Overlapping Channel Allocation Method for Wireless Communication Network Based on Discrete Particle Swarm Optimization Algorithm

To address the challenges of interference between network channels and low-frequency resource utilization efficiency, and to improve the overall performance and resource allocation fairness of the network, this study proposes a wireless communication network overlapping channel allocation strategy based on a discrete particle swarm optimization algorithm. First, the overlapping channel allocation problem in wireless networks is abstracted as a linear programming model with specific constraints, with the core objective of minimizing weighted interference between links. To solve this model, a discrete particle swarm optimization algorithm is introduced. In this algorithm, each particle’s position corresponds to a feasible overlapping channel allocation scheme, which is the potential solution to the optimization problem; The velocity of particles reflects the process of transitioning from the current channel configuration to the target channel configuration. Subsequently, based on the characteristics of discrete variables, corresponding operation rules and particle motion equations were defined. In addition, when particle swarm aggregation occurs, an optimal asymptotic mutation operator is introduced, which not only enhances the local search ability of the algorithm but also strengthens its global search ability. During algorithm execution, this mutation operator is applied based on a certain probability of mutation. The experimental results show that when using this strategy for overlapping channel allocation in wireless communication networks, even with an increase in the number of traffic flows, the average network throughput can remain stable, demonstrating a high network carrying capacity. Meanwhile, wireless networks exhibit a higher fairness factor, indicating a more balanced allocation of network resources among different users. In the process of optimal channel allocation, the packet loss rate reaches the lowest level, ensuring the reliability of data transmission.

Authors

Institutions

Publication Details

Journal
International Journal of Computational Intelligence Systems
Published
2024-12-18
DOI
https://doi.org/10.1007/s44196-024-00711-5
Citations
1
Primary Topic
Advanced MIMO Systems Optimization
Type
article
Field-Weighted Citation Impact
0.21

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Overlapping Channel Allocation Method for Wireless Communication Network Based on Discrete Particle Swarm Optimization Algorithm

Jingsong Zhang, Chao Liu, Lei Zhu, Yue Qin
1 citations
International Journal of Computational Intelligence Systems
Advanced MIMO Systems Optimization
0.21
article

Overlapping Channel Allocation Method for Wireless Communication Network Based on Discrete Particle Swarm Optimization Algorithm

Jingsong Zhang, Chao Liu, Lei Zhu, Yue Qin
article en
1 citations

Abstract

To address the challenges of interference between network channels and low-frequency resource utilization efficiency, and to improve the overall performance and resource allocation fairness of the network, this study proposes a wireless communication network overlapping channel allocation strategy based on a discrete particle swarm optimization algorithm. First, the overlapping channel allocation problem in wireless networks is abstracted as a linear programming model with specific constraints, with the core objective of minimizing weighted interference between links. To solve this model, a discrete particle swarm optimization algorithm is introduced. In this algorithm, each particle’s position corresponds to a feasible overlapping channel allocation scheme, which is the potential solution to the optimization problem; The velocity of particles reflects the process of transitioning from the current channel configuration to the target channel configuration. Subsequently, based on the characteristics of discrete variables, corresponding operation rules and particle motion equations were defined. In addition, when particle swarm aggregation occurs, an optimal asymptotic mutation operator is introduced, which not only enhances the local search ability of the algorithm but also strengthens its global search ability. During algorithm execution, this mutation operator is applied based on a certain probability of mutation. The experimental results show that when using this strategy for overlapping channel allocation in wireless communication networks, even with an increase in the number of traffic flows, the average network throughput can remain stable, demonstrating a high network carrying capacity. Meanwhile, wireless networks exhibit a higher fairness factor, indicating a more balanced allocation of network resources among different users. In the process of optimal channel allocation, the packet loss rate reaches the lowest level, ensuring the reliability of data transmission.

International Journal of Computational Intelligence SystemsVol. 17(1)
Qiqihar University (CN)
Department of Education, Heilongjiang Province
Openalex Percentile: Top 45%
Advanced MIMO Systems Optimization
0.21
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.