| dc.description.abstract |
Device-to-device (D2D) communication is a promising wireless technology for the next 5G
networks to improve network capacity and user experience. However, in large and complex
network, optimizing the number of links less than threshold value as missing links between nodes
remain a challenge. In D2D, mobile nodes communicate to share content with nodes in proximity
based on perceived quality of the link assigned by the weighted graph. Minimizing the number of
missing links between nodes will improve the quality of communication and assures users’ quality
of experience. In this paper, we proposed a computation-enhanced fireworks algorithm (CEFA)
that use the concept of graph based network propagation. Using CEFA, we generate an explosion
of sparks to examine the number of nodes on the graph. We determine connection link weight of
the nodes as well as their location in communication environment for the next possible
reconstruction. The scheme uses elite selection strategy to decide the communication path of
devices and mapping operator to allow the D2D computation to share media content. The quality
of the link between nodes is represented by a value of mean opinion score. The scheme function
is used to compute the redundancy of explosion sparks as well as their amplitudes. Moreover,
fitness functions are designed to make a comparison with other swarm intelligence algorithms
based on 1 and 2-factor graph functions. Finally, extensive simulation is performed to investigate
the developed algorithm, and the results show the effectiveness of the proposed algorithm
regarding mean fitness value over fitness function evaluation. The proposed scheme can achieve
the global optimal outcome for most evaluations. |
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