Browsing by Author "Jabire, Adamu Halilu"

Now showing 1 - 20 of 27
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    Application of Deep Learning and WT-SST in Localization of Epileptogenic Zone Using Epileptic EEG Signals
    (MDPI, 2022-05-11) Saminu, Sani; Xu, Guizhi; Zhang, Shuai; Abd El Kader, Isselmou; Jabire, Adamu Halilu; Ahmed, Yusuf Kola; Karaye, Ibrahim Abdullahi; Ahmad, Isah Salim
    Focal and non-focal Electroencephalogram(EEG) signals have proved to be effective techniques for identifying areas in the brain that are affected by epileptic seizures, known as the epileptogenic zones. The detection of the location of focal EEG signals and the time of seizure occurrence are vital information that help doctors treat focal epileptic seizures using a surgical method. This paper proposed a computer-aided detection (CAD) system for detecting and classifying focal and non-focal EEG signals as the manual process is time-consuming, prone to error, and tedious. The proposed technique employs time-frequency features, statistical, and nonlinear approaches to form a robust features extraction technique. Four detection and classification techniques for focal and non-focal EEG signals were proposed. (1). Combined hybrid features with Support Vector Machine (Hybrid-SVM) (2). Discrete Wavelet Transform with Deep Learning Network (DWT-DNN) (3). Combined hybrid features with DNN (Hybrid-DNN) as an optimized DNN model. Lastly, (4). A newly proposed technique using Wavelet Synchrosqueezing Transform-Deep Convolutional Neural Network (WTSST-DCNN). Prior to feeding the features to classifiers, statistical analyses, including t-tests, were deployed to obtain relevant and significant features at each approach. The proposed feature extraction technique and classification proved effective and suitable for smart Internet of Medical Things (IoMT) devices as performance parameters of accuracy, sensitivity, and specificity are higher than recently related works with a value of 99.7%, 99.5%, and 99.7% respectively.
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    Applications of Artificial Intelligence in Automatic Detection of Epileptic Seizures Using EEG Signals: A Review
    (BON VIEW PUBLISHING PTE. LTD, 2022-07) Saminu, Sani; Xu, Guizhi; Zhang, Shuai; Abd El Kader, Isselmou; Aliyu, Hajara Abdulkarim; Jabire, Adamu Halilu; Ahmed, Yusuf Kola; Adamu, Mohammed Jajere
    Correctly interpreting an electroencephalogram signal with high accuracy is a tedious and time-consuming task that may take several years of manual training due to its complexity, noisy, non-stationarity, and nonlinear nature. To deal with the vast amount of data and recent challenges of meeting the requirements to develop low cost, high speed, low complexity smart internet of medical things computer-aided devices (CAD), artificial intelligence (AI) techniques which consist of machine learning and deep learning (DL) play a vital role in achieving the stated goals. Over the years, machine learning techniques have been developed to detect and classify epileptic seizures. But until recently, DL techniques have been applied in various applications such as image processing and computer visions. However, several research studies have turned their attention to exploring the efficacy of DL to overcome some challenges associated with conventional automatic seizure detection techniques. This article endeavors to review and investigate the fundamentals, applications, and progress of AI-based techniques applied in CAD system for epileptic seizure detection and characterization. It would help in actualizing and realizing smart wireless wearable medical devices so that patients can monitor seizures before their occurrence and help doctors diagnose and treat them. The work reveals that the recent application of DL algorithms improves the realization and implementation of mobile health in a clinical environment.
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    CHARACTERISTIC MODE ANALYSIS OF A STEPPED GRADIENT PLANAR ANTENNA FOR UWB APPLICATION
    (Department of Electrical Engineering, Ahmadu Bello University, Zaria, 2020-03) Jabire, Adamu Halilu; Saminu, Sani; Abdu, Anas; abel, Noku Amos; Sadiq, Abubakar Muhammad
    Characteristic mode technique is employed to gain a physical insight and also to find out the dominant mode of stepped gradient planar antenna without considering the feeding port. The rational of the model further implies that we can consider antenna's shape and feed design as independent steps. The stepped gradient planar antenna is constructed and measured, in which both the measured and simulated agreed on each other in terms of reflection coefficient and voltage standing wave ratio. The miniature stepped gradient planar antenna is appropriate for numerous applications in ultra-wideband (UWB) communication systems from 2.7 to 12GHz and stable radiation pattern at both E and Hfields were attained over the operating frequency band which is suitable for use in UWB systems.
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    Circuit Modeling of Dual Band MIMO Diversity Antenna for LTE and X-Band Applications
    (Universitas Ahmad Dahlan, 2023-09) Abdullahi, Aminu Gambo; Kolawale, S. F.; Saminu, Sani; Danladi, Ali; Jabire, Adamu Halilu
    This paper presents a study on developing a dual-band antenna equivalent circuit model for X-Band and LTE applications. MIMO antennas play a crucial role in modern wireless communication systems, and understanding their impedance behavior is essential. This work proposes a dual-band lumped equivalent circuit model, utilizing gradient optimization based on antenna-simulated S-parameters in Advanced Design System (ADS). The four radiating elements of the MIMO antenna are accurately modeled, considering their geometry and the defected ground structure (DGS) effect, which enhances the antenna's isolation and low correlation coefficient (ECC). The calculated lumped equivalent circuit model is validated through rigorous simulation and measurement data, demonstrating consistency with the expected results. The experimental measurements show measured isolation exceeding 20 dB while achieving a maximum realized gain of 5.9 dBi and an efficiency of 87%. The developed model holds promise for improving the design and performance of MIMO antennas for various applications.
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    A crossed-polarized four port MIMO antenna for UWB communication
    (Elsevier, 2022-12-29) Jabire, Adamu Halilu; Salisu, Sani; Saminu, Sani; Adamu, Mohammed Jajere; Hussein, Mousa I.
    This paper presents a compact, crossed-polarized, ultra-wideband (UWB) four-ports multiple- input-multiple-output (MIMO) printed antenna. The proposed antenna is constructed from four microstrip circular patch elements fed by a 50-Ω microstrip line. Two metamaterial cell elements, in the form of a rectangular concentric double split ring resonator (SRR), are placed at the upper plane of the substrates for bandwidth improvement and isolation enhancement. The ultra- wideband frequency response is achieved using a defective ground plane. Surface current flow between the antenna’s four elements is limited to ensure maximum isolation. The four-port MIMO system is designed with orthogonal antenna elements orientation on an FR4 substrate with a loss tangent of 0.02 and an overall size of 30 mm ×30 mm ×1.6 mm. Such orientation resulted in less than 􀀀17dB port-to-port isolation and an impedance bandwidth of 148% (3.1–12 GHz). The proposed UWB-MIMO antenna achieved a maximum realized gain of 6.2dBi with an efficiency of 87%. The measured and simulated results are in good agreement over the operating frequency band (3.1–12 GHz). The results also provide overall good diversity performance with the TARC <􀀀10 dB, ECC <0.001, DG >9.9, MEG <􀀀3 dB and CCL <0.1. The proposed antenna is well- suited for applications in WLAN, WIMAX and GPRs.
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    Design of a Compact UWB/MIMO Antenna with High Isolation and Gain
    (IEEE, 2020) Jabire, Adamu Halilu; Adnan, Ghaffar; Xue, Jun Li; Abdu, Anas; Saminu, Sani; Sadiq, Abubakar Muhammad; Jajere, Adamu Muhammed
    The design of a compact two elements ultrawideband (UWB) multiple-input-multiple-output (MIMO) planar antenna is presented. It consists of two symmetrically circular patch antenna components. The overall size of the antenna is 30 × 60 × 1.6mm3 and is printed on the FR4 substrate. The proposed antenna exhibits UWB qualities from 2.6 – 12GHz with the isolation of less than 20dB. The total active reflection coefficient (TARC), the data rate that can be supported in a particular channel (CCL), and a factor that signifies higher pattern diversity are presented, which are useful for portable UWB applications.
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    Electroencephalogram (EEG) Based Imagined Speech Decoding and Recognition
    (institution of Applied Materials and Technology Society with the cooperation of Faculty of Engineering, Universitas Riau, Pekanbaru, Indonesia, 2021-05-25) Saminu, Sani; Guizhi, Xu; Zhang, Shuai; Isselmou, Abd El Kader; Jabire, Adamu Halilu; Karaye, Ibrahim Abdullahi; Ahmad, Isah Salim; Abdulkarim, Abubakar
    ĉe recent investigations and advances in imagined speech decoding and recognition has tremendously improved the decoding of speech directly from brain activity with the help of several neuroimaging techniques that assist us in exploring the neurological processes of imagined speech. ĉis development leads to assist people with disabilities to beneėt from neuroprosthetic devices that improve the life of those suffering from neurological disorders. ĉis paper presents the summary of recent progress in decoding imagined speech using Electroenceplography (EEG) signal, as this neuroimaging method enable us to monitor brain activity with high temporal resolution, it is very portable, low cost, and safer as compared to other methods. ĉerefore, it is a good candidate in investigating an imagined speech decoding from the human cortex which remains a challenging task. ĉe paper also reviews some recent techniques, challenges, future recommendations and possible solutions to improve prosthetic devices and the development of brain computer interface system (BCI).
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    Epilepsy Detection and Classification for Smart IoT Devices Using hybrid Technique
    (IEEE, 2019-12-10) Saminu, Sani; Guizhi, Xu; Zhang, Shuai; Isselmou, Abd El Kader; Zakariyya, Rabiu Sale; Jabire, Adamu Halilu
    Epilepsy is a type of neurological disorder which can happen without serious warning and affects people almost at any age. It is a brain disorder caused by sudden and unprovoked seizures as a result of excitation of a lot of brain cells simultaneously which may lead to physical symptoms abnormalities and deformation such as failure in concentration, memory, attention etc. therefore, proper and efficient method of continues monitoring and detection of these epileptic seizures is paramount. This work presents an effective and efficient technique suitable for smart, low cost, power and real time devices that can be easily integrated with recent 5G network IoT devices for mobile applications, home and health care centers for monitoring and alert the doctors and patients about its occurrence to prevent a sudden collapse and consciousness which may cause injury and death. We proposed a low computational cost features extraction method by utilizing the efficacy of time-frequency, statistical and non-linear features known as hybrid techniques. The efficiency and accuracy of these smart devices is highly depends on quality of feature extraction methods and classifier performance. Therefore, this work employed two machine learning classifiers, support vector machine (SVM) and feedforward neural network (FFNN) to detect and classify interictal (normal) and ictal (seizure) signals. Discrete wavelet transform (DWT) is employed to decomposes the signals into decomposition levels as sub-bands of the signals to capture the non-stationarity of the EEG signals. Mean, median, maximum, minimum etc. were calculated for each sub-band as statistical parameters, non-linear features such as sample entropy, approximate entropy and wavelet energy were also calculated. The combination of features is then fed to two classifiers for the classification. Based on the performance measures such as accuracy, sensitivity and specificity, our proposed approach reveals a promising result with highest accuracy of 99.6%.
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    EPILEPTIC EEG SIGNALS RHYTHMS ANALYSIS IN THE DETECTION OF FOCAL AND NON-FOCAL SEIZURES BASED ON OPTIMISED MACHINE LEARNING AND DEEP NEURAL NETWORK ARCHITECTURE
    (World Scientific, 2023-06) Saminu, Sani; Xu, Guizhi; Zhang, Shuai; Abd El Kader, Isselmou; Jabire, Adamu Halilu; Ahmed, Yusuf Kola; Karaye, Ibrahim Abdullahi; Ahmad, Isah Salim
    Objective: Most studies in epileptic seizure detection and classi¯cation focused on classifying di®erent types of epileptic seizures. However, localization of the epileptogenic zone in epilepsy patient brain's is paramount to assist the doctor in locating a focal region in patients screened for surgery. Therefore, this paper proposed robust models for the localization of epileptogenic areas for the success of epilepsy surgery. Method: Advanced feature extraction techniques were proposed as e®ective feature extraction techniques based on Electroencephalogram (EEG) rhythms extracted from Fourier Basel Series Expansion Multivariate Empirical Wavelet Transform (FBSE-MEWT). The proposed extracted EEG rhythms of ; ; ; and features were used to obtain a joint instantaneous frequency and amplitude components using a subband alignment approach. The features are used in Sparse Autoencoder (SAE), Deep Belief Network (DBN), and Support Vector Machine (SVM) with the optimized capability to develop three new models: 1. FMEWT–SVM 2. FMEWT SAE–SVM, and 3. FMEWT–DBN–SVM. The EEG signal was preprocessed using a proposed Multiscale Principal Component Analysis (mPCA) to denoise the noise embedded in the signal. Main results: The developed models show a signi¯cant performance improvement, with the SAE–SVM outperforming other proposed models and some recently reported works in literature with an accuracy of 99.7% using -rhythms in channels 1 and 2. Signi¯cance: This study validates the EEG rhythm as a means of discriminating the embedded features in epileptic EEG signals to locate the focal and non-focal regions in the epileptic patient's brain to increase the success of the surgery and reduce computational cost.
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    Hybrid Feature Extraction Technique for Multi-Classification of Ictal and Non-Ictal EEG Epilepsy Signals
    (Universiti Teknologi Malaysia (UTM), 2020-08-29) Saminu, Sani; Guizhi, Xu; Zhang, Shuai; Isselmou, Abd El Kader; Jabire, Adamu Halilu; Karaye, Ibrahim Abdullahi; Ahmad, Isah Salim
    These Electroencephalography (EEG) signals is an effective tool for identification, monitoring, and treatment of epilepsy, but EEG signals need highly experienced personnel to interpret it correctly due to its complexity, even for an expert it is monotonous and usually consume much time. Therefore, the automatic computer-aided device (CAD) needs to be developed to overcome those challenges associated with epilepsy interpretation and diagnosis. The system efficiency relies largely on the quality of features supply as input to classifiers. This paper presents an efficient feature extraction technique to develop a CAD system that can detect and classify normal, interictal and ictal epilepsy signals correctly with high accuracy. Our approach employs time-frequency features, statistical features and nonlinear features combined as hybrid features to train and test the classifier. Machine learning classifiers of multi-class support vector machine (mSVM) and feed-forward neural network (FFNN) with fivefold cross-validation are used to classifies normal, interictal and ictal with our proposed features. Our system was tested using a publicly available database with three classes each of 100 single channels EEG signals of 4096 samples point each. Based on sensitivity, specificity, and accuracy, our proposed approach of multiclass classification shows a good performance with 96.7%, 98.3% and 100% of sensitivity, specificity, and accuracy respectively.
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    Investigation of Optimal Components and Parameters of the Incremental PCA-based LSTM Network for Detection of EEG Epileptic Seizure Events
    (Faculty of Science, Gombe State University (GSU), Nigeria, 2023-12) Saminu, Sani; Jabire, Adamu Halilu; Aliyu, Hajar Abdulkarim; Yahaya, Suleiman Abimbola; Iliyasu, Adamu Yau; Ibitoye, Morufu Olusola; Xu, Guizhi
    Prediction of Epileptic seizures is highly imperative to improve the epileptic patient’s life. Epileptic seizures occur due to brain cells excessive abnormal activity that leads to unprovoked seizures and may occur without prior notice. Therefore, preventive measure that monitor and alert the possible occurrence of the seizures is paramount. Commercial and clinical available epileptic seizure computer aided detection system that utilized deep learning algorithms suffers from many challenges. These challenges ranges from low accuracy and precision, sensitive to artifacts and noise, among others. To enhance and increase the accuracy and optimal performance of these networks, this paper endeavor to investigate various optimization algorithm to optimized the network components and parameters in the developed incremental Principal Components Analysis based Long Short-Term Memory (Inc-PCA-LSTM) network for the detection and classification of Electroencephalograph (EEG) epileptic seizure signals based on the big data scenario. The model proved to be effective in the characterization of seven seizure events. The Adam, Elu, Orthogonal, and L1/L2 performed better than their counterparts in optimization functions, activation functions, initialization functions, and regularisation techniques respectively. The accuracy values of 97.5%, 97.5%, 98.4%, and 98.5% was obtained for each of the mentioned core components receptively.
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    Isolation Frequency Switchable MIMO Antenna for PCS, WIMAX and WLAN Application
    (Universiti Teknologi Malaysia (UTM), 2019-12-18) Jabire, Adamu Halilu; Abdu, Anas; Saminu, Sani; Sadiq, Abubakar Muhammad; Jajere, Adamu Muhammed
    In this study, a lumped component based frequency reconfigurable multiple-input-multiple-output (MIMO) receiving wire design is presented. The proposed antenna is composed of a planar structure in form of F-shaped together with a slotted and defected ground structure for bandwidth and isolation enhancement. The MIMO antenna operates in six frequencies upon the state of the four lumped element switches. The proposed receiving wire design exhibits a multiband frequency reconfigurable characteristics from 1-7GHz with isolation of more than 14dB for the whole band, with efficiency of about 75%. The MIMO antenna’s behavior in terms of ratio of square root of the sum of power reflected wave to the incident wave (TARC), ECC and CCL are all within the acceptable limits. The design is suitable for personal communication system (PCS), WIMAX and WLAN wireless applications.
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    Magnetoacoustic Tomography with Magnetic Induction: Multiphysics Imaging Approach_A Short Review
    (Ataturk University, Erzurum, Turkey, 2021) Saminu, Sani; Guizhi, Xu; Zhang, Shuai; Isselmou, Abd El Kader; Jabire, Adamu Halilu; Iliyasu, Adamu Yau
    Magnetoacoustic tomography with magnetic induction (MAT-MI) is one of the multiphysics imaging techniques that combines the principle of magnetic field excitation and acoustic vibration. It is a noninvasive imaging technique developed in order to achieve high electrical impedance contrast of biological tissue as well as high spatial resolution close to ultrasound imaging. The feasibility to reconstruct high spatial resolution conductivity images using MAT-MI method has been demonstrated by both computer simulation and experimental studies. This work reviews the summary of fundamental ideas of MAT-MI and major techniques developed in recent years. First, the physical mechanisms underlying MAT-MI imaging are described including the magnetic induction and Lorentz force induced acoustic wave propagation. Second, experimental setups and various imaging strategies for MAT-MI are reviewed. Finally, we give our opinions on existing challenges and future directions for MAT-MI research. With all the reported and future technical advancement, MAT-MI has the potential to become an important noninvasive modality for electrical conductivity imaging of biological tissue.
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    Metamaterial Based Design of Compact UWB/MIMO Monopoles Antenna with Characteristic Mode Analysis
    (MDPI, 2021-02-08) Jabire, Adamu Halilu; Adnan, Ghaffar; Xue, Jun Li; Abdu, Anas; Saminu, Sani; Alibakhshikenari, Mohammad; Falcone, Francisco; Limiti, Ernesto
    In this article, a novel metamaterial inspired UWB/multiple-input-multiple-output (MIMO) antenna is presented. The proposed antenna consists of a circular metallic part which formed the patch and a partial ground plane. Metamaterial structure is loaded at the top side of the patches for bandwidth improvement and mutual coupling reduction. The proposed antenna provides UWB mode of operation from 2.6–12 GHz. The characteristic mode theory is applied to examine each physical mode of the antenna aperture and access its many physical parameters without exciting the antenna. Mode 2 was the dominant mode among the three modes used. Considering the almost inevitable presence of mutual coupling effects within compact multiport antennas, we developed an additional decoupling technique in the form of perturbed stubs, which leads to a mutual coupling reduction of less than 20 dB. Finally, different performance parameters of the system, such as envelope correlation coefficient (ECC), channel capacity loss (CCL), diversity gain, total active reflection coefficient (TARC), mean effective gain (MEG), surface current, and radiation pattern, are presented. A prototype antenna is fabricated and measured for validation.
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    Modal Analysis of a Circular Slot Monopole Antenna for UWB application
    (Universiti Teknologi Malaysia (UTM), 2019-12-18) Jabire, Adamu Halilu; Abdu, Anas; Saminu, Sani; Taura, Aliyu Uba; Obalowu, Mohammed Olatunji
    In this study, a wideband double slot antenna is introduced. The proposed monopole antenna is designed using the theory of characteristic mode for slot monopoles. A circular shape is used as the initial design stage, to enhance the bandwidth, two circular slots are employed. Four modes have been excited to gain a physical insight and to find out which mode is dominant. The design and analysis were completed utilizing both the time domain and multilayer solver in CST 2017, without considering the feeding port. An antenna model in UWB frequency is constructed. Experimented and simulated results shows that the proposed planar structure has a wide impedance width with good radiation characteristics.
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    Multi-Classification of Electroencephalogram Epileptic Seizures Based on Robust Hybrid Feature Extraction Technique and Optimized Support Vector Machine Classifier
    (Istanbul University Cerrahpasa, 2023-08) Saminu, Sani; Xu, Guizhi; Zhang, Shuai; Abd El Kader, Isselmou; Aliyu, Hajara Abdulkarim; Jabire, Adamu Halilu; Ahmed, Yusuf Kola; Ahmad, Isah Salim
    Epilepsy is a disease with various forms. However, limited dataset has confined classification studies of epilepsy into binary classes only. This study sort to achieve multiclassification of epileptic seizures through a robust feature extraction technique by comprehensively analyzing various advanced feature parameters from different domains, such as energy and entropy. The values of these parameters were computed from the coefficients of dilation wavelet transform (DWT) and modified DWT, known as dual-tree complex wavelet transform decomposition. The model was evaluated from the features of each of the parameters. The hybrid features were divided into three experiments to extract the meaningful features as follows: 1). features from combined energy features were extracted; 2). features from combined entropy features were also extracted; and 3). features from combined parameters as hybrid features were extracted. Finally, the model was developed based on the extracted features to perform a multi-classification of seven types of seizures using an optimized support vector machine (SVM) classifier. A recently released temple university hospital corpus dataset consisting of long-time seizure recordings of various seizures was employed to evaluate our proposed model. The proposed optimized SVM classifier with the hybrid features performed better than other experimented models with the value of accuracy, sensitivity, specificity, precision, and F1-score of 96.9%, 96.8%, 93.4%, 95.6%, and 96.2%, respectively. The developed model was also compared with some recent works in literature that employed the same dataset and found that our model outperformed all the compared studies.
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    Mutual Coupling Reduction For Triple Band MIMO Antenna Using Stub Loading Technique
    (faculty of Natural and Applied Sciences, Sule Lamido University, Kafin Hausa, Jigawa State, Nigeria, 2021-01) Jabire, Adamu Halilu; Abdullahi, Aminu Gambo; Saminu, Sani; Jajere, Adamu Muhammed; Sadiq, Abubakar Muhammad
    This paper presents a mutual coupling reduction using stub and partial ground structure. The driven analysis comprises four antennas that are placed orthogonal to each other. A decoupling network is proposed, which consists of one long stub extended between the four defected ground structure for electromagnetic interaction reduction. The proposed antenna has triple-band frequencies at 3 GHz, 5.5 GHz, and 7.1 GHz. The performance of the four by four antenna arrays is evaluated based on envelope correlation coefficient, isolation, mean effective gain, channel capacity loss, total active reflection coefficient, and diversity gain. The results strongly support the applicability of fifth-generation sub 6 GHz applications.
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    A Novel Computer Aided Detection System for Detection of Focal and Non-Focal EEG Signals using Optimized Deep Neural Network
    (IEEExplore, 2021-12) Saminu, Sani; Xu, Guizhi; Zhang, Shuai; Abd El Kader, Isselmou; Jabire, Adamu Halilu; Ahmed, Yusuf Kola; Aliyu, Hajara Abdulkarim; Adamu, Mohammed Jajere; Iliyasu, Adamu Yau; Umar, Faiza Ali
    Epilepsy is a neurological disorder affecting people of all ages. This disorder is reported to affect over 50 million people, with a majority residing in developing countries [1]. It is a sudden and unprovoked seizure that occurs due to an erratic change in the brains' electrical activity often accompanied by loss of consciousness, uncontrolled motions, jerking, and loss of memory [2] [3]. These inconvenient and undesirable effects undermine the quality of life of epilepsy patients as it's difficult for patients and doctors to predict when and where these seizures would occur. Therefore, it is highly imperative to develop an automated system for monitoring epileptic seizures and to assist clinicians in proper and efficient diagnosing of this disease [4] [5].
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    OSTBC-MIMO Performance Evaluation Using Different Modulation Schemes
    (Faculty of Technology Education, Abubakar Tafawa Balewa University Bauchi, 2020-09) Saminu, Sani; Jabire, Adamu Halilu; Jajere, Adamu Muhammed; Sadiq, Abubakar Muhammad; Zakariyya, Rabiu Sale
    This paper presents the performance evaluation of Orthogonal Space Time Block Codes on Multiple Input Multiple Output (OSTBC-MIMO) system using our proposed extended Alamouti’s STBC scheme based on Orthogonal design. The model was evaluated with different modulation schemes such as BPSK, QPSK, 8-PSK, and 16-PSK for different antenna configurations. From our results, BPSK outperforms other modulations scheme. The system model was developed in Matlab environment and Bit error rate against signal to noise ratio was used to evaluate the performance under Rayleigh faded channel. Our proposed scheme improves the channel capacity gains, data throughput, and mitigate fading and interference.
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    PERFORMANCE ANALYSIS OF TRANSMIT DIVERSITY CONFIGURATIONS BASED ON OSTBC ALAMOUTI’S EXTENSION
    (Department of Electrical Engineering, Ahmadu Bello University, Zaria, 2021-03) Saminu, Sani; Jabire, Adamu Halilu; Abdulkarim, Abubakar; Ahmed, Yusuf Kola; Iliyasu, Adamu Yau; Salisu, Sani; Karaye, Ibrahim Abdullahi; Ahmad, Isah Salim
    One of the diversity configurations is transmit diversity. It is employed to mitigate multipath fading channel in a time varying channels to improve wireless communication system and make it more reliable. This paper presents a review of diversity techniques and configurations with various signal processing techniques and space time coding system that are mostly employed, diversity combining schemes and analysis of diversity schemes is also exploited. We also proposed a robust space time coding scheme based on orthogonal design by extending the Alamouti’s space time block coding to a higher order diversity and evaluates its performance based on signal to noise ratio (SNR) and bit error rate (BER). The advantage of this transmit diversity is to simplify the hardware requirement by providing a cost effective solution in broadband wireless system with eliminating the need for adopting many antennas at the receiver side