Norulhusna Ahmad

Bio: Norulhusna Ahmad is an academic researcher from Universiti Teknologi Malaysia. The author has contributed to research in topics: Bit error rate & Decoding methods. The author has an hindex of 5, co-authored 43 publications receiving 99 citations.

New weight function for adapting handover margin level over contiguous carrier aggregation deployment scenarios in LTE-advanced system

Abstract: In this paper, an Adaptive Handover Margin algorithm based on Novel Weight Function (AHOM-NWF) is proposed through Carrier Aggregation operation in Long Term Evolution—Advanced system. The AHOM-NWF algorithm automatically adjusts the Handover Margin level based on three functions, $$f(SINR), \; f(TL)\; {\text{and}}\; f(v)$$ , which are evaluated as functions of Signal-to-Interference-plus-Noise-Ratio (SINR), Traffic Load $$(TL)$$ , and User’s velocity $$(v)$$ respectively. The weight of each function is taken into account in order to estimate an accurate margin level. Furthermore, a mathematical model for estimating the weight of each function is formulated by a simple model. However, AHOM-NWF algorithm will contribute for the perspective of SINR improvement, cell edge spectral efficiency enhancement and outage probability reduction. Simulation results have shown that the AHOM-NWF algorithm enhances system performance more than the other considered algorithms from the literature by 24.4, 14.6 and 17.9%, as average gains over all the considered algorithms in terms of SINR, cell edge spectral efficiency and outage probability reduction respectively.

Soft-feedback MMSE equalization for non-orthogonal frequency division multiplexing (n-OFDM) signal detection

Abstract: A frequency division multiplexing technique, nonorthogonal frequency division multiplexing (n-OFDM), is proposed in [1]– [2] to enhance the efficiency of bandwidth utilization. This paper reveals that the smaller the frequency separation, the larger sum capacity can be achieved compared with the conventional OFDM technique. However, n-OFDM system introduces inter-carrier interference (ICI) at the transmitter because the orthogonality between the subcarriers no longer holds. Moreover, since the channel covariance matrix of n-OFDM has high condition number when the overlapping factor, 1 − α, is large, conventional linear detectors suffers from severe noise enhancement. To solve this problem, this paper proposes the use of soft cancellation- minimum mean-squared error (SCMMSE) turbo equalization. Binary constellation constrained mutual information (CCMI) is calculated by utilizing the area property for the EXtrinsic Information Transfer (EXIT) chart of the SC-MMSE equalizer. Results of the EXIT chart analysis and bit-error-rate (BER) simulations in additive white Gaussian noise (AWGN) channel are presented.

Performance Enhancement of SCM/WDM-RoF-XGPON System for Bidirectional Transmission With Square Root Module

Abstract: The 10-Gigabit Passive Optical Network (GPON), also known as (XGPON), is a key technology for the next generation of optical fibre communication systems that can improve reliability data rate. However, the increment in the transmission distance and data rate will lead to increased dispersion. This paper proposes a model based on the Sub-Carrier Multiplexing/Wavelength Division Multiplexing-Radio over Fibre-10-Gigabit Passive Optical Network system (SCM/WDM-RoF-XGPON) for Radio Frequency (RF) using a conventional Optical Network Unit (ONU) with the Square Root Module (SRM) on the side of the receiver. It is connected to a central office Optical Line Terminal (OLT) via an access network for multi-channel optical fibre transfers with a distance of 80 km and at 10 Gbps data rate. The Optisystem 15 simulation software is used to evaluate the proposed system based on the Bit Error Rate (BER), Quality factor (Q-factor) and eye diagram. The proposed system of bidirectional fibre links uses 1270 nm and 1577 nm wavelengths for uplink and downlink transmissions, respectively. The performance is further enhanced by using SRM to compensate for the square law characteristics, making it suitable for broadband services. Due to the utilisation of SRM in the architecture, the reported results reveal a double enhancement in successful transition performance at an optical fibre length of 80 km distance. The BER displayed significant improvement (less than 1.00E-09) with SRM at 80 km; however, without SRM and at 50 km, the BER is less than 1.00E-09. Investigations have presented an enhancement in the Q-factor’s effectiveness with the use of SRM, which helps to increase the XGPON length. When the length of the fibre is 80 km, the Q-factor is 6 with SRM, while it is 3.7 without SRM.

Classification of adult autistic spectrum disorder using machine learning approach

Abstract: Autism spectrum disorder (ASD) is a neurological-related disorder. Patients with ASD have poor social interaction and lack of communication that lead to restricted activities. Thus, early diagnosis with a reliable system is crucial as the symptoms may affect the patient’s entire lifetime. Machine learning approaches are an effective and efficient method for the prediction of ASD disease. The study mainly aims to achieve the accuracy of ASD classification using a variety of machine learning approaches. The dataset comprises 16 selected attributes that are inclusive of 703 patients and non-patients. The experiments are performed within the simulation environment and analyzed using the Waikato environment for knowledge analysis (WEKA) platform. Linear support vector machine (SVM), k-nearest neighbours (k-NN), J48, Bagging, Stacking, AdaBoost, and naive bayes are the methods used to compute the prediction of ASD status on the subject using 3, 5, and 10-folds cross validation. The analysis is then computed to evaluate the accuracy, sensitivity, and specificity of the proposed methods. The comparative result between the machine learning approaches has shown that linear SVM, J48, Bagging, Stacking, and naive bayes produce the highest accuracy at 100% with the lowest error rate.

Comparison on Some Machine Learning Techniques in Breast Cancer Classification

Abstract: Breast cancer is the second most common cancer after lung cancer and one of the main causes of death worldwide. Women have a higher risk of breast cancer as compared to men. Thus, one of the early diagnosis with an accurate and reliable system is critical in breast cancer treatment. Machine learning techniques are well known and popular among researchers, especially for classification and prediction. An investigation was conducted to evaluate the performance of breast cancer classification for malignant tumors and benign tumors using various machine learning techniques, namely k-Nearest Neighbors (k-NN), Random Forest, and Support Vector Machine (SVM) and ensemble techniques to compute the prediction of the breast cancer survival by implementing 10-fold cross validation. Additionally, the proposed methods are classified using 2-fold, 3-fold, and 5-fold cross validation to meet the best accuracy rate. This study used a dataset obtained from Wisconsin Diagnostic Breast Cancer (WDBC) with 23 selected attributes measured from 569 patients, from which 212 patients have malignant tumors and 357 patients have benign tumors. The performance evaluation of the proposed methods was computed to obtain accuracy, sensitivity, and specificity. Comparison results between all methods show that AdaBoost ensemble methods gave the highest accuracy at 98.77% for 10-fold cross validation, while 2-fold and 3-fold cross validation at 98.41% and 98.24%, respectively. Nevertheless, the result with 5-fold cross validation show SVM produced the best accuracy rate at 98.60% with the lowest error rate.

Multimedia Internet of Things: A Comprehensive Survey

Abstract: The immense increase in multimedia-on-demand traffic that refers to audio, video, and images, has drastically shifted the vision of the Internet of Things (IoT) from scalar to Multimedia Internet of Things (M-IoT). IoT devices are constrained in terms of energy, computing, size, and storage memory. Delay-sensitive and bandwidth-hungry multimedia applications over constrained IoT networks require revision of IoT architecture for M-IoT. This paper provides a comprehensive survey of M-IoT with an emphasis on architecture, protocols, and applications. This article starts by providing a horizontal overview of the IoT. Then, we discuss the issues considering the characteristics of multimedia and provide a summary of related M-IoT architectures. Various multimedia applications supported by IoT are surveyed, and numerous use cases related to road traffic management, security, industry, and health are illustrated to show how different M-IoT applications are revolutionizing human life. We explore the importance of Quality-of-Experience (QoE) and Quality-of-Service (QoS) for multimedia transmission over IoT. Moreover, we explore the limitations of IoT for multimedia computing and present the relationship between the M-IoT and emerging technologies including event processing, feature extraction, cloud computing, Fog/Edge computing and Software-Defined-Networks (SDNs). We also present the need for better routing and Physical-Medium Access Control (PHY-MAC) protocols for M-IoT. Finally, we present a detailed discussion on the open research issues and several potential research areas related to emerging multimedia communication in IoT.

Learning through play.

Abstract: At the beach, two children are pretending to be explorers, wearing their beach towels as capes. They climb among the rock pools collecting stones and shells. Now and then they check in with each other to compare treasures or call out to their parents to come and look. Later they sit and sort their treasures. Tired of being explorers they build a sand castle together and decorate it with the small treasures they’ve found.

Key Challenges, Drivers and Solutions for Mobility Management in 5G Networks: A Survey

Abstract: Ensuring a seamless connection during the mobility of various User Equipments (UEs) will be one of the major challenges facing the practical implementation of the Fifth Generation (5G) networks and beyond. Several key determinants will significantly contribute to numerous mobility challenges. One of the most important determinants is the use of millimeter waves (mm-waves) as it is characterized by high path loss. The inclusion of various types of small coverage Base Stations (BSs), such as Picocell, Femtocell and drone-based BSs is another challenge. Other issues include the use of Dual Connectivity (DC), Carrier Aggregation (CA), the massive growth of mobiles connections, network diversity, the emergence of connected drones (as BS or UE), ultra-dense network, inefficient optimization processes, central optimization operations, partial optimization, complex relation in optimization operations, and the use of inefficient handover decision algorithms. The relationship between these processes and diverse wireless technologies can cause growing concerns in relation to handover associated with mobility. The risk becomes critical with high mobility speed scenarios. Therefore, mobility issues and their determinants must be efficiently addressed. This paper aims to provide an overview of mobility management in 5G networks. The work examines key factors that will significantly contribute to the increase of mobility issues. Furthermore, the innovative, advanced, efficient, and smart handover techniques that have been introduced in 5G networks are discussed. The study also highlights the main challenges facing UEs’ mobility as well as future research directions on mobility management in 5G networks and beyond.

A Comprehensive Survey on Mobility Management in 5G Heterogeneous Networks: Architectures, Challenges and Solutions

Abstract: With the rapid increase in the number of mobile users, wireless access technologies are evolving to provide mobile users with high data rates and support new applications that include both human and machine-type communications. Heterogeneous networks (HetNets), created by the joint installation of macro cells and a large number of densely deployed small cells, are considered an important solution to deal with the increasing network capacity demands and provide high coverage to wireless users in future fifth generation (5G) wireless networks. Due to the increasing complexity of network topology in 5G HetNets with the integration of many different base station types, in 5G architecture mobility management has many challenges. Intense deployment of small cells, along with many advantages it provides, brings important mobility management problems such as frequent handover (HO), HO failure, HO delays, ping-pong HO and high energy consumption which will result in lower user experience and heavy signal loads. In this paper, we provide a comprehensive study on the mobility management in 5G HetNet in terms of radio resource control, the initial access and registration procedure of the user equipment (UE) to the network, the paging procedure that provides the location of the UE within the network, connected mode mobility management schemes, beam level mobility and beam management. Besides, this paper addresses the challenges and suggest possible solutions for the 5G mobility management.