Yazar "Kelechi, Anabi Hilary" seçeneğine göre listele

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    The Four-C Framework for High Capacity Ultra-Low Latency in 5G Networks: A Review
    (MDPI, ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND, 2019) Kelechi, Anabi Hilary; Alsharif, Mohammed H.; Ramly, Athirah Mohd; Abdullah, Nor Fadzilah; Nordin, Rosdiadee
    Network latency will be a critical performance metric for the Fifth Generation (5G) networks expected to be fully rolled out in 2020 through the IMT-2020 project. The multi-user multiple-input multiple-output (MU-MIMO) technology is a key enabler for the 5G massive connectivity criterion, especially from the massive densification perspective. Naturally, it appears that 5G MU-MIMO will face a daunting task to achieve an end-to-end 1 ms ultra-low latency budget if traditional network set-ups criteria are strictly adhered to. Moreover, 5G latency will have added dimensions of scalability and flexibility compared to prior existing deployed technologies. The scalability dimension caters for meeting rapid demand as new applications evolve. While flexibility complements the scalability dimension by investigating novel non-stacked protocol architecture. The goal of this review paper is to deploy ultra-low latency reduction framework for 5G communications considering flexibility and scalability. The Four (4) C framework consisting of cost, complexity, cross-layer and computing is hereby analyzed and discussed. The Four (4) C framework discusses several emerging new technologies of software defined network (SDN), network function virtualization (NFV) and fog networking. This review paper will contribute significantly towards the future implementation of flexible and high capacity ultra-low latency 5G communications.
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    Machine Learning Algorithms for Smart Data Analysis in Internet of Things Environment: Taxonomies and Research Trends
    (MDPI, ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND, 2020) Alsharif, Mohammed H.; Kelechi, Anabi Hilary; Chaudhry, Shehzad Ashraf
    Machine learning techniques will contribution towards making Internet of Things (IoT) symmetric applications among the most significant sources of new data in the future. In this context, network systems are endowed with the capacity to access varieties of experimental symmetric data across a plethora of network devices, study the data information, obtain knowledge, and make informed decisions based on the dataset at its disposal. This study is limited to supervised and unsupervised machine learning (ML) techniques, regarded as the bedrock of the IoT smart data analysis. This study includes reviews and discussions of substantial issues related to supervised and unsupervised machine learning techniques, highlighting the advantages and limitations of each algorithm, and discusses the research trends and recommendations for further study.
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    Sixth Generation (6G) Wireless Networks: Vision, Research Activities, Challenges and Potential Solutions
    (MDPI, ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND, 2020) Alsharif, Mohammed H.; Kelechi, Anabi Hilary; Albreem, Mahmoud A. M.; Chaudhry, Shehzad Ashraf; Zia, Muhammad Sultan; Kim, Sunghwan
    The standardization activities of the fifth generation communications are clearly over and deployment has commenced globally. To sustain the competitive edge of wireless networks, industrial and academia synergy have begun to conceptualize the next generation of wireless communication systems (namely, sixth generation, (6G)) aimed at laying the foundation for the stratification of the communication needs of the 2030s. In support of this vision, this study highlights the most promising lines of research from the recent literature in common directions for the 6G project. Its core contribution involves exploring the critical issues and key potential features of 6G communications, including: (i) vision and key features; (ii) challenges and potential solutions; and (iii) research activities. These controversial research topics were profoundly examined in relation to the motivation of their various sub-domains to achieve a precise, concrete, and concise conclusion. Thus, this article will contribute significantly to opening new horizons for future research directions.