A Resource Allocation Algorithm for 5G Electric Power IoT Communication Systems Based on a Multi-Strategy Collaborative Improved Whale Optimization Algorithm

With the rapid development of 5G technology and the swift growth in the number of heterogeneous devices in the Electric Power Internet of Things (EP-IoT), the issue of communication resource allocation in 5G EP-IoT systems has become increasingly important. A reasonable allocation of transmission po...

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Vydáno v:2024 6th International Conference on Energy, Power and Grid (ICEPG) s. 1838 - 1842
Hlavní autoři: Li, Jianxue, Nie, Wuzhou, Chen, Baohao, Suo, Siliang, Liu, Miao, Han, Jianwei, Zhang, Chengliang
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 27.09.2024
Témata:
5G Communication
5G mobile communication
component
Contrastive learning
Convergence
Electric Power Internet of Things
Energy efficiency
Internet of Things
Multi-strategy Collaboration
Physical layer
Power systems
Resource Allocation
Resource management
Search problems
Whale Optimization Algorithm
Whale optimization algorithms
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Shrnutí:With the rapid development of 5G technology and the swift growth in the number of heterogeneous devices in the Electric Power Internet of Things (EP-IoT), the issue of communication resource allocation in 5G EP-IoT systems has become increasingly important. A reasonable allocation of transmission power for IoT devices can reduce communication interference and improve the overall energy efficiency of EP-IoT systems. This paper proposes a communication resource allocation algorithm based on a Multi-strategy Improved Whale Optimization Algorithm (MSWOA), which allocates the transmission power of IoT communication terminals under constraint conditions to enhance the total energy efficiency (EE) of the 5G EP-IoT communication system. This strategy introduces multiple improvements to the traditional Whale Optimization Algorithm, incorporating differential evolution, contrastive learning, and a penalty mechanism. It also adaptively adjusts the scaling factor and crossover probability in differential evolution. Addressing the traditional Whale Optimization Algorithm's shortcomings, such as low accuracy in solving complex combinatorial problems, slow convergence speed, and tendency to fall into local optima, our strategy enhances local search capabilities while ensuring global convergence, preventing local optima in the later stages of iterations. Matlab simulation comparisons with several intelligent optimization algorithms verify that our proposed algorithm achieves better results in the communication resource allocation for EP-IoT communication terminals.
DOI:10.1109/ICEPG63230.2024.10775368