Device–to-Device Association Algorithm for Optimal Neighbour Selection and Channel Sharing in 5G Cellular Networks

  • Chiza M Christophe Department of Electronics and Telecommunications Engineering, College of Information and Communication Technologies, University of Dar es Salaam, Tanzania
  • Omar F Hamad Department of Electronics and Telecommunications Engineering, College of Information and Communication Technologies, University of Dar es Salaam, Tanzania
  • Libe V Massawe Department of Electronics and Telecommunications Engineering, College of Information and Communication Technologies, University of Dar es Salaam, Tanzania
  • Abdi T Abdalla Department of Electronics and Telecommunications Engineering, College of Information and Communication Technologies, University of Dar es Salaam, Tanzania.
Keywords: Cooperative Communication; D2D, Mode Selection; Relay-assisted; Traffic Overload

Abstract

The integration of device-to-device (D2D) communication in 5G cellular networks has generated the possibility of multiple transmission modes in a single cell. This has motivated scholars to investigate different mode selection and D2D association algorithms that guarantee the selection of proper transmission mode. However, the complexity of algorithms and tractability of devices in the cell are still remarkably challenging. This paper, therefore, presents a utility based D2D association algorithm that ensures optimal neighbour selection by using numerical linear algebra to minimize computational complexity. Simulation results show that the minimum utility based D2D association increases the expected values of attached devices by 6% and 10% compared to the relative distance and maximum utility based D2D associations, respectively. Alternatively, the throughput expectation increases by 2.5% and 4% compared to the relative distance and maximum utility based D2D associations, respectively.

Keywords: Cooperative Communication; D2D, Mode Selection; Relay-assisted; Traffic Overload

Published
2020-05-28
Section
Articles

Journal Identifiers


eISSN: 2507-7961
print ISSN: 0856-1761