International Journal of Electrical and Computer Engineering Systems

Reconfigurable Intelligent Surfaces in 6G mMIMO NOMA Networks: A Comprehensive Analysis

Tue, 21 Jan 2025 00:00:00 +0100

As the features and characteristics of six-generation (6G) connectivity are defined, advanced technologies such as multiple-input, multiple-output (mMIMO), non-orthogonal multiple access (NOMA), and reconfigurable intelligent surfaces (RISs) are becoming more important for many Internets of Things (IoT) uses. This study comprehensively and uniquely investigates the impact of RIS on the effectiveness of NOMA download (DL) mMIMO systems in the IoT environment within the context of the 6G network. This work aims to analyze the impact of including the RIS in the spectral efficiency (SE) and capacity performance of proposed hybrid system-enabled IoT setting device distributions, such as clustered and hotspot configurations. It highlights the ability of RIS to optimize wireless latency communication and throughput, depending on the mobility and density of IoT devices, respectively. The proposed methodologies are assessed through a simulation software application, under unstable channel conditions with varying distances and power locations while accounting for 256-quadrature amplitude modulation (256-QAM), frequency selective Rayleigh fading, and successive interference cancellation (SIC) context inside the 6G network environment. The results indicate that the four IoT groups (50, 100, 150, and 200) achieved capacity improvements of 5.84%, 5.81, 5.78, and 5.8%, and SE increases of 5.759%, 5.755%, 5.753%, and 5.84%, respectively, when utilizing RIS compared to their performance without it. The implementation of RIS yielded latency rate enhancements of 16.44%, 12.24%, 9.75%, and 8.1% across all four IoT groups, respectively, at a mobility speed of 120 Km/h. Each of the four IoT groups had throughput enhancements of 26%, 25.6%, 25.3%, and 25%, respectively, while using RIS within a coverage area of 400 square meters (sqm).

Design and Implementation of a Novel 5G Hairpin Bandpass Filter with Defected Ground Structure

Shereen Abdalkadum Shandal — Tue, 21 Jan 2025 00:00:00 +0100

In this paper, a three-pole hairpin resonator is designed, simulated, and fabricated on the top layer of the FR4 substrate. Recent trends in miniature size and improved filter performance, particularly in terms of scattering parameters and wider bandwidth, have increased demand for such filters. This filter uses two different Defect Ground Structure (DGS) techniques utilizing the top and ground layers. The first Defect Ground Structure (DGS) technique incorporates two dumbbells and rectangular slots beneath two feed lines, resulting in a unique and modified bandpass filter design. In the second DGS, a series of grooves embedded at three hairpin resonators provide a more compact size and enhanced scattering parameters with wider bandwidth, which is considered an improvement of this design over the existing works. The simulation results use High Frequency Structure Simulator (HFSS) software. Parametric optimization has been conducted; the optimized values of three significant parameters are 4mm length of tap Lt, 0,4mm space between resonators S, and (3×9)mm2 area of rectangular slot (DGS2). The presented filter resonates at 2.5 GHz center frequency with a -3dB fractional bandwidth of 22.4%. The acquired values of insertion loss (S21) and return loss (S11) at the passband are -1.6dB and -54.19dB, respectively, with a flat group delay. The design validity has been verified using Computer Simulation Technology (CST) simulation software and a fabricated prototype. The fabrication results match the simulations excellently, making the suggested filter suitable for various fifth-generation (5G) applications.

Performance Enhancement in OFDM System Using Preamble-Based Time Domain SNR Estimation

Shahid Manzoor, Noor Shamsiah Othman — Mon, 27 Jan 2025 00:00:00 +0100

This work proposes a time domain signal-to-noise ratio (SNR) estimator for a single input-single output (SISO) orthogonal frequency division multiplexing (OFDM) system using a pre-fast Fourier transform (pre-FFT) SNR estimator. The pre-FFT SNR estimator requires no additional overhead since it reuses the preamble for synchronization in the OFDM system. In this work, a preamble structure proposed by Morelli and Mengali to overcome carrier frequency offset (CFO) due to Doppler effects is utilized. The proposed pre-FFT SNR estimator has been employed to estimate SNR for the SISO-OFDM system, and its performance has been evaluated against the corresponding frequency domain SNR estimator, also known as a post-FFT SNR estimator. The normalized mean square error (NMSE) of the pre-FFT SNR estimator has also been evaluated against the normalized Cramer-Rao bound (NCRB). The simulation results show that for the additive white Gaussian noise (AWGN) and Stanford University Interim-5 (SUI-5) channels, the pre-FFT SNR estimator exhibits 0.41 dB and 0.66 dB difference between the estimated SNR and the actual SNR, respectively. The NMSE of the pre-FFT SNR estimator outperforms the benchmarker post-FFT SNR estimator, which is close to the NCRB. The proposed pre-FFT SNR estimator achieved bit error rate (BER) improvements of about 1 dB and 2 dB for AWGN and SUI-5 channels, respectively, over the post-FFT SNR estimator at BER= 10−4. Moreover, there is an approximately 50% reduction in complexity between the proposed pre-FFT SNR estimator and the benchmarker post-FFT SNR estimator.

Multipath Routing Algorithm to find Optimal Path in SDN with POX Controller

Deepthi Goteti, Imran Rasheed — Wed, 29 Jan 2025 00:00:00 +0100

The past decade witnessed a tremendous increase in network usage, and traditional network architecture is needed to sustain modern requirements with high throughput and minute delay. This leads to the introduction of software-defined networks. Congestion is a critical problem that needs attention, so identifying the optimal path is required to eliminate the congestion. Researchers introduce rigorous studies to identify optimal paths, some resulting in less data loss and delay. Identifying multiple paths between nodes may eliminate congestion. When the first best path is congested, selecting the second best path between nodes can solve the congestion problem. With this ideology, the multipath routing algorithm is developed and tested on Fat Tree, Custom, and Tree topologies, and performance is measured using quality of service factors. Considering Throuhput, Fat Tree produced 27.15% better throughput than the tree topology and 17.57% better than the custom topology, Whereas in the case of jitter, fat tree topology reduces jitter by about 90.36%compared to the tree topology, but custom topology reduces jitter by about 12.24% compared to the fat-tree topology. In the packet delivery ratio, fat tree topology reduces packet loss by about 77.87% compared to the tree topology. Fat tree topology reduces packet loss by about 55.62% compared to the custom topology. Fat tree performs best overall, with the highest throughput, lowest packet loss, and significantly reduced jitter compared to the tree and custom topologies.MiniNet is used to perform simulations. TCP and UDP flows are calculated with the iperf tool and tested on the POX Controller.

Enhanced Patch-wise Maximal Intensity Prior for Deblurring Neutron Radiographic Images

K. Yazid, H. Ibrahim, Mohd Zaid Abdullah — Mon, 03 Feb 2025 00:00:00 +0100

In neutron radiographic imaging, generally, the collimation ratio is assumed to be sufficiently large to ensure a valid approximation for parallel beam geometry. However, this assumption is difficult to apply in small nuclear reactors due to the low- intensity neutron flux. For this reason, these reactors produced inherently blurry neutron images. In this paper a blind deconvolution technique is investigated for the enhanced visual quality of neutron images through the reduction of blurring artefacts. Technically, this approach is extremely challenging because it requires an unknown point spread function. To solve this problem, scholars employ the gradient minimization strategy under the framework of a maximum a posterior, which leads to the development of an improved deblurring method, referred to in this paper as the enhanced patch-wise intensity prior. Experimental results demonstrate that the high competitiveness of the proposed method in terms of blind or no-reference evaluation measure, with an average of 46.1 for six neutron images used in this study. This value is considerably lower compared with those of existing deblurring techniques, which implies a more accurate restoration. Additionally, the proposed method resulted in the highest, and hence, the best entropy and contrast values, averaging at 7.09 and 1.05 respectively. The proposed method is also the second fastest technique witd mean time of 180 s.

Breast Pathology Changes Extraction and Measurement Based on Machine Learning and DWT

Sahar Shakir, Yousif A. Hamad, Rehab Kareem — Mon, 03 Feb 2025 00:00:00 +0100

In recent years, medical image analysis has witnessed significant advancements in aiding accurate diagnosis and treatment planning. Breast tumor segmentation is a critical task in medical imaging, as it facilitates the identification and characterization of tumors for effective clinical decisions. This paper proposes a novel approach for breast tumor segmentation and analysis by integrating Fuzzy C-Means Clustering (FCM) with Discrete Wavelet Transform (DWT), called FCMDWT. This method is effective in breast diagnosis analysis, tumor size measurements, and diagnosing reports and does not require prior training in segmentation. Initially, the DWT is applied to the mammography image, decomposing it into different frequency subbands. FCM is employed on the DWT coefficients to ensure robust clustering by accommodating uncertainty and overlapping regions in the image. The experimental evaluation conducted on a comprehensive dataset and comparative analyses demonstrates the superiority of the FCMDWT approach. Furthermore, the proposed method extends beyond segmentation, incorporating tumor analysis by extracting relevant features such as size, shape, and texture. The results indicate the potential of the FCMDWT approach in not only accurate segmentation but also in providing valuable insights for clinical decision-making.

Asphalt Pavement Distress Detection by Transfer Learning with Multi-head Attention Technique

Ahmed Bahaaulddin A. Alwahhab, Vian Sabeeh, Ali Abdulmunim Ibrahim Al-kharaz — Thu, 06 Feb 2025 00:00:00 +0100

Roads and highways represent a crucial lifeline between communities in all countries. They have to be healthy enough for safe and effective transportation. The traditional ways of inspecting roads by human inspectors consume time, and the inspection results may be subjective. For this reason, researchers are motivated to automate pavement distress detection to help the road monitoring and maintenance process. Additionally, many researchers have tried to present models to detect distress on road infrastructure. However, these models face accuracy challenges and overfitting because of the nature and complications of distress images. This paper proposes a model that combines pre-trained VGG16 with a multi-head attention layer. The proposed paradigm began with smoothing as a pre-processing step to eliminate the granular effect of the asphalt gravel and make asphalt damage more distinct. Then, data augmentation was conducted to improve model generalization by adding various distress scenes to the dataset in geometric, color, and intensity cases. This work also contributes to the broader body of research by collecting a local dataset that contains three types of asphalt distress (cracks, potholes, and ruts). The proposed model was tested using three benchmarked datasets in addition to the locally collected one, and it showed efficiency in detecting asphalt distress using offline and real-time images. The model achieved an accuracy 1.00 in the Pavmentscapes dataset, outperforming the UNET model, and a fully connected network was trialed with the same dataset. With the Deep Crack dataset, our model scored an accuracy of 1.00. In contrast, ResNet achieved an accuracy of 0.72 on the same dataset. The NHA12D dataset was also used to test the proposed model and achieved an accuracy of 1.00, but the VGG16 without an attention layer used on that dataset scored only 0.64. All previous obvious tests prove that the proposed VGG16 and multi-head attention paradigm outperform the earlier models. Additionally, the proposed model has undergone a real-time test on local roads. The future directions are to try to make the self-attention mechanism more explainable and implement an attention layer for multi-scales.

Applying Artificial Intelligence Techniques For Resource Management in the Internet of Things (IoT)

Salwa Othmen, Wahida Mansouri, Radhia Khdhir — Tue, 03 Dec 2024 00:00:00 +0100

Internet of Things (IoT) applications in smart cities (SCs) rely on free-flow services streamlined by artificial intelligence (AI) paradigms. However, the nature of resource constraint prevails due to external infrastructure costs and energy-based allocations. Existing approaches to smart city resource distribution rely on static thresholds or reactive responses, which are not always sufficient. These approaches may limit system performance and scalability in dynamic IoT environments owing to increased energy consumption, postponed resource allocation, and frequent device failures. This article introduces a Concerted Resource Management (CRM) using the Leveled Reinforcement Training (LRT) method. The proposed method accurately identifies cost-complex and high energy-consuming sharing intervals based on service response time and device failure. The reinforcement learning and training concerts both energy and device incorporations for SC applications based on its demand. This process requires leveled training in resource management, from energy depletion to device activeness. The interrupted sessions are identified using resource allocation failures, and the active resources with optimal energy expenses are selected to pursue resource management. The training method thus identifies the demands based on independent or concerted resource allocations to mitigate the management constraints in an SC environment. This proposed method reduces the resource constraint-based waiting for allocations and allocation failures in any SC application services. Under the varying devices, the following is observed: Improvements: 9.1% (Allocation Rate), 10% (Device Detection), 11.88% (Constraint Mitigation— Energy), 9.06% (Constraint Mitigation—Resource Allocation); Reduced: 8.01% (Allocation Failure), 9.64% (Waiting Time).