Optimization of the Deployment of Relay Nodes in Cellular Networks

Significant and continuous contributions related to 4G/5G cellular networks are still accelerating the investigation of the approaches that can boost the cell characteristics following the new aspirations of the users. The challenge of achieving sufficient coverage at the cell edge; represents a con...

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Bibliographic Details
Published in:IEEE access 2020, Vol.8, p.136605-136616
Main Authors: Mokhtar, Rana M., Abdel-Atty, Heba M., Mahmoud, Korany R.
Format: Article
Language:English
Subjects:
A simplified adaptive bat algorithm based on frequency (FSABA)
Adaptive algorithms
Algorithms
cell edge
Cellular communication
Computer architecture
Construction costs
Cost analysis
Interference
Microprocessors
Minimum cost
Nodes
Optimization
Particle swarm optimization
particle swarm optimization and gravitational search algorithm (PSOGSA)
Planning
Relay
relay nodes
Relays
Search algorithms
Signal to noise ratio
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Summary:Significant and continuous contributions related to 4G/5G cellular networks are still accelerating the investigation of the approaches that can boost the cell characteristics following the new aspirations of the users. The challenge of achieving sufficient coverage at the cell edge; represents a constant concern for both users and operators; in addition to ensuring a reasonable cost, are the most important search fields and in our scope of interest. As relay nodes can provide a solution, a scenario for a plan of relay nodes deployment at the cell edge is proposed, taking into account the interference due to the relay nodes. Since optimization algorithms are effective in terms of planning, an advanced hybrid particle swarm optimization and gravitational search algorithm (PSOGSA) is applied to the proposed scenario to detect the optimum solution. The optimum solution represents the optimum plan that attains the best coverage with the minimum cost. We submit cost analysis depends on three trails of construction cost, power and channel cost efficiency. To highlight that the optimal plan has been revealed, another recently developed optimization algorithm, a simplified adaptive bat algorithm based on frequency (FSABA) and a classic particle swarm optimization (PSO) algorithm are also applied to the suggested scenario. The obtained results are compared with the related findings of the PSOGSA. From the simulations, it is found that the PSOGSA achieves better performance than the other two algorithms with fruitful and promising results, and the optimal plan featuring great coverage at the cell edge and cost-saving is attained.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2020.3011472