Article
Engineering, Electrical & Electronic
Mahmoud A. Albreem, Markku Juntti, Shahriar Shahabuddin, Saeed Abdallah, Alaa Alhabbash, Eqab Almajali
Summary: Massive multiple-input multiple-output (MIMO) is a crucial technology in modern wireless communication systems. This paper investigates the performance and computational complexity of matrix decomposition based detectors in realistic channel scenarios for various massive MIMO configurations. The study compares data detectors based on decomposition algorithms with approximate-inversion detection (AID) methods. The results demonstrate the promising performance and stability of the alternating-direction-method-of-multipliers-based-Infinity-Norm (ADMIN) detection, even in environments with a small ratio of base-station (BS) antenna elements to the number of users. Additionally, this research examines the performance of several detectors in imperfect channel state information (CSI) and correlated channels. It provides valuable insights for the selection of appropriate massive MIMO detectors by massive MIMO systems and very large-scale integration (VLSI) designers based on their specifications.
PHYSICAL COMMUNICATION
(2023)
Article
Engineering, Electrical & Electronic
Mahtab Ataeeshojai, Robert C. Elliott, Witold A. Krzymien, Chintha Tellambura, Ivo Maljevic
Summary: Cell-free massive multiple-input multiple-output (mMIMO) systems, which distribute a large number of access points over the coverage area to serve users jointly, require efficient methods to calculate the precoding matrix. This study examines several iterative methods and proposes the hyper-power iterative inversion method, which demonstrates fast convergence, high accuracy, and strong numerical stability. The hyper-power method is a promising candidate for matrix inversion in cell-free mMIMO system precoders.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2022)
Review
Computer Science, Information Systems
Mahmoud A. Albreem, Alaa Alhabbash, Ammar M. Abu-Hudrouss, Tarik Adnan Almohamad
Summary: This paper provides insights on data detection techniques for decentralized and distributed massive MIMO (M-MIMO) networks. It discusses different detection techniques based on various architectures and presents their performance, complexity, throughput, and latency profiles. The role of expectation propagation algorithm (EPA) in decentralized architectures is comprehensively reviewed. The paper also illustrates the energy efficiency of several decentralized M-MIMO architectures and discusses the challenges and future research directions in this field.
COMPUTER COMMUNICATIONS
(2022)
Article
Engineering, Electrical & Electronic
Xiaohui Zhang, Huacheng Zeng, Baofeng Ji, Gaoyuan Zhang
Summary: In this paper, a low-complexity MIMO detection method based on the Neumann series is proposed, which reduces the computational complexity of signal detection by leveraging the statistical information of the Gram matrix and transforming matrix operations.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2022)
Article
Computer Science, Information Systems
Salah Berra, Sourav Chakraborty, Rui Dinis, Shahriar Shahabuddin
Summary: This paper discusses the use of accelerated Chebyshev SOR (AC-SOR) and accelerated Chebyshev AOR (AC-AOR) algorithms to improve the performance of conventional Successive Over-Relaxation (SOR) and Accelerated Over-Relaxation (AOR) methods in massive MIMO systems. Additionally, a deep unfolding network (DUN) is proposed to optimize the parameters of the iterative AC-SOR and AC-AOR algorithms, leading to the AC-AORNet and AC-SORNet methods. The results show that the proposed DUN-based methods outperform other state-of-the-art algorithms, especially for high-order modulations such as 256-QAM.
Article
Computer Science, Information Systems
Mahmoud A. Albreem, Khawla A. Alnajjar, Ali J. Almasadeh
Summary: Massive MIMO is a key technology in 5G and beyond 5G communication systems, used to meet high capacity demands. This paper proposes low complexity massive MIMO data detection techniques based on ZF and V-BLAST methods, using approximate matrix inversion techniques and stair matrix initialization to balance performance and complexity. Additionally, a massive MIMO detector based on approximate matrix inversion, stair matrix initialization, and deep learning is presented. Simulation results show significant improvement in performance and notable reduction in computational complexity compared to conventional methods in both simple and real channel scenarios.
Article
Engineering, Electrical & Electronic
Zheng Wang, Robert M. Gower, Cheng Zhang, Shanxiang Lyu, Yili Xia, Yongming Huang
Summary: This paper introduces the random iterative method to massive MIMO systems for efficient downlink linear precoding. By incorporating random sampling into traditional iterative methods, the matrix inversion in linear precoding schemes can be statistically approximated, resulting in faster convergence with low complexity and global convergence without convergence requirements. The paper proposes the randomized iterative precoding algorithm (RIPA) and shows that its approximation error decays exponentially and globally with the number of iterations. Furthermore, the concept of conditional sampling is introduced to optimize and enhance the convergence and efficiency of the randomized iterations. The modified randomized iterative precoding algorithm (MRIPA) is then presented based on the equivalent iteration transformation, achieving better precoding performance with low complexity for various scenarios of massive MIMO. Simulation results demonstrate the system gains of RIPA and MRIPA in terms of performance and complexity in downlink precoding for massive MIMO systems.
IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS
(2022)
Article
Engineering, Electrical & Electronic
Huizheng Wang, Yahui Ji, Yifei Shen, Wenqing Song, Muhao Li, Xiaohu You, Chuan Zhang
Summary: This paper proposes a two-level and block diagonal based improved Neumann series approximation (TL-BD-INSA) algorithm, suitable for various channel conditions. The algorithm significantly reduces computational cost and achieves error-rate performance only 0.25 dB away from the exact method in non-ideal channel scenarios.
IEEE TRANSACTIONS ON SIGNAL PROCESSING
(2021)
Article
Computer Science, Information Systems
Jun-Yong Jang, Won-Seok Lee, Jae-Hyun Ro, Young-Hawn You, Hyoung-Kyu Song
Summary: A novel precoding scheme based on the Gauss-Seidel method is proposed for downlink massive MIMO systems, with a weighted GS method being introduced to improve approximation accuracy and reduce complexity.
CMC-COMPUTERS MATERIALS & CONTINUA
(2021)
Article
Computer Science, Information Systems
Daniele Pinchera, Mario Lucido, Gaetano Chirico, Fulvio Schettino, Marco Donald Migliore
Summary: This paper proposes the use of a controllable local propagation environment to enhance the capability of wireless propagation channels in beyond-5G systems. The authors analyze its positive effect on the multiplexing capability of massive MIMO systems and show through numerical simulations that the proposed system outperforms its non-reconfigurable counterpart in terms of the number of contemporary connected users. Furthermore, the optimized system ensures higher channel fairness by substantially increasing the minimum received power by the terminals.
Article
Engineering, Electrical & Electronic
Haifan Yin, David Gesbert
Summary: This paper addresses the challenge of acquiring channel state information (CSI) in Frequency Division Duplex (FDD) massive MIMO. A novel CSI feedback framework is proposed based on the partial reciprocity of uplink and downlink channels in the wideband regime. The paper derives the rank expression of the wideband massive MIMO channel covariance matrix and identifies a low-rankness property. A partial channel reciprocity (PCR) codebook scheme is proposed, which outperforms the latest codebook in 5G in terms of performance, complexity, and feedback requirements. Simulations with practical channel models demonstrate the significant gains and practicality of the proposed methods for 5G and beyond.
IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS
(2022)
Article
Computer Science, Information Systems
Hyun-Sun Hwang, Jae-Hyun Ro, Chan-Yeob Park, Young-Hwan You, Hyoung-Kyu Song
Summary: In this paper, the authors propose a horizontal Gauss-Seidel (HGS) precoding scheme for massive MIMO systems. The HGS method achieves better performance and reduced required time through parallel computation and the use of ordered channel matrix.
CMC-COMPUTERS MATERIALS & CONTINUA
(2022)
Article
Computer Science, Interdisciplinary Applications
Rishika Chauhan, Shefali Sharma, Rahul Pachauri
Summary: This research focuses on the modeling and comparative investigation of signal detection algorithms in uplink massive MIMO systems. The goal is to achieve optimal error rate performance with low complexity. Through simulation-based comparisons, different detection algorithms were evaluated and their performance was analyzed under various MIMO scenarios. The results showed that the BOX equalization detector provided the best performance.
ADVANCES IN ENGINEERING SOFTWARE
(2022)
Article
Computer Science, Information Systems
Mushtaq Ahmad, Xiaofei Zhang, Imran A. Khoso, Xinlei Shi, Yang Qian
Summary: This paper addresses the challenge of signal detection in uplink massive MIMO systems and proposes an improved Gauss-Seidel iteration algorithm to enhance detection performance and reduce computational load.
Article
Telecommunications
Lin Li, Jianhao Hu
Summary: A novel limited-memory BFGS (L-BFGS) scheme was proposed for MMSE detection in massive MIMO systems, which significantly reduces storage and computation cost compared to the BFGS method. Simulation results confirmed the effectiveness of the proposed scheme.
IEEE COMMUNICATIONS LETTERS
(2022)
Article
Green & Sustainable Science & Technology
Kanchana Kadirvel, Raju Kannadasan, Mohammed H. Alsharif, Zong Woo Geem
Summary: Electric vehicles are becoming highly preferred in domestic transportation systems due to the increasing demand and cost of fuel. Companies like Tesla, BMW, Audi, and Mercedes have entered the market with their EV offerings. This study proposes an improved interleaved phase-shifted semi-bridgeless boost converter for EV battery charging, which achieves faster charging speed and increased current injection compared to conventional converters.
Review
Robotics
Syed Agha Hassnain Mohsan, Nawaf Qasem Hamood Othman, Yanlong Li, Mohammed H. H. Alsharif, Muhammad Asghar Khan
Summary: Unmanned aerial vehicles (UAVs) or drones are widely used in various fields such as economy, commerce, leisure, military and academics. Despite facing limitations, UAVs show great potential and offer a range of features and capabilities, along with challenges and security issues. This study provides valuable insights into the potentials and characteristics of UAVs, along with future research directions.
INTELLIGENT SERVICE ROBOTICS
(2023)
Article
Computer Science, Information Systems
Arun Kumar, J. Venkatesh, Nishant Gaur, Mohammed H. Alsharif, Abu Jahid, Kannadasan Raju
Summary: More spectrum bands are needed due to the increase in wireless applications. However, adapting the spectrum bands to new applications has become difficult, leading to a crowded spectrum and decreased quality of service. Cognitive radio, specifically spectrum sensing, is a promising technology to address this issue. In this study, a novel 5G spectrum sensing technique using a hybrid matched filter algorithm was implemented, and its performance was compared to traditional matched filtering in different channels. The hybrid matched filter demonstrated superior effectiveness in both Rayleigh and Rician channels.
Article
Energy & Fuels
Manish Kumar Singla, Jyoti Gupta, Mohammed H. Alsharif, Abu Jahid
Summary: This article presents a cost-effective and reliable solution for meeting the energy demands of remote areas through the integration of multiple renewable energy sources. The proposed system aims to reduce dependence on fossil fuels and promote sustainable development by utilizing accessible energy resources in a self-contained microgrid. The study used HOMER software to optimize the combination of energy sources and storage technologies, and identified that the solar PV/fuel cell combination is more cost-effective, reliable, and efficient compared to the solar PV/battery combination. The proposed IHRES model offers a promising solution for meeting energy demands in remote areas while reducing dependence on fossil fuels and promoting sustainable development.
Article
Energy & Fuels
Gopu Venugopal, Arun Kumar Udayakumar, Adhavan Balashanmugham, Mohamad Abou Houran, Faisal Alsaif, Rajvikram Madurai Elavarasan, Kannadasan Raju, Mohammed H. Alsharif
Summary: This article proposes a technique for detecting and categorizing interturn insulation problems in variable-speed motor drives. It combines Salp Swarm Optimization (SSO) with Recurrent Neural Network (RNN) to identify and label the insulation concerns. The proposed technique improves the system precision by simplifying the detection and classification process. The model is implemented in MATLAB/Simulink and various metrics are analyzed for evaluation.
Article
Engineering, Electrical & Electronic
Saeed Abdallah, Zeno Verboven, Mohamed Saad, Mahmoud A. Albreem
Summary: Ambient backscatter communication (AmBC) is a promising technology for low-cost, low-power devices in the Internet-of-Things (IoT) applications. This paper addresses the challenging channel estimation problem for full-duplex multi-antenna AmBC systems. Three solutions are proposed, including a pilot-based maximum likelihood (ML) estimator, a semi-blind estimator based on the expectation maximization (EM) framework, and a semi-blind estimator based on the decision-directed (DD) strategy. Simulations demonstrate the accuracy and performance superiority of the semi-blind estimators compared to the ML estimator.
IEEE TRANSACTIONS ON COMMUNICATIONS
(2023)
Article
Engineering, Marine
Abdul Razzaq, Syed Agha Hassnain Mohsan, Yanlong Li, Mohammed H. Alsharif
Summary: This study presents an architectural framework and algorithms as a solution for designing and developing an IoUT system. It includes recommendations and forecasts for potential partners in the smart ocean. A case study is implemented to assess the system's usability and agility in exploiting sensor data, executing algorithms, and querying outputs for performance evaluation.
JOURNAL OF MARINE SCIENCE AND ENGINEERING
(2023)
Article
Multidisciplinary Sciences
Manish Kumar Singla, Jyoti Gupta, Parag Nijhawan, Mohammed H. Alsharif, Mun-Kyeom Kim
Summary: A new optimum approach is presented for estimating the unknown parameters of a DMFC stack using a mathematical model. The proposed method minimizes the Sum of Squared Errors based on experimental data and is compared to other metaheuristic algorithms. The results show that the proposed algorithm outperforms the other algorithms.
Review
Energy & Fuels
Manish Kumar Singla, Jyoti Gupta, Parag Nijhawan, Amandeep Singh Oberoi, Mohammed H. H. Alsharif, Abu Jahid
Summary: This manuscript provides a comprehensive review of unitized regenerative fuel cells (URFCs) and their significance in Remote Area Power Supply (RAPS). URFCs offer a potential solution to reduce the expenses of solar hydrogen renewable energy systems in RAPS by combining the functionalities of the electrolyzer and fuel cell into a single unit. They are particularly well-suited for RAPS applications as the electrolyzer and fuel cell do not need to operate simultaneously.
Article
Energy & Fuels
Subhojit Dawn, Gummadi Srinivasa Rao, M. L. N. Vital, K. Dhananjay Rao, Faisal Alsaif, Mohammed H. Alsharif
Summary: Profit maximization is crucial in power system control, especially in renewable-associated power systems. The utilization of V2G technology, using vehicles as mobile storage devices during off-peak hours, can enhance system profitability and have an impact on system voltage profiles and pricing.
Article
Engineering, Electrical & Electronic
Mohamed Saad, Saeed Abdallah, Mahmoud A. Albreem
Summary: This article addresses the joint spectrum-efficient routing and power allocation problem in wireless networks of passive tags, proving that the problem is NP-complete. The study introduces polynomially solvable lower bounds, special cases, and heuristics to solve the original NP-complete problem. Experimental results show an average reduction of 16.1 dB in ambient transmit power with algorithm performance comparable to a pruned exhaustive search benchmark.
IEEE SENSORS JOURNAL
(2023)
Review
Chemistry, Analytical
Annisa Anggun Puspitasari, To Truong An, Mohammed H. Alsharif, Byung Moo Lee
Summary: This study is of great significance in applying ML algorithms and their derivatives to optimize emerging 6G technologies and meet the visions and requirements of the 6G network.
Article
Engineering, Multidisciplinary
Arun Kumar, Aziz Nanthaamornphong, R. Selvi, J. Venkatesh, Mohammed H. Alsharif, Peerapong Uthansakul, Monthippa Uthansakul
Summary: Smart Hospital will play a key role in improving the quality of healthcare services. Integrating technology with traditional hospitals allows for remote access to medical facilities, saving time and improving efficiency. This research focuses on enhancing power savings, spectral access, latency, and Bit error rate of 5G waveforms, while also examining the role of IoT and AI in smart hospitals.
ALEXANDRIA ENGINEERING JOURNAL
(2023)
Article
Engineering, Electrical & Electronic
Shanmugavadivu Natarajan, Raju Kannadasan, Faisal Alsaif, Mohammed H. Alsharif
Summary: This paper presents a modified double-ended forward converter (DEFC) designed for stepper motor-based robotic applications. The proposed converter topology provides galvanic isolation between the input and output, along with higher efficiency and a smooth operative system. The control strategy for the converter utilizes Proportional Integral (PI) to regulate output voltage and current, implemented using a microcontroller-based system. Experimental results demonstrate the effectiveness of the proposed topology and control strategy, making it a promising option for future robotic systems.
Article
Computer Science, Information Systems
Ashish Bagwari, J. Logeshwaran, K. Usha, Kannadasan Raju, Mohammed H. Alsharif, Peerapong Uthansakul, Monthippa Uthansakul
Summary: Industrial wireless sensor networks (WSNs) are popular due to scalability and low cost, but face challenges in energy optimization and network maintenance. Machine Learning techniques are used to create an enhanced energy optimization model, identifying and optimizing node energy consumption and predicting optimal outcomes.
