pwc-archive/papers-with-abstracts huggingface dataset:
IoT Cloud RAN Testbed for Ultra-Precise TDoA-based Localization in LPWANs
Authors: Thomas Maul, Joerg Robert, Sebastian Klob
Date: 2024-01-21 00:00:00

There have been many research efforts in the area of localization in recent years. Especially within the Internet of Things (IoT), the knowledge of position information for individual components is of great interest, for example, in asset tracking, to name just one. However, many of these use cases require a high energy efficiency, making a GNSS-based approach infeasible. One promising candidate can be found in Low Power Wide Area Networks (LPWAN), which enable battery lifetimes of up to 20 years. However, no gold standard for localization exists for these types of networks. Our work proposes a testbed architecture that allows the investigation and development of localization algorithms within LPWA Networks. The concept is built on a Cloud Radio Access Network (CRAN) architecture that allows the streaming of IQ from remote base stations to a central processing unit. Furthermore, the architecture is expanded by a synchronization concept based on Signals of Opportunity (SoO) to enable the testbed for runtime-based positioning. Therefore, we propose a hardware concept consisting of antennas and a low-cost off-the-shelf software-defined radio (SDR)-based frontend architecture and a software framework using a hypertext transfer protocol (HTTP)-based server and client architecture. The proposed system is installed in an urban environment. Initial measurements are conducted, where it can be shown that the proposed architecture can be used for highly precise Time Difference of Arrival (TDoA) measurements, offering the possibility of time synchronization down to approximately 200 ps and frequency synchronization of 3 mHz.
Joint User Scheduling and Computing Resource Allocation Optimization in Asynchronous Mobile Edge Computing Networks
Authors: Yihan Cang, Ming Chen, Yijin Pan, Zhaohui Yang, Ye Hu, Haijian Sun, Mingzhe Chen
Date: 2024-01-21 00:00:00
Tasks: Edge-computing, Scheduling

In this paper, the problem of joint user scheduling and computing resource allocation in asynchronous mobile edge computing (MEC) networks is studied. In such networks, edge devices will offload their computational tasks to an MEC server, using the energy they harvest from this server. To get their tasks processed on time using the harvested energy, edge devices will strategically schedule their task offloading, and compete for the computational resource at the MEC server. Then, the MEC server will execute these tasks asynchronously based on the arrival of the tasks. This joint user scheduling, time and computation resource allocation problem is posed as an optimization framework whose goal is to find the optimal scheduling and allocation strategy that minimizes the energy consumption of these mobile computing tasks. To solve this mixed-integer non-linear programming problem, the general benders decomposition method is adopted which decomposes the original problem into a primal problem and a master problem. Specifically, the primal problem is related to computation resource and time slot allocation, of which the optimal closed-form solution is obtained. The master problem regarding discrete user scheduling variables is constructed by adding optimality cuts or feasibility cuts according to whether the primal problem is feasible, which is a standard mixed-integer linear programming problem and can be efficiently solved. By iteratively solving the primal problem and master problem, the optimal scheduling and resource allocation scheme is obtained. Simulation results demonstrate that the proposed asynchronous computing framework reduces 87.17% energy consumption compared with conventional synchronous computing counterpart.
Self-supervised Contrastive Learning for 6G UM-MIMO THz Communications: Improving Robustness Under Imperfect CSI
Authors: Rafid Umayer Murshed, Md Saheed Ullah, Mohammad Saquib, Moe Z. Win
Date: 2024-01-21 00:00:00
Tasks: Contrastive Learning

This paper investigates the potential of contrastive learning in 6G ultra-massive multiple-input multiple-output (UM-MIMO) communication systems, specifically focusing on hybrid beamforming under imperfect channel state information (CSI) conditions at THz. UM-MIMO systems are promising for future 6G wireless communication networks due to their high spectral efficiency and capacity. The accuracy of CSI significantly influences the performance of UM-MIMO systems. However, acquiring perfect CSI is challenging due to various practical constraints such as channel estimation errors, feedback delays, and hardware imperfections. To address this issue, we propose a novel self-supervised contrastive learning-based approach for hybrid beamforming, which is robust against imperfect CSI. We demonstrate the power of contrastive learning to tackle the challenges posed by imperfect CSI and show that our proposed method results in improved system performance in terms of achievable rate compared to traditional methods.
A Fast Effective Greedy Approach for MU-MIMO Beam Selection in mm-Wave and THz Communications
Authors: Rafid Umayer Murshed, Md Saheed Ullah, Mohammad Saquib
Date: 2024-01-21 00:00:00
Tasks: Computational Efficiency

This paper addresses the beam-selection challenges in Multi-User Multiple Input Multiple Output (MU-MIMO) beamforming for mm-wave and THz channels, focusing on the pivotal aspect of spectral efficiency (SE) and computational efficiency. We introduce a novel approach, the Greedy Interference-Optimized Singular Vector Beam-selection (G-IOSVB) algorithm, which offers a strategic balance between high SE and low computational complexity. Our study embarks on a comparative analysis of G-IOSVB against the traditional IOSVB and the exhaustive Singular-Vector Beamspace Search (SVBS) algorithms. The findings reveal that while SVBS achieves the highest SE, it incurs significant computational costs, approximately 162 seconds per channel realization. In contrast, G-IOSVB aligns closely with IOSVB in SE performance yet is markedly more computationally efficient. Heatmaps vividly demonstrate this efficiency, highlighting G-IOSVB's reduced computation time without sacrificing SE. We also delve into the mathematical intricacies of G-IOSVB, demonstrating its theoretical and practical superiority through rigorous expressions and detailed algorithmic analysis. The numerical results illustrate that G-IOSVB stands out as an efficient, practical solution for MU-MIMO systems, making it a promising candidate for high-speed, high-efficiency wireless communication networks.
Reconfigurable Intelligent Surface-Enabled Array Radar for Interference Mitigation
Authors: Shengyao Chen, Qi Feng, Longyao Ran, Feng Xi, Zhong Liu
Date: 2024-01-20 00:00:00

Conventional active array radars often jointly design the transmit and receive beamforming for effectively suppressing interferences. To further promote the interference suppression performance, this paper introduces a reconfigurable intelligent surface (RIS) to assist the radar receiver because the RIS has the ability to bring plentiful additional degrees-of-freedom. To maximize the output signal-to-interference-plus-noise ratio (SINR) of receive array, we formulate the codesign of transmit beamforming and RIS-assisted receive beamforming into a nonconvex constrained fractional programming problem, and then propose an alternating minimization-based algorithm to jointly optimize the transmitor beamfmer, receive beamformer and RIS reflection coefficients. Concretely, we translate the RIS reflection coefficients design into a series of unimodular quadratic programming (UQP) subproblems by employing the Dinkelbach transform, and offer the closed-form optimal solutions of transmit and receive beamformers according to the minimum variance distortionless response principle. To tackle the UQP subproblems efficiently, we propose a second-order Riemannian Newton method (RNM) with improved Riemannian Newton direction, which avoids the line search and has better convergence speed than typical first-order Riemannian manifold optimization methods. Moreover, we derive the convergence of the proposed codesign algorithm by deducing the explicit convergence condition of RNM. We also analyze the computational complexity. Numerical results demonstrate that the proposed RIS-assisted array radar has superior performance of interference suppression to the RIS-free one, and the SINR improvement is proportional to the number of RIS elements.
Federated Learning with Dual Attention for Robust Modulation Classification under Attacks
Authors: Han Zhang, Medhat Elsayed, Majid Bavand, Raimundas Gaigalas, Yigit Ozcan, Melike Erol-Kantarci
Date: 2024-01-19 00:00:00
Tasks: Data Poisoning, Federated Learning

Federated learning (FL) allows distributed participants to train machine learning models in a decentralized manner. It can be used for radio signal classification with multiple receivers due to its benefits in terms of privacy and scalability. However, the existing FL algorithms usually suffer from slow and unstable convergence and are vulnerable to poisoning attacks from malicious participants. In this work, we aim to design a versatile FL framework that simultaneously promotes the performance of the model both in a secure system and under attack. To this end, we leverage attention mechanisms as a defense against attacks in FL and propose a robust FL algorithm by integrating the attention mechanisms into the global model aggregation step. To be more specific, two attention models are combined to calculate the amount of attention cast on each participant. It will then be used to determine the weights of local models during the global aggregation. The proposed algorithm is verified on a real-world dataset and it outperforms existing algorithms, both in secure systems and in systems under data poisoning attacks.
Homodyned K-Distribution Parameter Estimation in Quantitative Ultrasound: Autoencoder and Bayesian Neural Network Approaches
Authors: Ali K. Z. Tehrani, Guy Cloutier, An Tang, Ivan M. Rosado-Mendez, Hassan Rivaz
Date: 2024-01-19 00:00:00
Tasks: Denoising, Diagnostic, parameter estimation

Quantitative ultrasound (QUS) analyzes the ultrasound backscattered data to find the properties of scatterers that correlate with the tissue microstructure. Statistics of the envelope of the backscattered radiofrequency (RF) data can be utilized to estimate several QUS parameters. Different distributions have been proposed to model envelope data. The homodyned K-distribution (HK distribution) is one of the most comprehensive distributions that can model ultrasound backscattered envelope data under diverse scattering conditions (varying scatterer number density and coherent scattering). The scatterer clustering parameter (alpha) and the ratio of the coherent to diffuse scattering power (k) are the parameters of this distribution that have been used extensively for tissue characterization in diagnostic ultrasound. The estimation of these two parameters (which we refer to as HK parameters) is done using optimization algorithms in which statistical features such as the envelope point-wise signalto-noise ratio (SNR), skewness, kurtosis, and the log-based moments have been utilized as input to such algorithms. The optimization methods minimize the difference between features and their theoretical value from the HK model. We propose that the true value of these statistical features is a hyperplane that covers a small portion of the feature space. In this paper, we follow two approaches to reduce the effect of sample features' error. We propose a model projection neural network based on denoising autoencoders to project the noisy features into this space based on this assumption. We also investigate if the noise distribution can be learned by the deep estimators. We compare the proposed methods with conventional methods using simulations, an experimental phantom, and data from an in vivo animal model of hepatic steatosis. A demo code are available online at http://code.sonography.ai
Waveform-Domain Complementary Signal Sets for Interrupted Sampling Repeater Jamming Suppression
Authors: Hanning Su, Qinglong Bao, Jiameng Pan, Fucheng Guo, Weidong Hu
Date: 2024-01-19 00:00:00

The interrupted-sampling repeater jamming (ISRJ) is coherent and has the characteristic of suppression and deception to degrade the radar detection capabilities. The study focuses on anti-ISRJ techniques in the waveform domain, primarily capitalizing on waveform design and and anti-jamming signal processing methods in the waveform domain. By exploring the relationship between waveform-domain adaptive matched filtering (WD-AMF) output and waveform-domain signals, we demonstrate that ISRJ can be effectively suppressed when the transmitted waveform exhibits waveform-domain complementarity. We introduce a phase-coded (PC) waveform set with waveform-domain complementarity and propose a method for generating such waveform sets of arbitrary code lengths. The performance of WD-AMF are further developed due to the designed waveforms, and simulations affirm the superior adaptive anti-jamming capabilities of the designed waveforms compared to traditional ones. Remarkably, this improved performance is achieved without the need for prior knowledge of ISRJ interference parameters at either the transmitter or receiver stages.
Understanding Hepatitis B Virus Infection through Hepatocyte Proliferation and Capsid Recycling
Authors: Rupchand Sutradhar, D C Dalal
Date: 2024-01-21 00:00:00

Proliferation of uninfected as well as infected hepatocytes and recycling of DNA-containing capsids are two major mechanisms playing significant roles in the clearance of hepatitis B virus (HBV) infection. In this study, the temporal dynamics of this infection are investigated through two in silico bio-mathematical models considering both proliferation of hepatocytes and the recycling of capsids. Both models are formulated on the basis of a key finding in the existing literature: mitosis of infected yields in two uninfected progenies. In the first model, we examine regular proliferation (occurs continuously), while the second model deals with the irregular proliferation (happens when the total number of liver cells decreases to less than 70% of its initial volume). The models are calibrated with the experimental data obtained from an adult chimpanzee. Results of this study suggest that when both hepatocytes proliferate with equal rate, proliferation aids the individual in a rapid recovery from the acute infection whereas in the case of chronic infection, the severity of the infection increases if the proliferation occurs frequently. On the other hand, if the infected cells proliferate at a slower rate than uninfected cells, the proliferation of uninfected hepatocytes contributes to increase the infection, but the proliferation of infected hepatocytes acts to reduce the infection from the long-term perspective. Furthermore, it is also observed that the differences between the outcomes of regular and irregular proliferations are substantial and noteworthy.
Matching biomolecular structures by registration of point clouds
Authors: Michael Habeck, Andreas Kröpelin, Nima Vakili
Date: 2024-01-22 00:00:00
Tasks: global-optimization

Motivation: Assessing the match between two biomolecular structures is at the heart of structural analyses such as superposition, alignment and docking. These tasks are typically solved with specialized structure-matching techniques implemented in software for protein structural alignment, rigid-body docking, or rigid fitting into cryo-EM maps. Results: We present a unifying framework to compare biomolecular structures by applying ideas from computer vision. The structures are represented as three-dimensional point clouds and compared by quantifying their overlap. We use the kernel correlation to measure point cloud overlap, and discuss local and global optimization strategies for maximizing the kernel correlation over the space of rigid transformations. We derive a majorization-minimization procedure that can be used to register two point clouds without establishing a point-to-point correspondence. We demonstrate that the majorization-minimization algorithms outperform the commonly used Iterative Closest Point registration algorithm. Furthermore, we discuss and benchmark a randomization strategy for globally optimizing the kernel correlation. We illustrate the approach on various 3D fitting problems such as the comparison of circularly permuted structures and rigid fitting of cryo-EM maps or bead models from small-angle scattering.
Combining oligo pools and Golden Gate cloning to create protein variant libraries or guide RNA libraries for CRISPR applications
Authors: Alicia Maciá Valero, Rianne C. Prins, Thijs de Vroet, Sonja Billerbeck
Date: 2024-01-22 00:00:00

Oligo pools are array-synthesized, user-defined mixtures of single-stranded oligonucleotides that can be used as a source of synthetic DNA for library cloning. While currently offering the most affordable source of synthetic DNA, oligo pools also come with limitations such as a maximum synthesis length (approximately 350 bases), a higher error rate compared to alternative synthesis methods, and the presence of truncated molecules in the pool due to incomplete synthesis. Here, we provide users with a comprehensive protocol that details how oligo pools can be used in combination with Golden Gate cloning to create user-defined protein mutant libraries, as well as single guide RNA libraries for CRISPR applications. Our methods are optimized to work within the Yeast Toolkit Golden Gate scheme, but are in principle compatible with any other Golden Gate-based modular cloning toolkit and extendable to other restriction enzyme-based cloning methods beyond Golden Gate. Our methods yield high-quality, affordable, in-house variant libraries.
Sensory adaptation in a continuum model of bacterial chemotaxis -- working range, cost-accuracy relation, and coupled systems
Authors: Vansh Kharbanda, Benedikt Sabass
Date: 2024-01-20 00:00:00

Sensory adaptation enables organisms to adjust their perception in a changing environment. A paradigm is bacterial chemotaxis, where the output activity of chemoreceptors is adapted to different baseline concentrations via receptor methylation. The range of internal receptor states limits the stimulus magnitude to which these systems can adapt. Here, we employ a highly idealized, Langevin-equation based model to study how the finite range of state variables affects the adaptation accuracy and the energy dissipation in individual and coupled systems. Maintaining an adaptive state requires constant energy dissipation. We show that the steady-state dissipation rate increases approximately linearly with the adaptation accuracy for varying stimulus magnitudes in the so-called perfect adaptation limit. This result complements the well-known logarithmic cost-accuracy relationship for varying chemical driving. Next, we study linearly coupled pairs of sensory units. We find that the interaction reduces the dissipation rate per unit and affects the overall cost-accuracy relationship. A coupling of the slow methylation variables results in a better accuracy than a coupling of activities. Overall, the findings highlight the significance of both the working range and collective operation mode as crucial design factors that impact the accuracy and energy expenditure of molecular adaptation networks.
Approximating a gene regulatory network from non-sequential data
Authors: Cliff Stein, Pratik Worah
Date: 2024-01-22 00:00:00
Tasks: compressed sensing

Given non-sequential snapshots from instances of a dynamical system, we design a compressed sensing based algorithm that reconstructs the dynamical system. We formally prove that successful reconstruction is possible under the assumption that we can construct an approximate clock from a subset of the coordinates of the underlying system. As an application, we argue that our assumption is likely to be true for RNA-seq datasets, and thus we can recover the underlying nuclear receptor networks and predict pathways, as opposed to genes, that may differentiate between interesting phenotypes in some publicly available datasets.
Full-dimensional characterisation of time-warped spike-time stimulus-response distribution geometries
Authors: James B Isbister
Date: 2024-01-22 00:00:00
Tasks: Dimensionality Reduction

Characterising the representation of sensory stimuli in the brain is a fundamental scientific endeavor, which can illuminate principles of information coding. Most characterizations reduce the dimensionality of neural data by converting spike trains to firing rates or binned spike counts, applying explicitly named methods of ``dimensionality reduction'', or collapsing trial-to-trial variability. Characterisation of the full-dimensional geometry of timing-based representations may provide unexpected insights into how complex high-dimensional information is encoded. Recent research shows that the distribution of representations elicited over trials of a single stimulus can be geometrically characterized without the application of dimensionality reduction, maintaining the temporal spiking information of individual neurons in a cell assembly and illuminating rich geometric structure. We extend these results, showing that precise spike time patterns for larger cell assemblies are time-warped (i.e. stretched or compressed) on each trial. Moreover, by geometrically characterizing distributions of large spike time patterns, our analysis supports the hypothesis that the degree to which a spike time pattern is time-warped depends on the cortical area's background activity level on a single trial. Finally, we suggest that the proliferation of large electrophysiology datasets and the increasing concentration of ``neural geometrists'', creates ideal conditions for characterization of full-dimensional spike time representations, in complement to dimensionality reduction approaches.
Metacognition is all you need? Using Introspection in Generative Agents to Improve Goal-directed Behavior
Authors: Jason Toy, Josh MacAdam, Phil Tabor
Date: 2024-01-09 00:00:00
Tasks: All

Recent advances in Large Language Models (LLMs) have shown impressive capabilities in various applications, yet LLMs face challenges such as limited context windows and difficulties in generalization. In this paper, we introduce a metacognition module for generative agents, enabling them to observe their own thought processes and actions. This metacognitive approach, designed to emulate System 1 and System 2 cognitive processes, allows agents to significantly enhance their performance by modifying their strategy. We tested the metacognition module on a variety of scenarios, including a situation where generative agents must survive a zombie apocalypse, and observe that our system outperform others, while agents adapt and improve their strategies to complete tasks over time.