Now showing items 1-3 of 3

    • 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 ...
    • 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 ...
    • 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 ...