pwc-archive/papers-with-abstracts huggingface dataset:
Near-Field Channel Estimation in Dual-Band XL-MIMO with Side Information-Assisted Compressed Sensing
Authors: Haochen Wu, Liyang Lu, Zhaocheng Wang
Date: 2024-03-19 00:00:00
Tasks: compressed sensing

Near-field communication comes to be an indispensable part of the future sixth generation (6G) communications at the arrival of the forth-coming deployment of extremely large-scale multiple-input-multiple-output (XL-MIMO) systems. Due to the huge array aperture and high-frequency bands, the electromagnetic radiation field is modeled by the spherical waves instead of the conventional planar waves, leading to severe weak sparsity to angular-domain near-field channel. Therefore, the channel estimation reminiscent of the conventional compression sensing (CS) approaches in the angular domain, judiciously utilized for low pilot overhead, may result in unprecedented challenges. To this end, this paper proposes a brand-new near-field channel estimation scheme by exploiting the naturally occurring useful side information. Specifically, we formulate the dual-band near-field communication model based on the fact that high-frequency systems are likely to be deployed with lower-frequency systems. Representative side information, i.e., the structural characteristic information derived by the sparsity ambiguity and the out-of-band spatial information stemming from the lower-frequency channel, is explored and tailored to materialize exceptional near-field channel estimation. Furthermore, in-depth theoretical analyses are developed to guarantee the minimum estimation error, based on which a suite of algorithms leveraging the elaborating side information are proposed. Numerical simulations demonstrate that the designed algorithms provide more assured results than the off-the-shelf approaches in the context of the dual-band near-field communications in both on- and off-grid scenarios, where the angle of departures/arrivals are discretely or continuously distributed, respectively.
An Adaptive feature mode decomposition based on a novel health indicator for bearing fault diagnosis
Authors: Sumika Chauhan, Govind Vashishtha, Rajesh Kumar, Radoslaw Zimroz, Pradeep Kundu
Date: 2024-03-19 00:00:00
Tasks: Fault Diagnosis

The vibration analysis of the bearing is very crucial because of its non-stationary nature and low signal-to-noise ratio. Therefore, a novel scheme for detecting bearing defects is put forward based on the extraction of single-valued neutrosophic cross-entropy (SVNCE) to address this issue. Initially, the artificial hummingbird algorithm (AHA) is used to make the feature mode decomposition (FMD) adaptive by optimizing its parameter based on a newly developed health indicator (HI) i.e. sparsity impact measure index (SIMI). This HI ensures full sparsity and impact properties simultaneously. The raw signals are disintegrated into different modes by adaptive FMD at optimal values of its parameters. The energy of these modes is calculated for different health conditions. The energy interval range has been decided based on energy eigen which are then transformed into single-valued neutrosophic sets (SVNSs) for unknown defect conditions. The minimum argument principle employs the least SVNCE values between SVNSs of testing samples (obtained from unknown bearing conditions) and SVNSs of training samples (obtained from known bearing conditions) to recognize the different defects in the bearing. It has been discovered that the suggested methodology is more adept at identifying the various bearing defects.
Bearing damage detection with orthogonal and non-negative low-rank feature extraction
Authors: Mateusz Gabor, Rafal Zdunek, Agnieszka Wylomanska, Radoslaw Zimroz
Date: 2024-03-19 00:00:00

Local damage of bearings can be detected as a weak cyclic and impulsive component in a highly noisy measured signal. A key problem is how to extract the signal of interest (SOI) from the raw signal, i.e., how to identify and design an optimal filter. To tackle this problem, we propose to use stochastic sampled orthogonal non-negative matrix factorization for extracting frequency-based features from a spectrogram of the measured signal. The proposed algorithm finds a selective filter that is tailored to the frequency band of the SOI. We show that our approach outperforms the other state-of-the-art selectors that were previously used in condition monitoring. The efficiency of the proposed method is illustrated using both a simulation study and the following real signals: (a) vibration signal from a test rig in the laboratory and (b) acoustic signal from a belt conveyor.
Non-negative tensor factorization for vibration-based local damage detection
Authors: Mateusz Gabor, Rafal Zdunek, Radoslaw Zimroz, Jacek Wodecki, Agnieszka Wylomanska
Date: 2024-03-19 00:00:00
Tasks: Diagnostic

In this study, a novel non-negative tensor factorization (NTF)-based method for vibration-based local damage detection in rolling element bearings is proposed. As the diagnostic signal registered from a faulty machine is non-stationary, the time-frequency method is frequently used as a primary decomposition technique. It is proposed here to extract multi-linear NTF-based components from a 3D array of time-frequency representations of an observed signal partitioned into blocks. As a result, frequency and temporal informative components can be efficiently separated from non-informative ones. The experiments performed on synthetic and real signals demonstrate the high efficiency of the proposed method with respect to the already known non-negative matrix factorization approach.
Non-negative matrix underapproximation as optimal frequency band selector
Authors: Mateusz Gabor, Rafal Zdunek, Radoslaw Zimroz, Agnieszka Wylomanska
Date: 2024-03-19 00:00:00

Time-frequency representation (TFR) is often used for non-stationary signal analysis. The most intuitive and interpretable TFR is the spectrogram. Recently, a concept of non-negative matrix factorization (NMF) has been successfully applied to local damage detection in rolling elements of bearings via spectrogram factorization. NMF applied to the spectrogram allows one to find an informative frequency band, which could be further used as a filter characteristic. However, the obtained filter characteristics mostly detect the informative frequency band, which also encompasses a lot of noise. In the case where noise is more problematic, as is the case for acoustic signals from industrial machines, the NMF hardly detects the damage. To solve this problem and obtain more selective filters, which are more robust to noise, we propose the non-negative matrix under-approximation (NMU) as an informative frequency band selector. Due to the more sparse parts-based representation of the NMU compared to NMF, NMU provides more selective filter characteristics, which neglect the non-informative frequency bands related to the noise. In practice, it means that NMU gives a better signal-to-noise ratio for the filtered signal. The efficiency of the proposed approach has been validated on the vibration signal from the test rig and the acoustic signal from an idler.
Digital Twin Channel for 6G: Concepts, Architectures and Potential Applications
Authors: Heng Wang, Jianhua Zhang, Gaofeng Nie, Li Yu, Zhiqiang Yuan, Tongjie Li, Jialin Wang, Guangyi Liu
Date: 2024-03-19 00:00:00

Digital twin channel (DTC) is the real-time mapping of a wireless channel from the physical world to the digital world, which is expected to provide significant performance enhancements for the sixth-generation (6G) air-interface design. In this work, we first define five evolution levels of channel twins with the progression of wireless communication. The fifth level, autonomous DTC, is elaborated with multi-dimensional factors such as methodology, characterization precision, and data category. Then, we provide detailed insights into the requirements and architecture of a complete DTC for 6G. Subsequently, a sensing-enhanced real-time channel prediction platform and experimental validations are exhibited. Finally, drawing from the vision of the 6G network, we explore the potential applications and the open issues in future DTC research.
Deep Learning-Based CSI Feedback for RIS-Aided Massive MIMO Systems with Time Correlation
Authors: Zhangjie Peng, Zhaotian Li, Ruijing Liu, Cunhua Pan, Feiniu Yuan, Jiangzhou Wang
Date: 2024-03-19 00:00:00

In this paper, we consider an reconfigurable intelligent surface (RIS)-aided frequency division duplex (FDD) massive multiple-input multiple-output (MIMO) downlink system.In the FDD systems, the downlink channel state information (CSI) should be sent to the base station through the feedback link. However, the overhead of CSI feedback occupies substantial uplink bandwidth resources in RIS-aided communication systems. In this work, we propose a deep learning (DL)-based scheme to reduce the overhead of CSI feedback by compressing the cascaded CSI. In the practical RIS-aided communication systems, the cascaded channel at the adjacent slots inevitably has time correlation. We use long short-term memory to learn time correlation, which can help the neural network to improve the recovery quality of the compressed CSI. Moreover, the attention mechanism is introduced to further improve the CSI recovery quality. Simulation results demonstrate that our proposed DLbased scheme can significantly outperform other DL-based methods in terms of the CSI recovery quality
Near-Field Communications with Block-Dominant Compressed Sensing: Fundamentals, Approaches, and Future Directions
Authors: Liyang Lu, Ke Ma, Yue Wang, Zhaocheng Wang
Date: 2024-03-19 00:00:00
Tasks: compressed sensing

In the context of extremely large-scale antenna arrays deployed in sixth-generation (6G) mobile networks, near-field (NF) communications have gained considerable attention. Unlike the planar waves formulated in the far-field, electromagnetic radiation propagates as spherical waves in the NF. This alteration affects the NF channel characteristics, particularly resulting in weak sparsity in angular-domain NF channels, which poses tricky challenges to the application of compressed sensing (CS). Motivated by these facts, the block-dominant compressed sensing (BD-CS) techniques are proposed to assist NF communications. This article starts with the introduction on why block sparsity exists in the distance-limited NF region. Then, block-dominant side-information (BD-SI) is exploited to facilitate the actual NF communication implementation. While BD-CS shows promise in providing exceptional channel estimation accuracy and high spectral efficiency, several key challenges, opportunities, and practical implementation issues in NF communications need careful consideration.
Bacterial Communications and Computing in Internet of Everything (IoE)
Authors: B. Yagmur Koca, Ozgur B. Akan
Date: 2024-03-18 00:00:00

Concurrent with advancements in molecular communication (MC), bacterial communication is emerging as a key area of interest. Given the frequent use of bacteria in various MC models, it is essential to have a thorough grasp of their intrinsic communication, signaling, and engineering techniques. Although it is crucial to have a strong understanding of the communication background, the inherent biological variability of bacteria may introduce complexity. Thus, an in-depth understanding of bacteria and their communication is a must for improving and extending the models in which they are utilized. Furthermore, the emerging and evolving domain of bacterial computing provides an exciting opportunity for advancing applications in areas such as environmental monitoring and biological computing networks. By integrating the communication and sensing capabilities, bacterial computing offers a promising framework for enhancing the adaptability and responsiveness of bacteria. This paper provides a comprehensive review of bacterial communication and computing, illustrating their application and the link with the concept of the Internet of Everything (IoE). Through the analysis of these biological systems, we reach a deeper insight on how the small-scale interactions may contribute to the major concept of universal interconnectedness; thus, we make the knowledge to flow and communication stronger between different fields. The discussion include the identification of the different bacterial mechanisms that could revolutionize the traditional communication systems. Thus, this paper offers valuable insights into previously unaddressed aspects of bacterial behavior, suggesting novel avenues for future research and aiming to advance understanding and application of bacterial sensing, communication and computing in MC models.
A Wideband Distributed Massive MIMO Channel Sounder for Communication and Sensing
Authors: Michiel Sandra, Christian Nelson, Xuhong LI, Xuesong Cai, Fredrik Tufvesson, Anders J Johansson
Date: 2024-03-18 00:00:00
Tasks: Super-Resolution

Channel sounding is a vital step in understanding wireless channels for the design and deployment of wireless communication systems. In this paper, we present the design and implementation of a coherent distributed massive MIMO channel sounder operating at 5-6 GHz with a bandwidth of 400 MHz based on the NI USRP X410. Through the integration of transceiver chains and RF switches, the design facilitates the use of a larger number of antennas without significant compromise in dynamic capability. Our current implementation is capable of measuring thousands of antenna combinations within tens of milliseconds. Every radio frequency switch is seamlessly integrated with a 16-element antenna array, making the antennas more practical to be transported and flexibly distributed. In addition, the channel sounder features real-time processing to reduce the data stream to the host computer and increase the signal-to-noise ratio. The design and implementation are verified through two measurements in an indoor laboratory environment. The first measurement entails a single-antenna robot as transmitter and 128 distributed receiving antennas. The second measurement demonstrates a passive sensing scenario with a walking person. We evaluate the results of both measurements using the super-resolution algorithm SAGE. The results demonstrate the great potential of the presented sounding system for providing high-quality radio channel measurements, contributing to high-resolution channel estimation, characterization, and active and passive sensing in realistic and dynamic scenarios.
Simplified Self-homodyne Coherent System Based on Alamouti Coding and Digital Subcarrier Multiplexing
Authors: Wei Wang, Dongdong Zou, Zhenpeng Wu, Qi Sui, Xingwen Yi, Fan Li, Chao Lu, Zhaohui Li
Date: 2024-03-18 00:00:00

Coherent technology inherent with more availabledegrees of freedom is deemed a competitive solution for nextgeneration ultra-high-speed short-reach optical interconnects.However, the fatal barriers to implementing the conventiona.coherent system in short-reach optical interconnect are the costfootprint, and power consumption. Self-homodyne coherentsystem exhibits its potential to reduce the power consumption ofthe receiver-side digital signal processing (Rx-DSP) by deliveringthe local oscillator (LO) from the transmitter. However, anautomatic polarization controller (APC) is inevitable in the remoteLO link to avoid polarization fading, resulting in additional costsTo address the polarization fading issue, a simplified self.homodyne coherent system is proposed enabled by Alamouticoding in this paper. Benefiting from the Alamouti coding betweentwo polarizations, a polarization-insensitive receiver onlyincluding a 3dB coupler, a 90o Hybrid, and two balancedphotodiodes (BPDs)is sufficient for reception. Meanwhile, theAPC in the LO link is needless, simplifying the receiver structuresignificantly. Besides, the digital subcarrier multiplexing (DSCM)technique is also adopted to relax the computational complexity ofthe chromatic dispersion compensation (CDC), which is one of thedominant power consumption modules in Rx-DSP. Thetransmission performance of 50Gbaud 4-subcarrier 16/32OAM(4SC-16/320AM) DSCM signal based on the proposed simplifiedself-homodyne coherent system is investigated experimentallyThe results show that the bit-error-ratio(BER) performancedegradation caused by CD can be solved by increasing 4 taps inthe equalizer for 80km single mode fiber(SMF)transmissionwithout individual CDC, which operates in a low-complexitymanner.
Asymptotic spreading of predator-prey populations in a shifting environment
Authors: King-Yeung Lam, Ray Lee
Date: 2024-03-18 00:00:00

Inspired by recent studies associating shifting temperature conditions with changes in the efficiency of predator species in converting their prey to offspring, we propose a predator-prey model of reaction-diffusion type to analyze the consequence of such effects on the population dynamics and spread of species. In the model, the predator conversion efficiency is represented by a spatially heterogeneous function depending on the variable $\xi=x-c_1t$ for some given $c_1>0$. Using the Hamilton-Jacobi approach, we provide explicit formulas for the spreading speed of the predator species. When the conversion function is monotone increasing, the spreading speed is determined in all cases and non-local pulling is possible. When the function is monotone decreasing, we provide formulas for the spreading speed when the rate of shift of the conversion function is sufficiently fast or slow.
Nonconcave Robust Utility Maximization under Projective Determinacy
Authors: Laurence Carassus, Massinissa Ferhoune
Date: 2024-03-18 00:00:00

We study a robust utility maximization problem in a general discrete-time frictionless market. The investor is assumed to have a random, nonconcave and nondecreasing utility function, which may or may not be finite on the whole real-line. She also faces model ambiguity on her beliefs about the market, which is modeled through a set of priors. We prove, using only primal methods, the existence of an optimal investment strategy when the utility function is also upper-semicontinuous. For that, we introduce the new notion of projectively measurable functions. We show basic properties of these functions as stability under sums, differences, products, suprema, infima and compositions but also assuming the set-theoretical axiom of Projective Determinacy (PD) stability under integration and existence of $\epsilon$-optimal selectors. We consider projectively measurable random utility function and price process and assume that the graphs of the sets of local priors are projective sets. Our other assumptions are stated on a prior-by-prior basis and correspond to generally accepted assumptions in the literature on markets without ambiguity.
Strangers in a foreign land: 'Yeastizing' plant enzymes
Authors: Kristen Van Gelder, Steffen N. Lindner, Andrew D. Hanson, Juannan Zhou
Date: 2024-03-19 00:00:00

Expressing plant metabolic pathways in microbial platforms is an efficient, cost-effective solution for producing many desired plant compounds. As eukaryotic organisms, yeasts are often the preferred platform. However, expression of plant enzymes in a yeast frequently leads to failure because the enzymes are poorly adapted to the foreign yeast cellular environment. Here we first summarize current engineering approaches for optimizing performance of plant enzymes in yeast. A critical limitation of these approaches is that they are labor-intensive and must be customized for each individual enzyme, which significantly hinders the establishment of plant pathways in cellular factories. In response to this challenge, we propose the development of a cost-effective computational pipeline to redesign plant enzymes for better adaptation to the yeast cellular milieu. This proposition is underpinned by compelling evidence that plant and yeast enzymes exhibit distinct sequence features that are generalizable across enzyme families. Consequently, we introduce a data-driven machine learning framework designed to extract 'yeastizing' rules from natural protein sequence variations, which can be broadly applied to all enzymes. Additionally, we discuss the potential to integrate the machine learning model into a full design-build-test-cycle.
Predicting the stability of profiling signals of small RNAs
Authors: Qiuyun Li, Manda Riehl
Date: 2024-03-19 00:00:00

Profiling is a process that finds similarities between different RNA secondary structures by extracting signals from the Boltzmann sampling. The reproducibility of profiling can be identified by the standard deviation of number of features among Boltzmann samples. We found a strong relationship between the frequency of each helix class and its standard deviation of the frequency upon repeated Boltzmann sampling. We developed a perturbation technique to predict the stability of these featured helix classes without the need for repeated Boltzmann sampling, with accuracy between 84% and 94%, depending on the type of RNA. Our technique only requires 0.2% of the computation time compared to one profiling process.
AlphaFold2 for protein structure prediction: Best practices and critical analyses
Authors: Ragousandirane Radjasandirane, Alexandre G. de Brevern
Date: 2024-03-19 00:00:00
Tasks: Drug Design, Protein Structure Prediction

AlphaFold2 (AF2) has emerged in recent years as a groundbreaking innovation that has revolutionized several scientific fields, in particular structural biology, drug design and the elucidation of disease mechanisms. Many scientists now use AF2 on a daily basis, including non-specialist users. This chapter is aimed at the latter. Tips and tricks for getting the most out of AF2 to produce a high quality biological model are discussed here. We suggest to non-specialist users how to maintain a critical perspective when working with AF2 models and provide guidelines on how to properly evaluate them. After showing how to perform our own structure prediction using ColabFold, we list several ways to improve AF2 models by adding information that is missing from the original AF2 model. By using software such as AlphaFill to add cofactors and ligands to the models, or MODELLER to add disulfide bridges between cysteines, we guide users to build a high quality biological model suitable for applications such as drug design, protein interaction or molecular dynamics studies.