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Quality of Service Class Identifier (QCI) radio resource allocation algorithm for LTE downlink

Author

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  • Maharazu Mamman
  • Zurina Mohd Hanapi
  • Azizol Abdullah
  • Abdullah Muhammed

Abstract

The increasing demand for network applications, such as teleconferencing, multimedia messaging and mobile TV, which have diverse requirements, has resulted in the introduction of Long Term Evolution (LTE) by the Third Generation Partnership Project (3GPP). LTE networks implement resource allocation algorithms to distribute radio resource to satisfy the bandwidth and delay requirements of users. However, the scheduling algorithm problem of distributing radio resources to users is not well defined in the LTE standard and thus considerably affects transmission order. Furthermore, the existing radio resource algorithm suffers from performance degradation under prioritised conditions because of the minimum data rate used to determine the transmission order. In this work, a novel downlink resource allocation algorithm that uses quality of service (QoS) requirements and channel conditions to address performance degradation is proposed. The new algorithm is formulated as an optimisation problem where network resources are allocated according to users’ priority, whereas the scheduling algorithm decides on the basis of users’ channel status to satisfy the demands of QoS. Simulation is used to evaluate the performance of the proposed algorithm, and results demonstrate that it performs better than do all other algorithms according to the measured metrics.

Suggested Citation

  • Maharazu Mamman & Zurina Mohd Hanapi & Azizol Abdullah & Abdullah Muhammed, 2019. "Quality of Service Class Identifier (QCI) radio resource allocation algorithm for LTE downlink," PLOS ONE, Public Library of Science, vol. 14(1), pages 1-22, January.
  • Handle: RePEc:plo:pone00:0210310
    DOI: 10.1371/journal.pone.0210310
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    Cited by:

    1. Asmae Mamane & M. Fattah & M. El Ghazi & M. El Bekkali, 2022. "5G enhanced mobile broadband multi-criteria scheduler for dense urban scenario," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 80(1), pages 33-43, May.
    2. Fadhil Mukhlif & Kamarul Ariffin Bin Noordin & Omar B Abdulghafoor & Tengku Faiz Tengku Mohmed Noor Izam, 2020. "Green communication for cognitive radio networks based on game and utility-pricing theories," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-21, August.

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