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FDD based Cell-free massive MIMO with angular reciprocity over rician fading channels

Telecommunication Systems 2025年第3期88卷 1-18页

作者： Hassan, Beenish Baig, Sobia Aslam, Saad Tahzeeb, Shahab Hasan, syed Faraz Department of Electrical Engineering The Islamia University of Bahawalpur Bahawalpur Pakistan Electrical Engineering Department College of Engineering Qassim University Unaizah Saudi Arabia School of Computing and Artificial Intelligence Faculty of Engineering and Technology Sunway University Selangor Malaysia Department of Computer and Information Systems Engineering NED University of Engineering and Technology Karachi Pakistan Directorate of Research Services University of New England Armidale Australia

We present a combined angle reciprocity and Rician K factor based feedback for up/downlink scenarios using MMSE estimator for frequency division duplex (FDD) based massive Multiple Input Multiple Output (MIMO) system. It considers phase variation for spatially correlated Rician fading channel and also takes into account the phase shift in the line-of-sight component of Rician channel that is incurred due to user mobility and/or phase noise. The cell-free massive MIMO system has largely been implemented in the context of time division duplex (TDD) systems. However, FDD is still prevalent in communication standards and therefore, it is essential to assess its performance for FDD based channel estimation schemes in a typical cell-free massive MIMO environment. To begin with, we present an analysis of estimation schemes including the minimum mean square error (MMSE), linear MMSE (LMMSE), and least-square (LS) in terms of their spectral efficiency, using prior channel knowledge at the access points. We also present an angle-based beamforming technique that combines the quantized channel feedback based on Rician K factor with angle reciprocity-based channel estimates. The multipath channel estimation for MMSE and LMMSE schemes is also presented and simulation results are provided that show the superior performance of proposed angle and feedback MMSE over the LMMSE and theoretical MMSE schemes. Numerical simulations of the overall system confirm that the combined angular reciprocity and feedback-based MMSE estimates perform better with a value of $$12.6\%$$ for spectral efficiency and $$27\%$$ for energy efficiency, thus, producing an overall improved system performance compared to the conventional FDD based estimation schemes.

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Data-driven Approach for Optimising Resource Allocation of O-RAN Networks

Data-driven Approach for Optimising Resource Allocation of O...

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International Joint Conference on Neural Networks (IJCNN)

作者： Haitham Mahmoud Muhammad Najmul Islam Farooqui De Mi Liucheng Guo Chen Lu Yuxi Gan Zhen Gao Ziwei Wang Yunsheng Zhang College of Computing Birmingham City University Birmingham United Kingdom Computer Engineering Sir Syed University of Engineering & Technology Karachi Pakistan Tangi0 LTD (TG0) London United Kingdom Shenzhen Institute of Information Technology Shenzhen China School of Information and Electronics Beijing Institute of Technology Beijing China Department of Computer Science and Technology Tsinghua University Beijing China School of Engineering Lancaster University Lancaster United Kingdom

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Radio Access Network (RAN) deployments are evolving quickly owing to the innovative approaches of the Open Radio Access Network (O-RAN) Alliance. Specifically, they are moving away from closed, customized hardware implementations and toward virtualized instances operating on shared platforms. Future successful and affordable RAN deployments are made possible by this paradigm change, which is characterised by the separation of radio software components from hardware. Real-time network parameter configuration, sufficient computing resources for virtualized RAN (vRAN) deployment, and dependable processing unit sharing among numerous vRAN instances are some of the obstacles still standing in the way of successful O-RAN network implementations. Thus, this paper explored and compared the effectiveness of diverse optimization algorithms for minimising the number of resource blocks (nRBS), including machine learning (RandomForestRegressor), heuristic, and mathematical methods. Moreover, it investigates the lessons learned and the limitations of the proposed system. It demonstrates the practical success of a heuristic approach in O-RAN optimization, achieving significant reductions in resource blocks based on the Throughput-to-Bandwidth Ratio. It also provided insights into challenges with the RandomForestRegressor model, highlighted the importance of considering real-world network dynamics, and offered valuable lessons for future research, emphasizing the need for adaptive solutions and exploring hybrid optimization approaches, ultimately contributing to an enhanced understanding of O-RAN optimization.

关键词： Machine learning algorithms Neural networks Open RAN Machine learning Hardware Software Real-time systems

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Leveraging Artificial Intelligence for Age Prediction in Cattle based on Dentition

Leveraging Artificial Intelligence for Age Prediction in Cat...

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Computing and Information technology (ICCIT-1441), 2020 International Conference on

作者： Anas Inam Hasan Rehman Muhammad Farrukh Shahid syed Umaid Ahmed Department of Electrical Engineering National University of Sciences and Technology (NUST) Karachi Pakistan Department of A.I and Datascience National University of Computer and Emerging Sciences Karachi Pakistan

ISBN: (数字)9798350353839

ISBN: (纸本)9798350353846

Age estimation is of vital importance in cattle for various reasons such as health monitoring, breeding, and economic planning. Conventional methods rely on dentition but they are subjective and require considerable expertise, which can lead to inaccuracies. This research aims to address this problem through the use of artificial intelligence by comparing two approaches. In our first approach, bounding box annotation was performed on cattle teeth, and using a YOLOv8 object detection model, we predicted the age class of cattle. For the second approach, individual teeth were segmented to extract morphological features to train machine learning algorithms. Through these methods, this research provides an innovative solution for age estimation through computer vision, which had not been explored before. A demo video of cattle age estimation using dentition can be found here.

关键词： YOLO Training computer vision Machine learning algorithms Accuracy Estimation Teeth Cows Feature extraction Real-time systems

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Accuracy Enhancing Time Limited Model Reduction Framework: Computational Efficiency in Power Systems

Accuracy Enhancing Time Limited Model Reduction Framework: C...

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International Bhurban Conference on Applied Sciences & technology, IBCAST

作者： Muhammad Latif Hira Ambreen Muhammad Imran Samra Zafar Sajid Bashir Department of Electrical Engineering Military College of Signals (MCS) National University of Sciences & Technology (NUST) Islamabad Pakistan Department of Electrical Engineering Sir Syed Center for Advanced Studies in Engineering Institute of Technology (SS-CASE-IT) Islamabad Pakistan Henan University School of Computer and Information Engineering Kaifeng China College of Electrical & Mechanical Engineering NUST Islamabad Pakistan

ISBN: (数字)9798331516680

ISBN: (纸本)9798331516697

The analytical and modelling precision required to build modern wind turbines makes them stable, controlled, and observable. This article presents a state-of-the-art method for lowering the model order of a wind turbine driven by a double-fed induction generator, where the rotational speed varies over time. The suggested approach uses state-space representations to set up a comprehensive and systematic framework for analysing wind turbine performance and checking for compliance with grid standards. A time-constrained Gramians-based novel balanced realization approach is employed to reduce the dimensionality of the large-scale wind turbine model. Stability and reduced approximation error are two benefits of the lower-order system. The proposed method also gives researchers an a priori formula for the error-bound, making computations faster and more efficient. Using substantial simulation data, we show that our method improves upon previous methods in optimising models that include wind turbines with changing rotational speeds, taking into account both stability and error computation. To enhance the efficacy and accuracy of their modeling procedures, academics and researchers analyzing wind turbines should incorporate this new methodology.

关键词： Analytical models Accuracy Uncertainty Computational modeling Stability criteria Velocity control Power system stability Approximation error Wind turbines Computational efficiency

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Numerical simulation to assess floating instability of small passenger vehicle under sub-critical flow 1

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6th International Conference on Civil, Offshore and Environmental engineering, ICCOEE 2020

作者： Al-Qadami, Ebrahim Hamid Hussein Mustaffa, Zahiraniza Martínez-Gomariz, Eduardo Yusof, Khamaruzaman Wan Abdurrasheed, Abdurrasheed S. Shah, syed Muzzamil Hussain Department of Civil and Environmental Engineering Universiti Teknologi PETRONAS Seri Iskandar Perak32610 Malaysia Department of Civil and Environmental Engineering FLUMEN Research Institute Technical University of Catalonia Barcelona Spain Civil Engineering Department Sir Syed University of Engineering and Technology Karachi Pakistan

ISBN: (数字)9789813363113

ISBN: (纸本)9789813363106

Parked vehicles can be directly affected by the floods and at a certain flow velocity and depth, vehicles can be easily swept away. Therefore, studying flooded vehicles stability limits is required. Herein, an attempt has been done to assess numerically the floating instability mode of a small passenger car with a scaled-down ratio of 1:10 using FLOW-3D. The 3D car model was placed inside a closed box and the six degrees of freedom numerical simulation was conducted. Later, numerical results validated experimentally and analytically. Results showed that buoyancy depths were 3.6 and 3.8 cm numerically and experimentally, respectively with a percentage difference of 5.4%. Further, the buoyancy forces were 8.95 N and 8.97 N numerically and analytically, respectively with a percentage difference of 0.2%. With this small difference, it can be concluded that the numerical modeling for such cases using FLOW-3D software can give an acceptable prediction on the vehicle stability limits. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021.

关键词： Floods

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Impact of excessive use of social media on students learning performance: Gratifications theory perspective 1

Impact of excessive use of social media on students learning...

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1st International Visualization, Informatics and technology Conference, IVIT 2022

作者： Nand, Shardha Shaikh Ali, Siti Haryani Universiti Kuala Lumpur Malaysian Institute of Information Technology Kuala Lumpur50250 Malaysia Sir Syed University of Engineering & Technology Department of Computer Science & Information Technology Karachi75190 Pakistan Universiti Kuala Lumpur Edutainment Indust. Revolution 4.0 Res. Cluster Info. and Analytics Sect. Malaysian Inst. of Technol. Malaysia

ISBN: (纸本)9781665486484

Social media is a growing field, mostly everyone is availing the benefits of it. It seems that overuse of this technology may cripple academic standards of our next generations. Nevertheless research studies in this direction is limited, and recent information systems scholars has warranted more research in this direction. The motive of this study is to explore how excessive social media use affects students' academic performance and come up with a suggestion to reduce social media usage and increase educational learning of an individual. Using primary data collected from Pakistani university students, 265 entries were analyzed using SPSS tool. Findings suggest that usage of social networking sites has a significant result on undue use of social media. Furthermore, results also indicated that unwarranted use of social media has a detrimental effect on learning performance. Results also supported the idea that student technology self-efficacy diminishes the undesirable association between excessive social media use and academic performance. © 2022 IEEE.

关键词： Students

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The Hidden Ledger: Understanding Bitcoin's Pseudonymity in Dark Web Transactions

The Hidden Ledger: Understanding Bitcoin's Pseudonymity in D...

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Cyber Resilience (ICCR), International Conference on

作者： Atif Ali Furqan Wali Khattak Zulqarnain Farid Hina Shahid Usama Munir Hamza Muneer Ali Raza Research Management Centre (RMC) Multimedia University Cyberjaye Malaysia Abasyn university Islamabad campus Dept of Criminology University of Karachi Pakistan University of South Asia (USA) Lahore Cantt Sir syed CASE institute of technology Islamabad Department of Computer Science NFC-Instituteot Engineering & Technology Mult an Multan Pakistan University of Gujrat Pakistan

ISBN: (数字)9798350394962

ISBN: (纸本)9798350394979

This study examines the complex relationship between Bitcoin and the Dark Web, two giants who have changed the digital world. Private correspondence and illegal transactions are carried out over the “Dark Web” online. Due to its decentralization and anonymity, Bitcoin is excellent for many covert transactions. We thoroughly analyze Bitcoin's appeal on the Dark Web, how it aids unlawful behavior, and its challenges for international regulatory organizations. The report also examines how this link may affect cybersecurity, law enforcement, and the global economy. Our findings demonstrate the need for revised legal frameworks and proactive methods to address the growing issues caused by the Dark Web and Bitcoin. This abstract summarises the paper's major points and briefly overviews its size and findings.

关键词： Economics Technological innovation Dark Web Online banking Law enforcement Cyberspace Bitcoin

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Density Peaks Clustering Based on Gaussian Fuzzy Neighborhood with Noise Parameter

SSRN

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SSRN 2024年

作者： Waqas, syed Muhammad Khan, Sumra Talpur, Kashif Khan, Rizwan Ahmed Department of Computer Science Muhammad Ali Jinnah University Karachi Pakistan Faculty of Information Technology Department of Computer Science Salim Habib University Karachi Pakistan Department of Science and Engineering Solent University Southampton United Kingdom

Density peak clustering (DPC) is an effective clustering method known for its robustness, non-iterative nature, and hybrid approach. However, it is not without limitations. 1) the determination of the cutoff distance ($d_c$) relies on human experience, which can significantly impact the clustering outcome. 2) DPC does not take into account the local structure of the data when computing local densities. 3) it employs a crisp kernel for density computation. 4) the performance of DPC is affected by chain reactions. 5) DPC often struggles to handle noisy *** order to address these limitations, this paper proposes a novel approach called Density Peak Clustering based on Gaussian Fuzzy Neighborhood with Noise Parameter (DPC-GFNN). The proposed method leverages a Gaussian fuzzy kernel to enhance the separation between clusters and mitigates the influence of outliers through an adjustable noise parameter ($\lambda$). DPC-GFNN utilizes a $k$-nearest neighbor graph based on density to label highly dense regions. This technique effectively avoids chain reactions by assigning accurate labels to points in border areas, enabling proper clustering of data with diverse shapes and *** evaluate the effectiveness of DPC-GFNN, a series of experiments were conducted on both real-world and synthetic datasets. The experimental results demonstrate that DPC-GFNN exhibits superior robustness and clustering accuracy compared to other modified variants of DPC, including DPC-KNN, IDPC, DPC-DBFN, and FKNN. © 2024, The Authors. All rights reserved.

关键词： Iterative methods

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Insurematic : GGernalized framework for Insurance company to Automate Insurance process 2

Insurematic : GGernalized framework for Insurance company to...

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2nd International Conference on Information Science and Communication technology, ICISCT 2020

作者： Shaikh, Muhammad Kashif Palaniappan, Sellappan Khodadadi, Touraj Khurram, Muhammad Khan, Muhammad Hameez Juzer, Huzaifa Malaysia University of Engineering and Technology Department of Information Technology Kuala Lumpur Malaysia Sir Syed University of Engineering and Technology Department of Softwre Engineering Karachi Pakistan

ISBN: (纸本)9781728168999

The insurance industry works on the principle of risk. Customer take out policies based on their assessment of a particularly bad thing happening to them, and insurers offer them cover based on their assessment of the cost of covering any claims. Automobile insurance, also called motor/vehicle insurance, is a contract between the owner or operator of a vehicle and the insurance company, which protects against financial loss incurred through damage to property or persons as the result of an accident or theft. The amount of premium depends on various factors such as insured declared value, type of the vehicle, life of the vehicle, fuel type, age of the insured, and others. Additionally, the type of automotive insurance also depends on the legal principles in effect in each country. Generally, automotive insurance provides coverage for property (damage to or theft of the vehicle), liability (legal responsibility of the owner or operator towards others for body injury or vehicle damage), and medical (cost of treating injuries, rehabilitation, and sometimes lost wages and funeral expenses). Automotive insurance policies are generally issues for the timeframe of six months or one year and are renewable. Therefore, it would benefit everyone if there were a way to more accurately assess risks. In the age of Data Analytics and Data engineering, using vehicle port OBD-II port is being introduced. However, the data which insurers were getting from telematics solution was not meant to be displayed to customers. Whereas with our solution and research each and every detail is supposed to be revealed to its customers and upon performing analysis on that data the behavior of driver can also be determined. The technology is based upon Internet of Things since it involves communication with internet and is a part of Internet of Things domain. Improvement of cutting edge data and correspondence frameworks, huge drop in the innovation cost of possession, and the need for advertise sepa

关键词： Telematics

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Machine Learning-Based Liver Disease Prediction: Enhancing Diagnosis and Prognosis

Machine Learning-Based Liver Disease Prediction: Enhancing D...

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Sustainable technology and engineering (i-COSTE), International Conference on

作者： Priha Bhatti Affan Alim Fahim Uz Zaman syed Akmal Sultan Muhammad Zohaib Khan syed Muhammad Nabeel Mustafa Department of Computer Science Muhammad Ali Jinnah University Karachi Pakistan Department of Computer Science Iqra University Karachi Pakistan Department of Digital Technology Newcastle College University Center (NCUC) Newcastle United Kingdom Department of Orthopedic Surgery Shaheed Mohtarma Benazir Bhutto Institute of Trauma Karachi Pakistan Department of Information Technology Shaheed Mohtarma Benazir Bhutto Institute of Trauma Karachi Pakistan Department of Computer Science & Information Technology NED University of Engineering & Technology Karachi Pakistan

ISBN: (数字)9798331517335

ISBN: (纸本)9798331517342

Recently, liver diseases have emerged as a significant health concern globally, with a rising number of cases attributed to factors such as alcohol consumption, exposure to harmful substances, dietary choices, and certain medications. To address this issue, liver patient datasets were analyzed to develop classification models aimed at predicting liver disorders and reducing the burden on healthcare providers. In this study, we employ machine learning algorithms to conduct a comprehensive assessment of chronic liver disorders, defined by conditions persisting for at least six months. Utilizing patient health indicators, including positive and negative disease status markers, we apply various classification techniques to analyze liver disease prevalence and present the results in a confusion matrix. This research introduces the concept of Liver Disease Prediction (LDP) and evaluates multiple clustering and classification algorithms to improve prediction accuracy. Key algorithms used include KMeans clustering, Logistic Regression (LR), Decision Tree (DT), and Support Vector Machine (SVM). The combination of KMeans with LR and DT demonstrated high accuracy, achieving an accuracy rate of 99.9852 %, while the combination of KMeans and SVM attained 99.9705%. These results suggest that machine learning models, particularly hybrid models, can achieve near-perfect accuracy, enhancing diagnostic support for liver disease prediction.

关键词： Support vector machines Logistic regression Accuracy Machine learning algorithms Liver diseases Clustering algorithms Medical services Predictive models Prediction algorithms Classification algorithms

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