Browsing Computational and Communication Science Engineering by Author "Mollel, Michael"

Now showing items 1-10 of 10

    • Comparison of Empirical Propagation Path Loss Models for Mobile Communication 

      Mollel, Michael; Michael, Kisangiri (Computer Engineering and Intelligent Systems, 2014)
      Empirical propagation models have found favor in both research and industrial communities owing to their speed of execution and their limited reliance on detailed knowledge of the terrain. In mobile communication the accuracy ...

    • Deep Reinforcement Learning based Handover Management for Millimeter Wave Communication 

      Mollel, Michael; Kaijage, Shubi; Michael, Kisangiri (International Journal of Advanced Computer Science and Applications,, 2021)
      The Millimeter Wave (mm-wave) band has a broad-spectrum capable of transmitting multi-gigabit per-second date-rate. However, the band suffers seriously from obstruction and high path loss, resulting in line-of-sight (LOS) ...

    • The effect of real ground on dual band Yagi-Uda antenna 

      Mollel, Michael; Kisangiri, Michael (IJERT, 2013-10)
      This paper aims to analyse radiation pattern for horizontally polarised dual band Yagi-Uda antenna (900/1800 MHz) with the presence of real ground. Simulations were performed at 920 MHz and 1780 MHz and antenna were placed ...

    • Handover Management in Dense Networks with Coverage Prediction from Sparse Networks 

      Mollel, Michael; Ozturk, Metin; Kisangiri, Michael; Kaijage, Shubi; Onireti, Oluwakayode; Imran, Muhammad; Abbasi, Qammer (IEEE, 2019)
      Millimeter Wave (mm-Wave) provides high bandwidth and is expected to increase the capacity of the network thousand-fold in the future generations of mobile communications. However, since mm-Wave is sensitive to blockage ...

    • Improved handover decision scheme for 5g mm-wave communication: optimum base station selection using machine learning approach. 

      Mollel, Michael (NM-AIST, 2022-09)
      The rapid growth in mobile and wireless devices has led to an exponential demand for data traf fic and exacerbated the burden on conventional wireless networks. Fifth generation (5G) and beyond networks are expected to ...

    • Intelligent handover decision scheme using double deep reinforcement learning 

      Mollel, Michael; Abubakar, Attai Ibrahim; Ozturk, Metin; Kaijage, Shubi; Michael, Kisangiri,; Zoha, Ahmed; Imran, Muhammad Ali; Abbasi, Qammer (Elsevier B.V., 2020-10)
      Handovers (HOs) have been envisioned to be more challenging in 5G networks due to the inclusion of millimetre wave (mm-wave) frequencies, resulting in more intense base station (BS) deployments. This, by its turn, increases ...

    • Optimization of Hata Model based on Measurements Data using Least Square Method: A Case Study in Dar-es-Salaam – Tanzania 

      Mollel, Michael; Michael, Kisangiri (International Journal of Computer Applications, 2014-09)
      ABSTRACT In this study, we present a measurement-based model for path loss prediction in three GSM service areas at 900 MHz .Modified Hata model for rural, suburban, and urban environments were derived in this study on ...

    • An Overview of Various Propagation Model for Mobile Communication 

      Mollel, Michael; Michael, Kisangiri (Pan African International Conference on Information Science, Computing and Telecommunications, 2014)
      System’s propagation characteristics through a medium is one of the important task to be done before planning and optimization of any network in order to estimate the signal parameter accurately for mobile system. Propagation ...

    • A Survey of Machine Learning Applications to Handover Management in 5G and Beyond 

      Mollel, Michael; Abubakar, Attai Ibrahim; Ozturk, Metin; Kaijage, Shubi; Michael, Kisangiri; Hussain, Sajjad; Imran, Muhammad Ali; Abbasi, Qammer (IEEE, 2021-03-19)
      Handover (HO) is one of the key aspects of next-generation (NG) cellular communication networks that need to be properly managed since it poses multiple threats to quality-of-service (QoS) such as the reduction in the ...

    • A Survey of Machine Learning Applications to Handover Management in 5G and Beyond 

      Mollel, Michael; Abubakar, Attai; Ozturk, Metin; Kaijage, Shubi; Kisangiri, Michael; Imran, Muhammad; Abbasi, Qammer (IEEE, 2021-03-14)
      Handover (HO) is one of the key aspects of next-generation (NG) cellular communication networks that need to be properly managed since it poses multiple threats to quality-of-service (QoS) such as the reduction in the ...