the only approach to achieve successful convergence in deep PT-RBF architectures. These findings pave the way to more robust and efficient neural network deployments in complex digital communication systems.
Anisotropic Diffusion Model of Communication in 2D Biofilm
Authors: Yanahan Paramalingam, Hamidreza Arjmandi, Freya Harrison, Tara Schiller, Adam Noel
Date: 2024-08-14 00:00:00
Tasks: model

A biofilm is a microbial city. It consists of bacteria embedded in an extracellular polymeric substance (EPS) that functions as a protective barrier. Quorum sensing (QS) is a method of bacterial communication, where autoinducers (AIs) propagate via diffusion through the EPS and water channels within the biofilm. This diffusion process is anisotropic due to varying densities between the EPS and water channels. This study introduces a 2D anisotropic diffusion model for molecular communication (MC) within biofilms, analyzing information propagation between a point-to-point transmitter (TX) and receiver (RX) in bounded space. The channel impulse response is derived using Green's function for concentration (GFC) and is validated with particle-based simulation (PBS). The outcomes reveal similar results for both isotropic and anisotropic diffusion when the TX is centrally located due to symmetry. However, anisotropic conditions lead to greater diffusion peaks when the TX is positioned off-center. Additionally, the propagation of AIs is inversely proportional to both overall biofilm size and and diffusion coefficient values. It is hypothesized that anisotropic diffusion supports faster responses to hostile environmental changes because signals can propagate faster from the edge of the biofilm to the center.
Multi-periodicity dependency Transformer based on spectrum offset for radio frequency fingerprint identification
Authors: Jing Xiao, Wenrui Ding, Zeqi Shao, Duona Zhang, Yanan Ma, Yufeng Wang, Jian Wang
Date: 2024-08-14 00:00:00

Radio Frequency Fingerprint Identification (RFFI) has emerged as a pivotal task for reliable device authentication. Despite advancements in RFFI methods, background noise and intentional modulation features result in weak energy and subtle differences in the RFF features. These challenges diminish the capability of RFFI methods in feature representation, complicating the effective identification of device identities. This paper proposes a novel Multi-Periodicity Dependency Transformer (MPDFormer) to address these challenges. The MPDFormer employs a spectrum offset-based periodic embedding representation to augment the discrepency of intrinsic features. We delve into the intricacies of the periodicity-dependency attention mechanism, integrating both inter-period and intra-period attention mechanisms. This mechanism facilitates the extraction of both long and short-range periodicity-dependency features , accentuating the feature distinction whilst concurrently attenuating the perturbations caused by background noise and weak-periodicity features. Empirical results demonstrate MPDFormer's superiority over established baseline methods, achieving a 0.07s inference time on NVIDIA Jetson Orin NX.
Optimizing UAV Trajectory for Emergency Response Operations under Real 3D Environments: Integrating Priority Levels and LoS Constraints
Authors: Mohammad Taghi Dabiri, Mazen Hasna, Saud Althunibat, Khalid Qaraqe
Date: 2024-08-14 00:00:00

Unmanned Aerial Vehicles (UAVs) have emerged as a critical component in next-generation wireless networks, particularly for disaster recovery scenarios, due to their flexibility, mobility, and rapid deployment capabilities. This paper focuses on optimizing UAV trajectories to ensure effective communication in disaster-stricken areas using terahertz (THz) links. We address specific challenges such as energy consumption, user priority levels, and navigating complex urban environments to maintain Line of Sight (LoS) connections amidst 3D obstacles. Our contributions include the development of a detailed modeling approach using online 3D map data, the formulation of an optimal trajectory optimization problem, and the proposal of a Genetic Algorithm (GA)-based method alongside an enhanced heuristic algorithm for faster convergence. Through 3D simulations, we demonstrate the trade-off between minimizing total service time and prioritizing higher-weight nodes, showing the impact of different priority weight factors on the trajectory time. The proposed algorithms are evaluated using real-world data from the West Bay area of Doha, Qatar, demonstrating their effectiveness in optimizing UAV trajectories for emergency response.
Coordinated Spectral Efficiency Prediction for Real-World 5G CoMP Systems
Authors: Zhixing Chen, Zhaoyu Fan, Yang Li, Yibin Kang, Qi Yan, Qingjiang Shi
Date: 2024-08-14 00:00:00
Tasks: Diversity, Management, Prediction

Coordinated multipoint (CoMP) systems incur substantial resource consumption due to the management of backhaul links and the coordination among various base stations (BSs). Accurate prediction of coordinated spectral efficiency (CSE) can guide the optimization of network parameters, resulting in enhanced resource utilization efficiency. However, characterizing the CSE is intractable due to the inherent complexity of the CoMP channel model and the diversity of the 5G dynamic network environment, which poses a great challenge for CSE prediction in real-world 5G CoMP systems. To address this challenge, in this letter, we propose a data-driven model-assisted approach. Initially, we leverage domain knowledge to preprocess the collected raw data, thereby creating a well-informed dataset. Within this dataset, we explicitly define the target variable and the input feature space relevant to channel statistics for CSE prediction. Subsequently, a residual-based network model is built to capture the high-dimensional non-linear mapping function from the channel statistics to the CSE. The effectiveness of the proposed approach is validated by experimental results on real-world data.
Near-Field Localization with Antenna Arrays in the Presence of Direction-Dependent Mutual Coupling
Authors: Zohreh Ebadi, Amir Masoud Molaei, George C. Alexandropoulos, Muhammad Ali Babar Abbasi, Simon Cotton, Anvar Tukmanov, Okan Yurduseven
Date: 2024-08-13 00:00:00

Localizing near-field sources considering practical arrays is a recent challenging topic for next generation wireless communication systems. Practical antenna array apertures with closely spaced elements exhibit direction-dependent mutual coupling (MC), which can significantly degrade the performance localization techniques. A conventional method for near-field localization in the presence of MC is the three-dimensional (3D) multiple signal classification technique, which, however, suffers from extremely high computational complexity. Recently, two-dimensional (2D) search alternatives have been presented, exhibiting increased complexity still for direction-dependent MC scenarios. In this paper, we devise a low complexity one-dimensional (1D) iterative method based on an oblique projection operator (IMOP) that estimates direction-dependent MC and the locations of multiple near-field sources. The proposed method first estimates the initial direction of arrival (DOA) and MC using the approximate wavefront model, and then, estimates the initial range of one near-field source using the exact wavefront model. Afterwards, at each iteration, the oblique projection operator is used to isolate components associated with one source from those of other sources. The DOA and range of this one source are estimated using the exact wavefront model and 1D searches. Finally, the direction-dependent MC is estimated for each pair of the estimated DOA and range. The performance of the proposed near-field localization approach is comprehensively investigated and verified using both a full-wave electromagnetic solver and synthetic simulations. It is showcased that our IMOP scheme performs almost similarly to a state-of-the-art approach but with a 42 times less computational complexity.
Achieving Practical OAM Based Wireless Communications With Misaligned Transceiver
Authors: Wenchi Cheng, Haiyue Jing, Wei zhang, Zan Li, Hailin Zhang
Date: 2024-08-13 00:00:00

Orbital angular momentum (OAM) has attracted much attention for radio vortex wireless communications due to the orthogonality among different OAM-modes. To maintain the orthogonality among different OAM modes at the receiver, the strict alignment between transmit and receive antennas is highly demanded. However, it is not practical to guarantee the transceiver alignment in wireless communications. The phase turbulence, resulting from the misaligned transceivers, leads to serious inter-mode interference among different OAM modes and therefore fail for signals detection of multiple OAM modes at the receiver. To achieve practical OAM based wireless communications, in this paper we investigate the radio vortex wireless communications with misaligned transmit and receive antennas. We propose a joint Beamforming and Pre-detection (BePre) scheme, which uses two unitary matrices to convert the channel matrix into the equivalent circulant matrix for keeping the orthogonality among OAM-modes at the receiver. Then, the OAM signals can be detected with the mode-decomposition scheme at the misaligned receiver. Extensive simulations obtained validate and evaluate that our developed joint BePre scheme can efficiently detect the signals of multiple OAM-modes for the misaligned transceiver and can significantly increase the spectrum efficiency.
Orbital-Angular-Momentum Embedded Massive MIMO: Achieving Multiplicative Spectrum-Efficiency for mmWave Communications
Authors: Wenchi Cheng, Hailin Zhang, Liping Liang, Haiyue Jing, Zan Li
Date: 2024-08-13 00:00:00

By enabling very high bandwidth for radio communications, the millimeter-wave (mmWave), which can easily be integrated with massive-multiple-input-multiple-output (massive-MIMO) due to small antenna size, has been attracting growing attention as a candidate for the fifth-generation (5G) and 5G-beyond wireless communications networks. On the other hand, the communication over the orthogonal states/modes of orbital angular momentum (OAM) is a subset of the solutions offered by massive-MIMO communications. Traditional massive-MIMO based mmWave communications did not concern the potential spectrum-efficiency-gain (SE-gain) offered by orthogonal states of OAM. However, the highly expecting maximum SE-gain for OAM and massive-MIMO communications is the product of SE-gains offered by OAM and multiplexing-MIMO. In this paper, we propose the OAM-embedded-MIMO (OEM) communication framework to obtain the multiplicative SE-gain for joint OAM and massive-MIMO based mmWave wireless communications. We design the parabolic antenna for each uniform circular array antenna to converge OAM signals. Then, we develop the mode-decomposition and multiplexing-detection scheme to obtain the transmit signal on each OAM-mode of each transmit antenna. Also, we develop the OEM-water-filling power allocation policy to achieve the maximum multiplicative SE-gain for OEM communications. The extensive simulations obtained validate and evaluate our developed parabolic antenna based converging method, mode-decomposition and multiplexing-detection scheme, and OEM-water-filling policy, showing that our proposed OEM mmWave communications can significantly increase the spectrum-efficiency as compared with traditional massive-MIMO based mmWave communications.
Persistence kernels for classification: A comparative study
Authors: Cinzia Bandiziol, Stefano De Marchi
Date: 2024-08-09 00:00:00
Tasks: Classification

The aim of the present work is a comparative study of different persistence kernels applied to various classification problems. After some necessary preliminaries on homology and persistence diagrams, we introduce five different kernels that are then used to compare their performances of classification on various datasets. We also provide the Python codes for the reproducibility of results.
Pretrained-Guided Conditional Diffusion Models for Microbiome Data Analysis
Authors: Xinyuan Shi, Fangfang Zhu, Wenwen Min
Date: 2024-08-10 00:00:00
Tasks: Denoising, Imputation, Missing Values

Emerging evidence indicates that human cancers are intricately linked to human microbiomes, forming an inseparable connection. However, due to limited sample sizes and significant data loss during collection for various reasons, some machine learning methods have been proposed to address the issue of missing data. These methods have not fully utilized the known clinical information of patients to enhance the accuracy of data imputation. Therefore, we introduce mbVDiT, a novel pre-trained conditional diffusion model for microbiome data imputation and denoising, which uses the unmasked data and patient metadata as conditional guidance for imputating missing values. It is also uses VAE to integrate the the other public microbiome datasets to enhance model performance. The results on the microbiome datasets from three different cancer types demonstrate the performance of our methods in comparison with existing methods.
The gene function prediction challenge: large language models and knowledge graphs to the rescue
Authors: Rohan Shawn Sunil, Shan Chun Lim, Manoj Itharajula, Marek Mutwil
Date: 2024-08-13 00:00:00
Tasks: Knowledge Graphs, Prediction

Elucidating gene function is one of the ultimate goals of plant science. Despite this, only ~15% of all genes in the model plant Arabidopsis thaliana have comprehensively experimentally verified functions. While bioinformatical gene function prediction approaches can guide biologists in their experimental efforts, neither the performance of the gene function prediction methods nor the number of experimental characterisation of genes has increased dramatically in recent years. In this review, we will discuss the status quo and the trajectory of gene function elucidation and outline the recent advances in gene function prediction approaches. We will then discuss how recent artificial intelligence advances in large language models and knowledge graphs can be leveraged to accelerate gene function predictions and keep us updated with scientific literature.