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Fulltext Efficient models for enhancing the link adaptation performance of LTE/LTE-A networks

Bin-Salem AA, Wan TC, Naeem H, Anbar M, Hanshi SM, Redjaimia A

EURASIP J Wirel Commun Netw, 2022;2022(1):10.
PMID: 35132311 DOI: 10.1186/s13638-022-02091-w

Link adaptation (LA) is the ability to adapt the modulation scheme (MS) and the coding rate of the error correction in accordance with the quality of the radio link. The MS plays an important role in enhancing the performance of LTE/LTE-A, which is typically dependent on the received signal to noise ratio (SNR). However, using the SNR to select the proper MSs is not enough given that adaptive MSs are sensitive to error. Meanwhile, non-optimal MS selection may seriously impair the system performance and hence degrades LA. In LTE/ LTE-A, the LA system must be designed and optimized in accordance with the characteristics of the physical (e.g., MSs) and MAC layers (e.g., Packet loss) to enhance the channel efficiency and throughput. Accordingly, this study proposes using two LA models to overcome the problem. The first model, named the cross-layer link adaptation (CLLA) model, is based on the downward cross-layer approach. This model is designed to overcome the accuracy issue of adaptive modulation in existing systems and improve the channel efficiency and throughput. The second model, named the Markov decision process over the CLLA (MDP-CLLA) model, is designed to improve on the selection of modulation levels. Besides that, our previous contribution, namely the modified alpha-Shannon capacity formula, is adopted as part of the MDP-CLLA model to enhance the link adaptation of LTE/LTE-A. The effectiveness of the proposed models is evaluated in terms of throughput and packet loss for different packet sizes using the MATLAB and Simulink environments for the single input single output (SISO) mode for transmissions over Rayleigh fading channels. In addition, phase productivity, which is defined as the multiplication of the total throughput for a specific modulation with the difference between adjacent modulation SNR threshold values, is used to determine the best model for specific packet sizes in addition to determine the optimal packet size for specific packet sizes among models. Results generally showed that the throughput improved from 87.5 to 89.6% for (QPSK → 16-QAM) and from 0 to 43.3% for (16-QAM → 64-QAM) modulation transitions, respectively, using the CLLA model when compared with the existing system. Moreover, the throughput using the MDP-CLLA model was improved by 87.5-88.6% and by 0-43.2% for the (QPSK → 16-QAM)and (16-QAM → 64-QAM) modulation transitions, respectively, when compared with the CLLA model and the existing system. Results were also validated for each model via the summation of the phase productivity for every modulation at specific packet sizes, followed by the application one-way analysis of variance (ANOVA) statistical analysis with a post hoc test, to prove that the MDP-CLLA model improves with best high efficiency than the CLLA model and the existing system.
Fulltext Field-Dependent Pollution Model under Polluted Environments for Outdoor Polymeric Insulators

Ahmed R, Abd Rahman R, Jamal A, Salem AA, Saman B, Lau KY, et al.

Polymers (Basel), 2022 Jan 27;14(3).
PMID: 35160504 DOI: 10.3390/polym14030516

In-depth understanding of the pollution problems such as dry bands and the polymeric aging process requires better determination of electric field strength and its distribution over the polymeric surface. To determine the electric field distribution over the insulator surface, this research proposes utilizing a novel approach model based on nonlinear electrical characteristics derived from experimental results for polluted polymer insulators. A case study was carried out for a typical 11 kV polymeric insulator to underline the merits of this new modeling approach. The developments of the proposed pollution model and the subsequent computational works are described in detail. The study is divided into two main stages; laboratory measurements and computer simulations. In the first stage, layer conductance tests were carried out to develop nonlinear field-dependent conductivity for the pollution modeling. In the second part, equipotential and electric field distributions along the leakage were computed using the finite element method (FEM). Comparative field studies showed that the simulation using the proposed dynamic pollution model results in more detailed and realistic field profiles around insulators. This may be useful to predict the formation of dry bands and the initiation of electrical discharges on the polymeric surface.
Fulltext Role of deep learning in early detection of COVID-19: Scoping review

Alzubaidi M, Zubaydi HD, Bin-Salem AA, Abd-Alrazaq AA, Ahmed A, Househ M

Comput Methods Programs Biomed Update, 2021;1:100025.
PMID: 34345877 DOI: 10.1016/j.cmpbup.2021.100025

BACKGROUND: Since the onset of the COVID-19 pandemic, the world witnessed disruption on an unprecedented scale affecting our daily lives including but not limited to healthcare, business, education, and transportation. Deep Learning (DL) is a branch of Artificial intelligence (AI) applications, the recent growth of DL includes features that could be helpful in fighting the COVID-19 pandemic. Utilizing such features could support public health efforts.
OBJECTIVE: Investigate the literature available in the use of DL technology to support dealing with the COVID-19 crisis. We summarize the literature that uses DL features to analyze datasets for the purpose of a quick COVID-19 detection.
METHODS: This review follows PRISMA Extension for Scoping Reviews (PRISMA-ScR). We have scanned the most two commonly used databases (IEEE, ACM). Search terms were identified based on the target intervention (DL) and the target population (COVID-19). Two authors independently handled study selection and one author assigned for data extraction. A narrative approach is used to synthesize the extracted data.
RESULTS: We retrieved 53 studies and after passing through PRISMA excluding criteria, only 17 studies are considered in this review. All studies used deep learning for detection of COVID-19 cases in early stage based on different diagnostic modalities. Convolutional Neural Network (CNN) and Transfer Learning (TL) were the most commonly used techniques.
CONCLUSION: The included studies showed that DL techniques has significant impact on early detection of COVID-19 with high accuracy rate. However, most of the proposed methods are still in development and not tested in a clinical setting. Further investigation and collaboration are required from the research community and healthcare professionals in order to develop and standardize guidelines for use of DL in the healthcare domain.
Fulltext High-efficiency electromagnetic energy harvesting using double-elliptical metasurface resonators

Amer AAG, Othman N, Sapuan SZ, Alphones A, Salem AA

PLoS One, 2023;18(12):e0291354.
PMID: 38127949 DOI: 10.1371/journal.pone.0291354

This study introduces a metasurface (MS) based electrically small resonator for ambient electromagnetic (EM) energy harvesting. It is an array of novel resonators comprising double-elliptical cylinders. The harvester's input impedance is designed to match free space, allowing incident EM power to be efficiently absorbed and then maximally channelled to a single load through optimally positioned vias. Unlike the previous research works where each array resonator was connected to a single load, in this work, the received power by all array resonators is channelled to a single load maximizing the power efficiency. The performance of the MS unit cell, when treated as an infinite structure, is examined concerning its absorption and harvesting efficiency. The numerical results demonstrate that the MS unit cell can absorb EM power, with near-perfect absorption of 90% in the frequency range of 5.14 GHz to 5.5 GHz under normal incidence and with a fractional bandwidth of 21%. The MS unit cell also achieves higher harvesting efficiency at various incident angles up to 60o. The design and analysis of an array of 4x4 double elliptical cylinder MS resonators integrated with a corporate feed network are also presented. The corporate feed network connects all the array elements to a single load, maximizing harvesting efficiency. The simulation and measurement results reveal an overall radiation to AC efficiency of about 90%, making it a prime candidate for energy harvesting applications.