Machine‐learning‐based prediction methods for path loss and delay spread in air‐to‐ground millimetre‐wave channels

The main objective of this paper to determine multipath and time-varying channel behaviour of short-terrestrial millimetre-wave point-to-point radio links. In an attempt to invigorate the impact of rain attenuation on mm-wave channel parameters such as the RMS delay spread, path loss received power strength and Rician distribution with a K factor. A brief analysis of rain fading was presented based on the simultaneous measurement of one-minute rain rate and its effects on a short experimental link of 38 GHz. Rain fade average is observed as high as 16 dB for 300 m path at about 125 mm/hr rain intensity. The statistical spatial channel mode (SSCM) simulation software was utilized for an operating frequency of 38 GHz. To generate of power delay profile (PDP). For both omnidirectional and directional antenna. The RMS delay spread and path loss has been estimated using the environmental parameters of Kuala Lumpur city which illustrates the theoretical performances of 5G in Malaysia. It is observed that RMS delay spread, path loss received power strength and K factor effected dramatically by rain fade. (SSCM) simulation software has to be modified to consider rain fade dynamic characteristics to achieve ultra-reliability requirements of outdoor applications in the tropical regions. This study is important for understanding signal propagation phenomena in short distance and enabling the utilization of the millimetre wave band for an urban micro-cellular environment for 5G communication system.

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Protein Inter-Residue Contacts Prediction: Methods, Performances and Applications

Current Bioinformatics ◽

10.2174/1574893613666181109130430 ◽

2019 ◽

Vol 14 (3) ◽

pp. 178-189 ◽

Cited By ~ 3

Author(s):

Xiaoyang Jing ◽

Qimin Dong ◽

Ruqian Lu ◽

Qiwen Dong

Keyword(s):

Machine Learning ◽

Protein Structure ◽

Tertiary Structure ◽

Prediction Methods ◽

Learning Methods ◽

Typical Application ◽

Machine Learning Methods ◽

Residue Contacts ◽

Fusion Methods ◽

Correlated Mutations

Background:Protein inter-residue contacts prediction play an important role in the field of protein structure and function research. As a low-dimensional representation of protein tertiary structure, protein inter-residue contacts could greatly help de novo protein structure prediction methods to reduce the conformational search space. Over the past two decades, various methods have been developed for protein inter-residue contacts prediction.Objective:We provide a comprehensive and systematic review of protein inter-residue contacts prediction methods.Results:Protein inter-residue contacts prediction methods are roughly classified into five categories: correlated mutations methods, machine-learning methods, fusion methods, templatebased methods and 3D model-based methods. In this paper, firstly we describe the common definition of protein inter-residue contacts and show the typical application of protein inter-residue contacts. Then, we present a comprehensive review of the three main categories for protein interresidue contacts prediction: correlated mutations methods, machine-learning methods and fusion methods. Besides, we analyze the constraints for each category. Furthermore, we compare several representative methods on the CASP11 dataset and discuss performances of these methods in detail.Conclusion:Correlated mutations methods achieve better performances for long-range contacts, while the machine-learning method performs well for short-range contacts. Fusion methods could take advantage of the machine-learning and correlated mutations methods. Employing more effective fusion strategy could be helpful to further improve the performances of fusion methods.

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Survey of Millimeter-Wave Propagation Measurements and Models in Indoor Environments

Electronics ◽

10.3390/electronics10141653 ◽

2021 ◽

Vol 10 (14) ◽

pp. 1653

Author(s):

Ahmed Al-Saman ◽

Michael Cheffena ◽

Olakunle Elijah ◽

Yousef A. Al-Gumaei ◽

Sharul Kamal Abdul Rahim ◽

...

Keyword(s):

Millimeter Wave ◽

Measurement Techniques ◽

Path Loss ◽

Delay Spread ◽

Indoor Environments ◽

Future Trends ◽

Signal Loss ◽

Indoor Wireless ◽

The Future ◽

Future Demands

The millimeter-wave (mmWave) is expected to deliver a huge bandwidth to address the future demands for higher data rate transmissions. However, one of the major challenges in the mmWave band is the increase in signal loss as the operating frequency increases. This has attracted several research interests both from academia and the industry for indoor and outdoor mmWave operations. This paper focuses on the works that have been carried out in the study of the mmWave channel measurement in indoor environments. A survey of the measurement techniques, prominent path loss models, analysis of path loss and delay spread for mmWave in different indoor environments is presented. This covers the mmWave frequencies from 28 GHz to 100 GHz that have been considered in the last two decades. In addition, the possible future trends for the mmWave indoor propagation studies and measurements have been discussed. These include the critical indoor environment, the roles of artificial intelligence, channel characterization for indoor devices, reconfigurable intelligent surfaces, and mmWave for 6G systems. This survey can help engineers and researchers to plan, design, and optimize reliable 5G wireless indoor networks. It will also motivate the researchers and engineering communities towards finding a better outcome in the future trends of the mmWave indoor wireless network for 6G systems and beyond.

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Accurate estimation of isoelectric point of protein and peptide based on amino acid sequences

Bioinformatics ◽

10.1093/bioinformatics/btv674 ◽

2015 ◽

Vol 32 (6) ◽

pp. 821-827 ◽

Cited By ~ 19

Author(s):

Enrique Audain ◽

Yassel Ramos ◽

Henning Hermjakob ◽

Darren R. Flower ◽

Yasset Perez-Riverol

Keyword(s):

Machine Learning ◽

Isoelectric Point ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Basis Set ◽

Superior Performance ◽

Supplementary Information ◽

Training Dataset ◽

Accurate Estimation ◽

Prediction Methods

Abstract Motivation: In any macromolecular polyprotic system—for example protein, DNA or RNA—the isoelectric point—commonly referred to as the pI—can be defined as the point of singularity in a titration curve, corresponding to the solution pH value at which the net overall surface charge—and thus the electrophoretic mobility—of the ampholyte sums to zero. Different modern analytical biochemistry and proteomics methods depend on the isoelectric point as a principal feature for protein and peptide characterization. Protein separation by isoelectric point is a critical part of 2-D gel electrophoresis, a key precursor of proteomics, where discrete spots can be digested in-gel, and proteins subsequently identified by analytical mass spectrometry. Peptide fractionation according to their pI is also widely used in current proteomics sample preparation procedures previous to the LC-MS/MS analysis. Therefore accurate theoretical prediction of pI would expedite such analysis. While such pI calculation is widely used, it remains largely untested, motivating our efforts to benchmark pI prediction methods. Results: Using data from the database PIP-DB and one publically available dataset as our reference gold standard, we have undertaken the benchmarking of pI calculation methods. We find that methods vary in their accuracy and are highly sensitive to the choice of basis set. The machine-learning algorithms, especially the SVM-based algorithm, showed a superior performance when studying peptide mixtures. In general, learning-based pI prediction methods (such as Cofactor, SVM and Branca) require a large training dataset and their resulting performance will strongly depend of the quality of that data. In contrast with Iterative methods, machine-learning algorithms have the advantage of being able to add new features to improve the accuracy of prediction. Contact: [email protected] Availability and Implementation: The software and data are freely available at https://github.com/ypriverol/pIR. Supplementary information: Supplementary data are available at Bioinformatics online.

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Broadband Wireless Channel in Composite High-Speed Railway Scenario: Measurements, Simulation, and Analysis

Wireless Communications and Mobile Computing ◽

10.1155/2017/2897636 ◽

2017 ◽

Vol 2017 ◽

pp. 1-15 ◽

Cited By ~ 4

Author(s):

Jianwen Ding ◽

Lei Zhang ◽

Jingya Yang ◽

Bin Sun ◽

Jiying Huang

Keyword(s):

Ray Tracing ◽

Communication Systems ◽

High Speed ◽

Channel Model ◽

High Reliability ◽

Rapid Development ◽

Path Loss ◽

Wireless Channel ◽

Delay Spread ◽

High Speed Railway

The rapid development of high-speed railway (HSR) and train-ground communications with high reliability, safety, and capacity promotes the evolution of railway dedicated mobile communication systems from Global System for Mobile Communications-Railway (GSM-R) to Long Term Evolution-Railway (LTE-R). The main challenges for LTE-R network planning are the rapidly time-varying channel and high mobility, because HSR lines consist of a variety of complex terrains, especially the composite scenarios where tunnels, cuttings, and viaducts are connected together within a short distance. Existing researches mainly focus on the path loss and delay spread for the individual HSR scenarios. In this paper, the broadband measurements are performed using a channel sounder at 950 MHz and 2150 MHz in a typical HSR composite scenario. Based on the measurements, the pivotal characteristics are analyzed for path loss exponent, power delay profile, and tap delay line model. Then, the deterministic channel model in which the 3D ray-tracing algorithm is applied in the composite scenario is presented and validated by the measurement data. Based on the ray-tracing simulations, statistical analysis of channel characteristics in delay and Doppler domain is carried out for the HSR composite scenario. The research results can be useful for radio interface design and optimization of LTE-R system.

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Millimetre‐wave path‐loss variability between two body‐mounted monopole antennas

IET Microwaves Antennas & Propagation ◽

10.1049/iet-map.2011.0564 ◽

2013 ◽

Vol 7 (1) ◽

pp. 1-7 ◽

Cited By ~ 15

Author(s):

Yuriy Ivanovich Nechayev ◽

Xianyue Wu ◽

Costas C. Constantinou ◽

Peter S. Hall

Keyword(s):

Path Loss ◽

Millimetre Wave ◽

Monopole Antennas ◽

Wave Path

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The 5-GHz Airport Surface Area Channel—Part I: Measurement and Modeling Results for Large Airports

IEEE Transactions on Vehicular Technology ◽

10.1109/tvt.2007.912334 ◽

2008 ◽

Vol 57 (4) ◽

pp. 2014-2026 ◽

Cited By ~ 35

Author(s):

David W. Matolak ◽

Indranil Sen ◽

Wenhui Xiong

Keyword(s):

Path Loss ◽

Correlation Coefficients ◽

Line Of Sight ◽

Delay Spread ◽

Rms Delay Spread ◽

Amplitude Data ◽

Amplitude Correlation ◽

5 Ghz ◽

Best Fit ◽

Probability Density Function Pdf

We describe results from a channel measurement and modeling campaign for the airport surface environment in the 5-GHz band. Using a 50-MHz bandwidth test signal, thousands of power delay profiles (PDPs) were obtained and processed to develop empirical tapped-delay line statistical channel models for large airports. A log-distance path loss model was also developed. The large airport surface channel is classified into three propagation regions, and models are presented for each of the regions for two values of bandwidth. Values of the median root-mean-square (RMS) delay spread range from 500 to 1000 ns for these airports, with the 90 th percentile RMS delay spreads being approximately 1.7 ms. Corresponding correlation bandwidths (i.e., correlation value 1/2) range from approximately 1.5 MHz in non-line-of-sight (NLOS) settings to 17.5 MHz in line-of-sight (LOS) settings. Two types of statistical nonstationarity were also observed: 1) multipath component persistence and 2) propagation region transitions. We provide the multipath component probability of occurrence models and describe Markov chains that are used for modeling both phenomena. Channel tap amplitude statistics are also provided, using the flexible Weibull probability density function (pdf). This pdf was found to best fit fading tap amplitude data, particularly for frequently observed severe fading, which is characterized by fade probabilities that are worse than the commonly used Rayleigh model. Fading parameters equivalent to Nakagami-m-model values ofmnear 0.7 were often observed (withm= 1 being Rayleigh and m < 1 being worse than Rayleigh). We also provide channel tap amplitude correlation coefficients, which typically range from 0.1 to 0.4 but occasionally take values greater than 0.7.

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